Academic literature on the topic 'Embedding Network'
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Journal articles on the topic "Embedding Network"
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Bandyopadhyay, Sambaran, N. Lokesh, and M. N. Murty. "Outlier Aware Network Embedding for Attributed Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 12–19. http://dx.doi.org/10.1609/aaai.v33i01.330112.
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Attributed network embedding has received much interest from the research community as most of the networks come with some content in each node, which is also known as node attributes. Existing attributed network approaches work well when the network is consistent in structure and attributes, and nodes behave as expected. But real world networks often have anomalous nodes. Typically these outliers, being relatively unexplainable, affect the embeddings of other nodes in the network. Thus all the downstream network mining tasks fail miserably in the presence of such outliers. Hence an integrated approach to detect anomalies and reduce their overall effect on the network embedding is required.Towards this end, we propose an unsupervised outlier aware network embedding algorithm (ONE) for attributed networks, which minimizes the effect of the outlier nodes, and hence generates robust network embeddings. We align and jointly optimize the loss functions coming from structure and attributes of the network. To the best of our knowledge, this is the first generic network embedding approach which incorporates the effect of outliers for an attributed network without any supervision. We experimented on publicly available real networks and manually planted different types of outliers to check the performance of the proposed algorithm. Results demonstrate the superiority of our approach to detect the network outliers compared to the state-of-the-art approaches. We also consider different downstream machine learning applications on networks to show the efficiency of ONE as a generic network embedding technique. The source code is made available at https://github.com/sambaranban/ONE.
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Armandpour, Mohammadreza, Patrick Ding, Jianhua Huang, and Xia Hu. "Robust Negative Sampling for Network Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3191–98. http://dx.doi.org/10.1609/aaai.v33i01.33013191.
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Many recent network embedding algorithms use negative sampling (NS) to approximate a variant of the computationally expensive Skip-Gram neural network architecture (SGA) objective. In this paper, we provide theoretical arguments that reveal how NS can fail to properly estimate the SGA objective, and why it is not a suitable candidate for the network embedding problem as a distinct objective. We show NS can learn undesirable embeddings, as the result of the “Popular Neighbor Problem.” We use the theory to develop a new method “R-NS” that alleviates the problems of NS by using a more intelligent negative sampling scheme and careful penalization of the embeddings. R-NS is scalable to large-scale networks, and we empirically demonstrate the superiority of R-NS over NS for multi-label classification on a variety of real-world networks including social networks and language networks.
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He, Tao, Lianli Gao, Jingkuan Song, Xin Wang, Kejie Huang, and Yuanfang Li. "SNEQ: Semi-Supervised Attributed Network Embedding with Attention-Based Quantisation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4091–98. http://dx.doi.org/10.1609/aaai.v34i04.5832.
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Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many network analytics tasks. Moreover, the trained embeddings often require a significant amount of space to store, making storage and processing a challenge, especially as large-scale networks become more prevalent. In this paper, we present a novel semi-supervised network embedding and compression method, SNEQ, that is competitive with state-of-art embedding methods while being far more space- and time-efficient. SNEQ incorporates a novel quantisation method based on a self-attention layer that is trained in an end-to-end fashion, which is able to dramatically compress the size of the trained embeddings, thus reduces storage footprint and accelerates retrieval speed. Our evaluation on four real-world networks of diverse characteristics shows that SNEQ outperforms a number of state-of-the-art embedding methods in link prediction, node classification and node recommendation. Moreover, the quantised embedding shows a great advantage in terms of storage and time compared with continuous embeddings as well as hashing methods.
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Li, Yu, Yuan Tian, Jiawei Zhang, and Yi Chang. "Learning Signed Network Embedding via Graph Attention." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4772–79. http://dx.doi.org/10.1609/aaai.v34i04.5911.
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Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks. Recently graph convolutional networks (GCNs) have revolutionized the field of network embedding, and led to state-of-the-art performance in network analysis tasks such as link prediction and node classification. Nevertheless, most of the existing GCN-based network embedding methods are proposed for unsigned networks. However, in the real world, some of the networks are signed, where the links are annotated with different polarities, e.g., positive vs. negative. Since negative links may have different properties from the positive ones and can also significantly affect the quality of network embedding. Thus in this paper, we propose a novel network embedding framework SNEA to learn Signed Network Embedding via graph Attention. In particular, we propose a masked self-attentional layer, which leverages self-attention mechanism to estimate the importance coefficient for pair of nodes connected by different type of links during the embedding aggregation process. Then SNEA utilizes the masked self-attentional layers to aggregate more important information from neighboring nodes to generate the node embeddings based on balance theory. Experimental results demonstrate the effectiveness of the proposed framework through signed link prediction task on several real-world signed network datasets.
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Wang, Yueyang, Ziheng Duan, Binbing Liao, Fei Wu, and Yueting Zhuang. "Heterogeneous Attributed Network Embedding with Graph Convolutional Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10061–62. http://dx.doi.org/10.1609/aaai.v33i01.330110061.
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Network embedding which assigns nodes in networks to lowdimensional representations has received increasing attention in recent years. However, most existing approaches, especially the spectral-based methods, only consider the attributes in homogeneous networks. They are weak for heterogeneous attributed networks that involve different node types as well as rich node attributes and are common in real-world scenarios. In this paper, we propose HANE, a novel network embedding method based on Graph Convolutional Networks, that leverages both the heterogeneity and the node attributes to generate high-quality embeddings. The experiments on the real-world dataset show the effectiveness of our method.
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Zhong, Jianan, Hongjun Qiu, and Benyun Shi. "Dynamics-Preserving Graph Embedding for Community Mining and Network Immunization." Information 11, no. 5 (May 2, 2020): 250. http://dx.doi.org/10.3390/info11050250.
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In recent years, the graph embedding approach has drawn a lot of attention in the field of network representation and analytics, the purpose of which is to automatically encode network elements into a low-dimensional vector space by preserving certain structural properties. On this basis, downstream machine learning methods can be implemented to solve static network analytic tasks, for example, node clustering based on community-preserving embeddings. However, by focusing only on structural properties, it would be difficult to characterize and manipulate various dynamics operating on the network. In the field of complex networks, epidemic spreading is one of the most typical dynamics in networks, while network immunization is one of the effective methods to suppress the epidemics. Accordingly, in this paper, we present a dynamics-preserving graph embedding method (EpiEm) to preserve the property of epidemic dynamics on networks, i.e., the infectiousness and vulnerability of network nodes. Specifically, we first generate a set of propagation sequences through simulating the Susceptible-Infectious process on a network. Then, we learn node embeddings from an influence matrix using a singular value decomposition method. Finally, we show that the node embeddings can be used to solve epidemics-related community mining and network immunization problems. The experimental results in real-world networks show that the proposed embedding method outperforms several benchmark methods with respect to both community mining and network immunization. The proposed method offers new insights into the exploration of other collective dynamics in complex networks using the graph embedding approach, such as opinion formation in social networks.
