Relevant bibliographies by topics / Node embeddings

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Node embeddings'

Author: Grafiati
Published: 28 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Node embeddings"

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BOZKURT, ILKER NADI, HAI HUANG, BRUCE MAGGS, ANDRÉA RICHA, and MAVERICK WOO. "Mutual Embeddings." Journal of Interconnection Networks 15, no. 01n02 (March 2015): 1550001. http://dx.doi.org/10.1142/s0219265915500012.

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This paper introduces a type of graph embedding called a mutual embedding. A mutual embedding between two n-node graphs [Formula: see text] and [Formula: see text] is an identification of the vertices of V1 and V2, i.e., a bijection [Formula: see text], together with an embedding of G1 into G2 and an embedding of G2 into G1 where in the embedding of G1 into G2, each node u of G1 is mapped to π(u) in G2 and in the embedding of G2 into G1 each node v of G2 is mapped to [Formula: see text] in G1. The identification of vertices in G1 and G2 constrains the two embeddings so that it is not always possible for both to exhibit small congestion and dilation, even if there are traditional one-way embeddings in both directions with small congestion and dilation. Mutual embeddings arise in the context of finding preconditioners for accelerating the convergence of iterative methods for solving systems of linear equations. We present mutual embeddings between several types of graphs such as linear arrays, cycles, trees, and meshes, prove lower bounds on mutual embeddings between several classes of graphs, and present some open problems related to optimal mutual embeddings.
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Cheng, Pengyu, Yitong Li, Xinyuan Zhang, Liqun Chen, David Carlson, and Lawrence Carin. "Dynamic Embedding on Textual Networks via a Gaussian Process." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7562–69. http://dx.doi.org/10.1609/aaai.v34i05.6255.

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Textual network embedding aims to learn low-dimensional representations of text-annotated nodes in a graph. Prior work in this area has typically focused on fixed graph structures; however, real-world networks are often dynamic. We address this challenge with a novel end-to-end node-embedding model, called Dynamic Embedding for Textual Networks with a Gaussian Process (DetGP). After training, DetGP can be applied efficiently to dynamic graphs without re-training or backpropagation. The learned representation of each node is a combination of textual and structural embeddings. Because the structure is allowed to be dynamic, our method uses the Gaussian process to take advantage of its non-parametric properties. To use both local and global graph structures, diffusion is used to model multiple hops between neighbors. The relative importance of global versus local structure for the embeddings is learned automatically. With the non-parametric nature of the Gaussian process, updating the embeddings for a changed graph structure requires only a forward pass through the learned model. Considering link prediction and node classification, experiments demonstrate the empirical effectiveness of our method compared to baseline approaches. We further show that DetGP can be straightforwardly and efficiently applied to dynamic textual networks.
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Park, Chanyoung, Donghyun Kim, Jiawei Han, and Hwanjo Yu. "Unsupervised Attributed Multiplex Network Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5371–78. http://dx.doi.org/10.1609/aaai.v34i04.5985.

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Nodes in a multiplex network are connected by multiple types of relations. However, most existing network embedding methods assume that only a single type of relation exists between nodes. Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph. We present a simple yet effective unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and the global representation of the entire graph. We devise a systematic way to jointly integrate the node embeddings from multiple graphs by introducing 1) the consensus regularization framework that minimizes the disagreements among the relation-type specific node embeddings, and 2) the universal discriminator that discriminates true samples regardless of the relation types. We also show that the attention mechanism infers the importance of each relation type, and thus can be useful for filtering unnecessary relation types as a preprocessing step. Extensive experiments on various downstream tasks demonstrate that DMGI outperforms the state-of-the-art methods, even though DMGI is fully unsupervised.
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Hou, Yuchen, and Lawrence B. Holder. "On Graph Mining With Deep Learning: Introducing Model R for Link Weight Prediction." Journal of Artificial Intelligence and Soft Computing Research 9, no. 1 (January 1, 2019): 21–40. http://dx.doi.org/10.2478/jaiscr-2018-0022.

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Abstract Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to the link weight prediction problem. This model uses a node embedding technique that extracts node embeddings (knowledge of nodes) from the known links’ weights (relations between nodes) and uses this knowledge to predict the unknown links’ weights. We demonstrate the power of Model R through experiments and compare it with the stochastic block model and its derivatives. Model R shows that deep learning can be successfully applied to link weight prediction and it outperforms stochastic block model and its derivatives by up to 73% in terms of prediction accuracy. We analyze the node embeddings to confirm that closeness in embedding space correlates with stronger relationships as measured by the link weight. We anticipate this new approach will provide effective solutions to more graph mining tasks.
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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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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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Wang, Zheng, Yuexin Wu, Yang Bao, Jing Yu, and Xiaohui Wang. "Fusing Node Embeddings and Incomplete Attributes by Complement-Based Concatenation." Wireless Communications and Mobile Computing 2021 (February 25, 2021): 1–10. http://dx.doi.org/10.1155/2021/6654349.

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Network