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Zhuo, Wei, Qianyi Zhan, Yuan Liu, Zhenping Xie, and Jing Lu. "Context Attention Heterogeneous Network Embedding." Computational Intelligence and Neuroscience 2019 (August 21, 2019): 1–15. http://dx.doi.org/10.1155/2019/8106073.
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Network embedding (NE), which maps nodes into a low-dimensional latent Euclidean space to represent effective features of each node in the network, has obtained considerable attention in recent years. Many popular NE methods, such as DeepWalk, Node2vec, and LINE, are capable of handling homogeneous networks. However, nodes are always fully accompanied by heterogeneous information (e.g., text descriptions, node properties, and hashtags) in the real-world network, which remains a great challenge to jointly project the topological structure and different types of information into the fixed-dimensional embedding space due to heterogeneity. Besides, in the unweighted network, how to quantify the strength of edges (tightness of connections between nodes) accurately is also a difficulty faced by existing methods. To bridge the gap, in this paper, we propose CAHNE (context attention heterogeneous network embedding), a novel network embedding method, to accurately determine the learning result. Specifically, we propose the concept of node importance to measure the strength of edges, which can better preserve the context relations of a node in unweighted networks. Moreover, text information is a widely ubiquitous feature in real-world networks, e.g., online social networks and citation networks. On account of the sophisticated interactions between the network structure and text features of nodes, CAHNE learns context embeddings for nodes by introducing the context node sequence, and the attention mechanism is also integrated into our model to better reflect the impact of context nodes on the current node. To corroborate the efficacy of CAHNE, we apply our method and various baseline methods on several real-world datasets. The experimental results show that CAHNE achieves higher quality compared to a number of state-of-the-art network embedding methods on the tasks of network reconstruction, link prediction, node classification, and visualization.
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Lu, Ruili, Pengfei Jiao, Yinghui Wang, Huaming Wu, and Xue Chen. "Layer Information Similarity Concerned Network Embedding." Complexity 2021 (August 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/2260488.
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Great achievements have been made in network embedding based on single-layer networks. However, there are a variety of scenarios and systems that can be presented as multiplex networks, which can reveal more interesting patterns hidden in the data compared to single-layer networks. In the field of network embedding, in order to project the multiplex network into the latent space, it is necessary to consider richer structural information among network layers. However, current methods for multiplex network embedding mostly focus on the similarity of nodes in each layer of the network, while ignoring the similarity between different layers. In this paper, for multiplex network embedding, we propose a Layer Information Similarity Concerned Network Embedding (LISCNE) model considering the similarities between layers. Firstly, we introduce the common vector for each node shared by all layers and layer vectors for each layer where common vectors obtain the overall structure of the multiplex network and layer vectors learn semantics for each layer. We get the node embeddings in each layer by concatenating the common vectors and layer vectors with the consideration that the node embedding is related not only to the surrounding neighbors but also to the overall semantics. Furthermore, we define an index to formalize the similarity between different layers and the cross-network association. Constrained by layer similarity, the layer vectors with greater similarity are closer to each other and the aligned node embedding in these layers is also closer. To evaluate our proposed model, we conduct node classification and link prediction tasks to verify the effectiveness of our model, and the results show that LISCNE can achieve better or comparable performance compared to existing baseline methods.
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Makarov, Ilya, Mikhail Makarov, and Dmitrii Kiselev. "Fusion of text and graph information for machine learning problems on networks." PeerJ Computer Science 7 (May 11, 2021): e526. http://dx.doi.org/10.7717/peerj-cs.526.
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Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, link prediction and network visualization. Naturally, some networks have text information associated with them. For instance, in a citation network, each node is a scientific paper associated with its abstract or title; in a social network, all users may be viewed as nodes of a network and posts of each user as textual attributes. In this work, we explore how combining existing methods of text and network embeddings can increase accuracy for downstream tasks and propose modifications to popular architectures to better capture textual information in network embedding and fusion frameworks.
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Ji, Fujiao, Zhongying Zhao, Hui Zhou, Heng Chi, and Chao Li. "A comparative study on heterogeneous information network embeddings." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3463–73. http://dx.doi.org/10.3233/jifs-191796.
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Heterogeneous information networks are widely used to represent real world applications in forms of social networks, word co-occurrence networks, and communication networks, etc. However, It is difficult for traditional machine learning methods to analyze these networks effectively. Heterogeneous information network embedding aims to convert the network into low dimensional vectors, which facilitates the following tasks. Thus it is receiving tremendous attention from the research community due to its effectiveness and efficiency. Although numerous methods have been present and applied successfully, there are few works to make a comparative study on heterogeneous information network embedding, which is very important for developers and researchers to select an appropriate method. To address the above problem, we make a comparative study on the heterogeneous information network embeddings. Specifically, we first give the problem definition of heterogeneous information network embedding. Then the heterogeneous information networks are classified into four categories from the perspective of network type. The state-of-the-art methods for each category are also compared and reviewed. Finally, we make a conclusion and suggest some potential future research directions.
Dissertations / Theses on the topic "Embedding Network"
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Bays, Leonardo Richter. "Virtual network embedding in software-defined networks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/178658.
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Pesquisas acadêmicas em virtualização de redes vêm sendo realizadas durante diversos anos, nos quais diferentes abordagens de alocação de redes virtuais foram propostas. Tais abordagens, no entanto, negligenciam requisitos operacionais importantes impostos por plataformas de virtualização. No caso de virtualização baseada em SDN/OpenFlow, um exemplo fundamental de tais requisitos operacionais é a disponibilidade de espaço de memória para armazenar regras em dispositivos OpenFlow. Diante dessas circunstâncias, argumentamos que a alocação de redes virtuais deve ser realizada com certo grau de conhecimento sobre infraestruturas físicas; caso contrário, após instanciadas, tais redes podem sofrer instabilidade ou desempenho insatisfatório. Considerando redes físicas baseadas em SDN/OpenFlow como um cenário importante de virtualização, propõe-se um arcabouço baseado na coordenação entre a alocação de redes virtuais e redes OpenFlow para realizar a instanciação de redes virtuais de forma adequada. A abordagem proposta desdobra-se nas seguintes contribuições principais: uma abstração de infraestruturas virtuais que permite que um requisitante represente os detalhes de seus requerimentos de rede de maneira aprofundada; um compilador ciente de privacidade que é capaz de pré-processar requisições com tal grau de detalhamento, ofuscando informações sensíveis e derivando requisitos operacionais computáveis; um modelo para a alocação de redes virtuais que visa a maximizar a viabilidade no nível físico. Resultados obtidos por meio de uma avaliação da nossa abordagem evidenciam que considerar tais requisitos operacionais, bem como computá-los de forma precisa, é imprescindível para garantir a “saúde” das redes virtuais hospedadas na plataforma de virtualização considerada.Research on network virtualization has been active for a number of years, during which a number of virtual network embedding (VNE) approaches have been proposed. These approaches, however, neglect important operational requirements imposed by the underlying virtualization platforms. In the case of SDN/OpenFlow-based virtualization, a crucial example of an operational requirement is the availability of enough memory space for storing flow rules in OpenFlow devices. Due to these circumstances, we advocate that VNE must be performed with some degree of knowledge of the underlying physical networks, otherwise the deployment may suffer from unpredictable or even unsatisfactory performance. Considering SDN/OpenFlow-based physical networks as an important virtualization scenario, we propose a framework based on VNE and OpenFlow coordination for proper deployment of virtual networks (VNs). The proposed approach unfolds in the following main contributions a virtual infrastructure abstraction that allows a service provider to represent the details of his/her VN requirements in a comprehensive manner; a privacy-aware compiler that is able to preprocess this detailed VN request in order to obfuscate sensitive information and derive computable operational requirements; a model for embedding requested VNs that aims at maximizing their feasibility at the physical level. Results obtained through an evaluation of our framework demonstrate that taking such operational requirements into account, as well as accurately assessing them, is of paramount importance to ensure the “health” of VNs hosted on top of the virtualization platform.
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Ghazar, Tay. "Efficient Virtual Network Embedding onto A Hierarchical-Based Substrate Network Framework." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23932.
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The current Internet architecture presents a barrier to accommodate the vigorous arising
demand for deploying new network services and applications. The next-generation architecture views the network virtualization as the gateway to overcome this limitation. Network virtualization promises to run efficiently and securely multiple dedicated virtual networks (VNs) over a shared physical infrastructure. Each VN is tailored to host a unique application based on the user’s preferences.
This thesis addresses the problem of the efficient embedding of multiple VNs onto a
shared substrate network (SN). The contribution of this thesis are twofold: First, a novel hierarchical SN management framework is proposed that efficiently selects the optimum VN mapping scheme for the requested VN from more than one proposed VN mapping candidates obtained in parallel. In order to accommodate the arbitrary architecture
of the VNs, the proposed scheme divides the VN request into smaller subgraphs, and
individually maps them on the SN using a variation of the exact subgraph matching
techniques.
Second, the physical resources pricing policy is introduced that is based on time-ofuse,
that reflects the effect of resource congestion introduced by VN users. The preferences of the VN users are first represented through corresponding demand-utility functions that quantify the sensitivity of the applications hosted by the VNs to resource consumption and time-of-use. A novel model of time-varying VNs is presented, where users are allowed to up- or down-scale the requested resources to continuously maximize their utility while minimizing the VNs embedding cost.
In contrast to existing solutions, the proposed work does not impose any limitations
on the size or topology of the VN requests. Instead, the search is customized according
to the VN size and the associated utility. Extensive simulations are then conducted to
demonstrate the improvement achieved through the proposed work in terms of network
utilization, the ratio of accepted VN requests and the SP profits.
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Chochlidakis, Georgios. "Mobility-aware virtual network embedding techniques for next-generation mobile networks." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/mobilityaware-virtual-network-embedding-techniques-for-nextgeneration-mobile-networks(174e714f-2a4a-447a-bcd5-d526170377fd).html.
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Network virtualisation has become one of the most prominent solutions for sus-tainability towards the dramatic increase of data demand in next-generation mobile networks. In addition, apart from increasing the overall infrastructure utilisation, it also greatly improves the manageability, the scalability and the robustness of the network. In order to allow multiple virtual networks to coexist in the same substrate network, the need for efficient network sharing techniques is imperative. The main purpose of this work is to provide a holistic optimization framework for vir-tual network embedding solutions, where the actual user mobility effect is explicitly considered. First, the main focus is given on the study of the mobility effect and the impact of the mobility management techniques on the end-to-end communication of the mobile user. A hybrid-distributed mobility management scheme is proposed and compared against the latest mobility management schemes. Then, an optimisation framework for efficient mobility-aware virtual network embedding is proposed and evaluated by comparison with other works from the literature. Moving deeper in the area of virtual network embedding, the focus is given on minimizing the end-to-end delay and providing service differentiation, allowing in this way delay sensitive services to use the formed virtual networks with the minimum possible delay, as op-posed to other more elastic services that use the same substrate network. The last part of this work is the study and the analysis of the stochastic nature of the virtual network embedding parameters and the proposal of an optimisation framework for adjustable-robustness virtual network embedding. Driven by the benefits from virtualising the network and its functions, research as well as industry are expected to exploit in a greater degree than today the merits of this concept. The co-existence of multiple tenants not only will greatly change the network industry from a business perspective, but also will emphasise the need for more efficient and flexible network sharing techniques. This work belongs to the initial efforts to embrace and adopt the virtualisation concept in the next-generation wireless networks.
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Dietrich, David [Verfasser]. "Multi-provider network service embedding / David Dietrich." Hannover : Technische Informationsbibliothek (TIB), 2016. http://d-nb.info/109909643X/34.
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Wåhlin, Lova. "Towards Machine Learning Enabled Automatic Design of IT-Network Architectures." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249213.
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There are many machine learning techniques that cannot be performed on graph-data. Techniques such as graph embedding, i.e mapping a graph to a vector, can open up a variety of machine learning solutions. This thesis addresses to what extent static graph embedding techniques can capture important characteristics of an IT-architecture graph, with the purpose of embedding the graphs in a common euclidean vector space that can serve as the state space in a reinforcement learning setup. The metric used for evaluating the performance of the embedding is the security of the graph, i.e the time it would take for an unauthorized attacker to penetrate the IT-architecture graph. The algorithms evaluated in this work are the node embedding methods node2vec and gat2vec and the graph embedding method graph2vec. The predictive results of the embeddings are compared with two baseline methods. The results of each of the algorithms mostly display a significant predictive performance improvement compared to the baseline, where the F1 score in some cases is doubled. Indeed, the results indicate that static graph embedding methods can in fact capture some information about the security of an IT-architecture. However, no conclusion can be made whether a static graph embedding is actually the best contender for posing as the state space in a reinforcement learning framework. To make a certain conclusion other options has to be researched, such as dynamic graph embedding methods.Det är många maskininlärningstekniker som inte kan appliceras på data i form av en graf. Tekniker som graph embedding, med andra ord att mappa en graf till ett vektorrum, can öppna upp för en större variation av maskininlärningslösningar. Det här examensarbetet evaluerar hur väl statiska graph embeddings kan fånga viktiga säkerhetsegenskaper hos en IT-arkitektur som är modellerad som en graf, med syftet att användas i en reinforcement learning algoritm. Dom egenskaper i grafen som används för att validera embedding metoderna är hur lång tid det skulle ta för en obehörig attackerare att penetrera IT-arkitekturen. Algorithmerna som implementeras är node embedding metoderna node2vec och gat2vec, samt graph embedding metoden graph2vec. Dom prediktiva resultaten är jämförda med två basmetoder. Resultaten av alla tre metoderna visar tydliga förbättringar relativt basmetoderna, där F1 värden i några fall uppvisar en fördubbling. Det går alltså att dra slutsatsen att att alla tre metoder kan fånga upp säkerhetsegenskaper i en IT-arkitektur. Dock går det inte att säga att statiska graph embeddings är den bästa lösningen till att representera en graf i en reinforcement learning algoritm, det finns andra komplikationer med statiska metoder, till exempel att embeddings från dessa metoder inte kan generaliseras till data som inte var använd till träning. För att kunna dra en absolut slutsats krävs mer undersökning, till exempel av dynamiska graph embedding metoder.
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Moura, Leonardo Fernando dos Santos. "Branch & price for the virtual network embedding problem." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/115213.
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Virtualização permite o compartilhamento de uma rede física entre uma ou mais redes virtuais. O Problema de Mapeamento de Redes Virtuais é um dos principais desafios na virtualização de redes. Esse problema consiste em mapear uma rede virtual em uma rede física, respeitando restrições de capacidade. O presente trabalho mostra que encontrar uma solução factível para esse problema é NP-Difícil. Mesmo assim, muitas instâncias podem ser pode ser resolvidas na prática através da exploração de sua estrutura. Nós apresentamos um algoritmo de Branch & Price aplicado a instâncias de diferentes topologias e tamanhos. Os experimentos realizados sugerem que o algoritmo proposto é superior ao modelo de programação linear resolvido com CPLEX.Virtualization allows one or more virtual networks to share physical infrastructures. The Virtual Network Embedding problem (VNEP) is one of the main challenges in the virtualization of physical networks. This problem consists in mapping a virtual network into a physical network while respecting capacity constraints. This work shows that finding a feasible solution for this problem is NP-Hard. However, many instances can be solved up to optimality in practice by exploiting the problem structure. We present a Branch & Price algorithm applied to instances of different topologies and sizes. The experimental results suggest that the proposed algorithm is superior to the Integer Linear Programming model solved by CPLEX.
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DeFreeuw, Jonathan Daniel. "Embedding Network Information for Machine Learning-based Intrusion Detection." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/99342.
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As computer networks grow and demonstrate more complicated and intricate behaviors, traditional intrusion detections systems have fallen behind in their ability to protect network resources. Machine learning has stepped to the forefront of intrusion detection research due to its potential to predict future behaviors. However, training these systems requires network data such as NetFlow that contains information regarding relationships between hosts, but requires human understanding to extract. Additionally, standard methods of encoding this categorical data struggles to capture similarities between points. To counteract this, we evaluate a method of embedding IP addresses and transport-layer ports into a continuous space, called IP2Vec. We demonstrate this embedding on two separate datasets, CTU'13 and UGR'16, and combine the UGR'16 embedding with several machine learning methods. We compare the models with and without the embedding to evaluate the benefits of including network behavior into an intrusion detection system. We show that the addition of embeddings improve the F1-scores for all models in the multiclassification problem given in the UGR'16 data.MS
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Boutigny, François. "Multidomain virtual network embedding under security-oriented requirements applied to 5G network slices." Electronic Thesis or Diss., Institut polytechnique de Paris, 2019. http://www.theses.fr/2019IPPAS002.
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La 5G apporte un nouveau concept, le network slicing (découpage du réseau en tranches). Cette technologie permet de généraliser le modèle économique des MVNO à des entreprises qui ont besoin d’opérer un réseau, sans que cela ne soit leur cœur de métier. Chaque tranche (slice) est un réseau virtuel de bout en bout, dédié et personnalisé, au-dessus d’une infrastructure partagée ; cette infrastructure elle-même être fournie par l’interconnexion de fournisseurs d’infrastructure: nous parlons dans ce cas d’infrastructure multi-domaine.L’objectif de cette thèse est d’étudier l’allocation de ces tranches dans une telle infrastructure multi-domaine. Le problème est connu comme l’incorporation de réseau virtuel (Virtual Network Embedding (VNE)). Il s’agit d’un problème NP-difficile. Pratiquement, le problème VNE recherche à quelles ressources physiques associer un ensemble d’éléments virtuels. Les ressources physiques décrivent ce qu’elles peuvent offrir. Les éléments virtuels décrivent ce qu’ils exigent. La mise en relation de ces offres et de ces demandes est la clé pour résoudre le problème VNE.En l’espèce, nous nous sommes intéressés à la modélisation et à la mise en place d’exigences de sécurité. En effet, nous nous attendons à ce que les acteurs à l’initiative des tranches appartiennent à des sphères éloignées des télécommunications. Or de la même façon qu’ils connaissent peu ce domaine, nous pouvons nous attendre à ce que leurs besoins, notamment de sécurité, s’expriment d’une façon sans précédent dans le contexte des tranches.Cette thèse présente un algorithme capable de traiter des exigences variées selon un modèle extensible fondé sur un solveur de satisfiabilité appliqué à des théories décidables (Satisfiability Modulo Theories (SMT)). Comparée à la programmation linéaire (Integer Linear Programming (ILP)), plus commune dans le domaine des VNE, cette formulation permet d’exprimer les contraintes à satisfaire de façon plus transparente, et d’auditer l’ensemble des contraintes.De plus, ayant conscience que les fournisseurs d’infrastructure sont réticents à exposer les informations relatives à leurs ressources physiques, nous proposons une résolution limitant cette exposition. Ce système a été implémenté et testé avec succès au cours du doctorat5G brings a new concept called network slicing. This technology makes it possible to generalize the business model of MVNOs to companies in need to operate a network, without it being their core business. Each slice is an end-to-end, dedicated and customized virtual network, over a shared infrastructure; this infrastructure itself is provided by the interconnection of infrastructure providers: we refer to this case as a multi-domain infrastructure.The objective of this thesis is to study the allocation of these slices in such a multi-domain infrastructure. The problem is known as Virtual Network Embedding (VNE). It is an NP-hard problem. Practically, the VNE problem looks for which physical resources to associate a set of virtual elements. Physical resources describe what they can offer. Virtual elements describe what they require. Linking these offers and requests is the key to solve the VNE problem.In this thesis, we focused on modeling and implementing security requirements. Indeed, we expect that the initiators of the slices belong to areas distant from telecommunications. In the same way that they know little about this field, we can expect that their needs, especially in security, are novel in the slice context.This thesis presents an algorithm able to handling various requirements, according to an extensible model based on a Satisfiability Modulo Theories (SMT) solver. Compared to Integer Linear Programming (ILP), more common in the VNE field, this formulation allows to express the satisfaction constraints in a more transparent way, and allows to audit all the constraints.Moreover, being aware that infrastructure providers are reluctant to disclose information about their physical resources, we propose a resolution limiting this disclosure. This system has been successfully implemented and tested during the Ph.D
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Törnegren, Viktor. "Applying Similarity Condition Embedding Network to an industrial fashion dataset." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283351.
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To create a fashionable outfit one needs to take into account several different similarity conditions between clothing items, such as season, colour, what kind of context the outfit is supposed worn in etc. This is of course a hard task for a human to do but an even harder task for a computer to solve. To make an algorithm take into account different similarity conditions from images Veit, Belongie, and Karaletsos [1] and Vasileva et al. [2] introduced two different models that utilizes predefined similarity conditions. Tan et al. [3] took inspiration from [1, 2] and created an algorithm that learns the similairty conditions in an unsupervised way and they tested their model on a dataset containing outfits created by regular people. In this thesiswe present a newfashion datset that has been created with the help of fashion experts from Hennes & Mauritz AB. We provide evidence that our reimplementation of the Similarity Condition Embedding Network (SCE-net) from [3] can pick out garments that complete an outfit as well as evaluate if the clothing items in an outfit are compatible or not on data that contains outifts for both men and women. We also show that the SCE-net can be trained on outfits for one gender and then predict on a dataset containing clothes for another gender. We further provide results that our network generalize well to unseen categories by training it on outfits without accessories and then test the network on outfits with accessories. In addition we also introduce a dataset that contains baskets of items for customers from Hennes & Mauritz online shop as well as their boutiques. On this data we provide evidence that our reimplementation of the SCE-net can predict the next item in a customers shopping basket.För att skapa en mode riktigt klädes outfit behöver man ta hänsyn till flertalet olika faktoer, som t.ex. säsong, färg samt i vilken typ av sammanghang klädesutstyrseln är tänkt att bäras inom etc. Detta är naturligtvis en svår uppgift för en människa att göra men det är ett ännu svårare problem för en dator att lösa. För att lära en algorithm att ta hänsyn till olika likhetsvillkor introducerade Veit, Belongie och Karaletsos [1] och Vasileva m. fl. [2] två olika modeller som använder sig av förbestämda likhetsvillkor. Vidare blev Tan m. fl. [3] inspirerad av [1, 2] och skapade en algorithm som kan lära sig likhetsvillkor via oövervakad inlärning, denna modell testade dom på ett dataset som innehåller klädesutstyrsal som är skapade av vanliga människor. I detta examensarbete presenterar vi ett nytt modedataset som har skapats med hjälp av modeexperter från Henns & Mauritz AB. Vidare bevisar vi att våran implementering av Similarity Condition Embedding Network (SCE-net) från [3] kan välja ut ett klädesplagg som tillsammans med tidigare utvalda plagg skapar en outfit samt utvärdera om klädesplaggen i en outfit är kompatibla eller inte. Vi utför dessa tester på data som innehåller kläder för både män och kvinnor. Vi visar också att SCE-net tränas med data som innehåller kläder för ett kön för att senare prediktera kläder i outfits för ett annat kön. Vidare tillhandahåller vi resultat som påvisar att SCE-net generaliserar väl till osedda kategorier genom att träna modellen på outfits som inte innehåller accessoarer och sedan testar vi modellen på klädes utstyrslar som innehåller accessoarer. Utöver detta introducerar vi även ett dataset som innehåller artiklar från kunders kundvagnar från Hennes & Mauritz onlinebutiker samt deras fysiska butiker. Med hjälp av denna data visar vi att våran implementering av SCE-net kan prediktera nästa vara i en kunds varukorg.
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Okuno, Akifumi. "Studies on Neural Network-Based Graph Embedding and Its Extensions." Kyoto University, 2020. http://hdl.handle.net/2433/259075.
Full textBooks on the topic "Embedding Network"
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Wijers, Jean Paul, ed. Managing Authentic Relationships. NL Amsterdam: Amsterdam University Press, 2019. http://dx.doi.org/10.5117/9789462988613.
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In an increasingly connected world, Strategic Relationship Management is a vital capability for successful organizations. The book Managing Authentic Relationships; Facing New Challenges in a Changing Context focuses on building and managing a strong network and reciprocal relationships for the entire organization by implementing a professional relationship management approach at strategic, tactical and operational level. Professional relationship management makes valuable and measurable contributions to the strategic goals of an organization by: Expanding the organization's strategy to a Relationship Management Strategy; Efficiently managing relationships and correctly mapping stakeholders; Embedding clear responsibility for relationship management throughout the organization; Measuring results and calculating the Return-on-Relationship; Developing strong networking skills and networkers who are able to act as eyes and ears for the organization; Organizing effective networking activities with measurable results. This book also offers a holistic view. Managing authentic relationships requires a shared understanding of what relationships are. It is impossible to develop successful relationship management without authentic relationships based on trust and reciprocity.
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Campbell, Roy Harold. The embedded operating system project: Mid-year report, May 1985. Urbana, Ill: Software Systems Research Group, University of Illinois at Urbana-Champaign, Dept. of Computer Science, 1985.
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Unger, Herwig, and Wolfgang A. Halang, eds. Autonomous Systems 2016. VDI Verlag, 2016. http://dx.doi.org/10.51202/9783186848109.
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To meet the expectations raised by the terms Industrie 4.0, Industrial Internet and Internet of Things, real innovations are necessary, which can be brought about by information processing systems working autonomously. Owing to their growing complexity and their embedding in complex environments, their design becomes increasingly critical. Thus, the topics addressed in this book span from verification and validation of safety-related control software and suitable hardware designed for verifiability to be deployed in embedded systems over approaches to suppress electromagnetic interferences to strategies for network routing based on centrality measures and continuous re-authentication in peer-to-peer networks. Methods of neural and evolutionary computing are employed to aid diagnosing retinopathy of prematurity, to invert matrices and to solve non-deterministic polynomial-time hard problems. In natural language processing, interface problems between humans and machines are solved with g...
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Kwon, Younggeun. Embeddings in parallel systems. 1993.
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Rowley, Robert A. Fault-tolerant ring embedding in De Bruijn networks. 1993.
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Rowley, Robert A. Fault-tolerant ring embedding in De Bruijn networks. 1993.
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Kubek, Maria M., and Zhong Li, eds. Autonomous Systems 2018. VDI Verlag, 2018. http://dx.doi.org/10.51202/9783186862105.
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To meet the expectations raised by the terms Industry 4.0, Industrial Internet and Internet of Things, real innovations are necessary, which can be brought about by information processing systems working autonomously. Owing to their growing complexity and their embedding in ever-changing environments, their design becomes increasingly critical. Thus, the many topics addressed in this book range from data integration on hardware level to methods for security and safety of data and to stochastic methods, data interferences as well as machine learning and search in decentralised systems. Their validity is proven by extensive simulation results. Also, applications for methods from deep learning and neurocomputing are presented. The sustainable management of energy systems using intelligent methods of self-organisation and learning is dealt with in the second major part of this book. As in these particular settings, the assessment of network vulnerabilities plays a crucial role, respective ...
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Schäfer, Anne, and Rüdiger Schmitt-Beck. A Vicious Circle of Demobilization? Context Effects on Turnout at the 2009 and 2013 German Federal Elections. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198792130.003.0006.
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This chapter explores the contextual effects of constituency-level turnout on individual turnout intentions at the 2009 and 2013 German federal elections. It assesses whether these effects are mediated by citizens’ embedding into networks of political discussants, differentiating between influences originating from discussants inside and those outside of voters’ households. Although we can establish contextual effects, no empirical support is established for their mediation by voters’ discussion networks. Still, we detect relationships between shares of constituency turnout and citizens’ propensity to talk about political matters at all and to do so with other voters. It turns out that political discussants are a very powerful source of environmental influence on electoral behavior. Discussants cohabitating in voters’ households are especially influential. However, embedding into discussion networks is not always a boon; talking to non-voters also has substantial demobilizing effects.
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United States. National Aeronautics and Space Administration., ed. The embedded operating system project: Mid-year report, May 1985. Urbana, Ill: Software Systems Research Group, University of Illinois at Urbana-Champaign, Dept. of Computer Science, 1985.
Find full textBook chapters on the topic "Embedding Network"
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Zhang, Jiawei, and Philip S. Yu. "Network Embedding." In Broad Learning Through Fusions, 385–413. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12528-8_11.
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Chen, Weizheng, Xianling Mao, Xiangyu Li, Yan Zhang, and Xiaoming Li. "PNE: Label Embedding Enhanced Network Embedding." In Advances in Knowledge Discovery and Data Mining, 547–60. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_43.
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Makarov, Ilya, Olga Gerasimova, Pavel Sulimov, and Leonid E. Zhukov. "Co-authorship Network Embedding and Recommending Collaborators via Network Embedding." In Lecture Notes in Computer Science, 32–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-11027-7_4.
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Rahman, Muntasir Raihan, Issam Aib, and Raouf Boutaba. "Survivable Virtual Network Embedding." In NETWORKING 2010, 40–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12963-6_4.
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Huang, Xiao, Jundong Li, and Xia Hu. "Accelerated Attributed Network Embedding." In Proceedings of the 2017 SIAM International Conference on Data Mining, 633–41. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2017. http://dx.doi.org/10.1137/1.9781611974973.71.
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Li, Jundong, Chen Chen, Hanghang Tong, and Huan Liu. "Multi-Layered Network Embedding." In Proceedings of the 2018 SIAM International Conference on Data Mining, 684–92. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2018. http://dx.doi.org/10.1137/1.9781611975321.77.
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Yuan, Shuhan, Xintao Wu, and Yang Xiang. "SNE: Signed Network Embedding." In Advances in Knowledge Discovery and Data Mining, 183–95. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57529-2_15.
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Li, Chaozhuo, Zhoujun Li, Senzhang Wang, Yang Yang, Xiaoming Zhang, and Jianshe Zhou. "Semi-Supervised Network Embedding." In Database Systems for Advanced Applications, 131–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55753-3_9.
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Kong, Chao, Baoxiang Chen, Shaoying Li, Qi Zhou, Dongfang Wang, and Liping Zhang. "D2NE: Deep Dynamic Network Embedding." In Advanced Data Mining and Applications, 168–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65390-3_14.
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Zhang, Xia, Weizheng Chen, and Hongfei Yan. "TLINE: Scalable Transductive Network Embedding." In Information Retrieval Technology, 98–110. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48051-0_8.
Full textConference papers on the topic "Embedding Network"
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Zhang, Yizhou, Guojie Song, Lun Du, Shuwen Yang, and Yilun Jin. "DANE: Domain Adaptive Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/606.
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Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. In this paper, we propose a novel Domain Adaptive Network Embedding framework, which applies graph convolutional network to learn transferable embeddings. In DANE, nodes from multiple networks are encoded to vectors via a shared set of learnable parameters so that the vectors share an aligned embedding space. The distribution of embeddings on different networks are further aligned by adversarial learning regularization. In addition, DANE's advantage in learning transferable network embedding can be guaranteed theoretically. Extensive experiments reflect that the proposed framework outperforms other state-of-the-art network embedding baselines in cross-network domain adaptation tasks.
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Shen, Xiaobo, Shirui Pan, Weiwei Liu, Yew-Soon Ong, and Quan-Sen Sun. "Discrete Network Embedding." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/493.
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Network embedding aims to seek low-dimensional vector representations for network nodes, by preserving the network structure. The network embedding is typically represented in continuous vector, which imposes formidable challenges in storage and computation costs, particularly in large-scale applications. To address the issue, this paper proposes a novel discrete network embedding (DNE) for more compact representations. In particular, DNE learns short binary codes to represent each node. The Hamming similarity between two binary embeddings is then employed to well approximate the ground-truth similarity. A novel discrete multi-class classifier is also developed to expedite classification. Moreover, we propose to jointly learn the discrete embedding and classifier within a unified framework to improve the compactness and discrimination of network embedding. Extensive experiments on node classification consistently demonstrate that DNE exhibits lower storage and computational complexity than state-of-the-art network embedding methods, while obtains competitive classification results.
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Sun, Yiwei, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, and Vasant Honavar. "MEGAN: A Generative Adversarial Network for Multi-View Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/489.
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Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e.g., friendship, shared interests in music, etc.) between real-world individuals or entities. There is an urgent need for methods to obtain low-dimensional, information preserving and typically nonlinear embeddings of such multi-view networks. However, most of the work on multi-view learning focuses on data that lack a network structure, and most of the work on network embeddings has focused primarily on single-view networks. Against this background, we consider the multi-view network representation learning problem, i.e., the problem of constructing low-dimensional information preserving embeddings of multi-view networks. Specifically, we investigate a novel Generative Adversarial Network (GAN) framework for Multi-View Network Embedding, namely MEGAN, aimed at preserving the information from the individual network views, while accounting for connectivity across (and hence complementarity of and correlations between) different views. The results of our experiments on two real-world multi-view data sets show that the embeddings obtained using MEGAN outperform the state-of-the-art methods on node classification, link prediction and visualization tasks.
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Guo, Junliang, Linli Xu, and Jingchang Liu. "SPINE: Structural Identity Preserved Inductive Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/333.
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Recent advances in the field of network embedding have shown that low-dimensional network representation is playing a critical role in network analysis. Most existing network embedding methods encode the local proximity of a node, such as the first- and second-order proximities. While being efficient, these methods are short of leveraging the global structural information between nodes distant from each other. In addition, most existing methods learn embeddings on one single fixed network, and thus cannot be generalized to unseen nodes or networks without retraining. In this paper we present SPINE, a method that can jointly capture the local proximity and proximities at any distance, while being inductive to efficiently deal with unseen nodes or networks. Extensive experimental results on benchmark datasets demonstrate the superiority of the proposed framework over the state of the art.
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Huang, Hong, Ruize Shi, Wei Zhou, Xiao Wang, Hai Jin, and Xiaoming Fu. "Temporal Heterogeneous Information Network Embedding." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/203.
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Heterogeneous information network (HIN) embedding, learning the low-dimensional representation of multi-type nodes, has been applied widely and achieved excellent performance. However, most of the previous works focus more on static heterogeneous networks or learning node embedding within specific snapshots, and seldom attention has been paid to the whole evolution process and capturing all temporal dynamics. In order to fill the gap of obtaining multi-type node embeddings by considering all temporal dynamics during the evolution, we propose a novel temporal HIN embedding method (THINE). THINE not only uses attention mechanism and meta-path to preserve structures and semantics in HIN but also combines the Hawkes process to simulate the evolution of the temporal network. Our extensive evaluations with various real-world temporal HINs demonstrate that THINE achieves state-of-the-art performance in both static and dynamic tasks, including node classification, link prediction, and temporal link recommendation.
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Zhang, Jie, Yuxiao Dong, Yan Wang, Jie Tang, and Ming Ding. "ProNE: Fast and Scalable Network Representation Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/594.
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Recent advances in network embedding has revolutionized the field of graph and network mining. However, (pre-)training embeddings for very large-scale networks is computationally challenging for most existing methods. In this work, we present ProNE---a fast, scalable, and effective model, whose single-thread version is 10--400x faster than efficient network embedding benchmarks with 20 threads, including LINE, DeepWalk, node2vec, GraRep, and HOPE. As a concrete example, the single-version ProNE requires only 29 hours to embed a network of hundreds of millions of nodes while it takes LINE weeks and DeepWalk months by using 20 threads. To achieve this, ProNE first initializes network embeddings efficiently by formulating the task as sparse matrix factorization. The second step of ProNE is to enhance the embeddings by propagating them in the spectrally modulated space. Extensive experiments on networks of various scales and types demonstrate that ProNE achieves both effectiveness and significant efficiency superiority when compared to the aforementioned baselines. In addition, ProNE's embedding enhancement step can be also generalized for improving other models at speed, e.g., offering >10% relative gains for the used baselines.
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Yang, Liang, Yuexue Wang, Junhua Gu, Chuan Wang, Xiaochun Cao, and Yuanfang Guo. "JANE: Jointly Adversarial Network Embedding." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/192.
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Motivated by the capability of Generative Adversarial Network on exploring the latent semantic space and capturing semantic variations in the data distribution, adversarial learning has been adopted in network embedding to improve the robustness. However, this important ability is lost in existing adversarially regularized network embedding methods, because their embedding results are directly compared to the samples drawn from perturbation (Gaussian) distribution without any rectification from real data. To overcome this vital issue, a novel Joint Adversarial Network Embedding (JANE) framework is proposed to jointly distinguish the real and fake combinations of the embeddings, topology information and node features. JANE contains three pluggable components, Embedding module, Generator module and Discriminator module. The overall objective function of JANE is defined in a min-max form, which can be optimized via alternating stochastic gradient. Extensive experiments demonstrate the remarkable superiority of the proposed JANE on link prediction (3% gains in both AUC and AP) and node clustering (5% gain in F1 score).
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Huang, Hong, Zixuan Fang, Xiao Wang, Youshan Miao, and Hai Jin. "Motif-Preserving Temporal Network Embedding." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/172.
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Network embedding, mapping nodes in a network to a low-dimensional space, achieves powerful performance. An increasing number of works focus on static network embedding, however, seldom attention has been paid to temporal network embedding, especially without considering the effect of mesoscopic dynamics when the network evolves. In light of this, we concentrate on a particular motif --- triad --- and its temporal dynamics, to study the temporal network embedding. Specifically, we propose MTNE, a novel embedding model for temporal networks. MTNE not only integrates the Hawkes process to stimulate the triad evolution process that preserves motif-aware high-order proximities, but also combines attention mechanism to distinguish the importance of different types of triads better. Experiments on various real-world temporal networks demonstrate that, compared with several state-of-the-art methods, our model achieves the best performance in both static and dynamic tasks, including node classification, link prediction, and link recommendation.
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Dong, Yuxiao, Ziniu Hu, Kuansan Wang, Yizhou Sun, and Jie Tang. "Heterogeneous Network Representation Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/677.
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Representation learning has offered a revolutionary learning paradigm for various AI domains. In this survey, we examine and review the problem of representation learning with the focus on heterogeneous networks, which consists of different types of vertices and relations. The goal of this problem is to automatically project objects, most commonly, vertices, in an input heterogeneous network into a latent embedding space such that both the structural and relational properties of the network can be encoded and preserved. The embeddings (representations) can be then used as the features to machine learning algorithms for addressing corresponding network tasks. To learn expressive embeddings, current research developments can fall into two major categories: shallow embedding learning and graph neural networks. After a thorough review of the existing literature, we identify several critical challenges that remain unaddressed and discuss future directions. Finally, we build the Heterogeneous Graph Benchmark to facilitate open research for this rapidly-developing topic.
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Zhang, Hongming, Liwei Qiu, Lingling Yi, and Yangqiu Song. "Scalable Multiplex Network Embedding." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/428.
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Network embedding has been proven to be helpful for many real-world problems. In this paper, we present a scalable multiplex network embedding model to represent information of multi-type relations into a unified embedding space. To combine information of different types of relations while maintaining their distinctive properties, for each node, we propose one high-dimensional common embedding and a lower-dimensional additional embedding for each type of relation. Then multiple relations can be learned jointly based on a unified network embedding model. We conduct experiments on two tasks: link prediction and node classification using six different multiplex networks. On both tasks, our model achieved better or comparable performance compared to current state-of-the-art models with less memory use.
Reports on the topic "Embedding Network"
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Bano, Masooda, and Zeena Oberoi. Embedding Innovation in State Systems: Lessons from Pratham in India. Research on Improving Systems of Education (RISE), December 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/058.
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The learning crisis in many developing countries has led to searches for innovative teaching models. Adoption of innovation, however, disrupts routine and breaks institutional inertia, requiring government employees to change their way of working. Introducing and embedding innovative methods for improving learning outcomes within state institutions is thus a major challenge. For NGO-led innovation to have largescale impact, we need to understand: (1) what factors facilitate its adoption by senior bureaucracy and political elites; and (2) how to incentivise district-level field staff and school principals and teachers, who have to change their ways of working, to implement the innovation? This paper presents an ethnographic study of Pratham, one of the most influential NGOs in the domain of education in India today, which has attracted growing attention for introducing an innovative teaching methodology— Teaching at the Right Level (TaRL) – with evidence of improved learning outcomes among primary-school students and adoption by a number of states in India. The case study suggests that while a combination of factors, including evidence of success, ease of method, the presence of a committed bureaucrat, and political opportunity are key to state adoption of an innovation, exposure to ground realities, hand holding and confidence building, informal interactions, provision of new teaching resources, and using existing lines of communication are core to ensuring the co-operation of those responsible for actual implementation. The Pratham case, however, also confirms existing concerns that even when NGO-led innovations are successfully implemented at a large scale, their replication across the state and their sustainability remain a challenge. Embedding good practice takes time; the political commitment leading to adoption of an innovation is often, however, tied to an immediate political opportunity being exploited by the political elites. Thus, when political opportunity rather than a genuine political will creates space for adoption of an innovation, state support for that innovation fades away before the new ways of working can replace the old habits. In contexts where states lack political will to improve learning outcomes, NGOs can only hope to make systematic change in state systems if, as in the case of Pratham, they operate as semi-social movements with large cadres of volunteers. The network of volunteers enables them to slow down and pick up again in response to changing political contexts, instead of quitting when state actors withdraw. Involving the community itself does not automatically lead to greater political accountability. Time-bound donor-funded NGO projects aiming to introduce innovation, however large in scale, simply cannot succeed in bringing about systematic change, because embedding change in state institutions lacking political will requires years of sustained engagement.
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Chakraborty, I., B. Kelley, B. Gallagher, and D. Merl. Performance Evaluation of Network Flow and Device Classification using Network Features and Device Embeddings. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1668490.
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Kelly, Luke. Lessons Learned on Cultural Heritage Protection in Conflict and Protracted Crisis. Institute of Development Studies (IDS), April 2021. http://dx.doi.org/10.19088/k4d.2021.068.
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This rapid review examines evidence on the lessons learned from initiatives aimed at embedding better understanding of cultural heritage protection within international monitoring, reporting and response efforts in conflict and protracted crisis. The report uses the terms cultural property and cultural heritage interchangeably. Since the signing of the Hague Treaty in 1954, there has bee a shift from 'cultural property' to 'cultural heritage'. Culture is seen less as 'property' and more in terms of 'ways of life'. However, in much of the literature and for the purposes of this review, cultural property and cultural heritage are used interchangeably. Tangible and intangible cultural heritage incorporates many things, from buildings of globally recognised aesthetic and historic value to places or practices important to a particular community or group. Heritage protection can be supported through a number of frameworks international humanitarian law, human rights law, and peacebuilding, in addition to being supported through networks of the cultural and heritage professions. The report briefly outlines some of the main international legal instruments and approaches involved in cultural heritage protection in section 2. Cultural heritage protection is carried out by national cultural heritage professionals, international bodies and non-governmental organisations (NGOs) as well as citizens. States and intergovernmental organisations may support cultural heritage protection, either bilaterally or by supporting international organisations. The armed forces may also include the protection of cultural heritage in some operations in line with their obligations under international law. In the third section, this report outlines broad lessons on the institutional capacity and politics underpinning cultural protection work (e.g. the strength of legal protections; institutional mandates; production and deployment of knowledge; networks of interested parties); the different approaches were taken; the efficacy of different approaches; and the interface between international and local approaches to heritage protection.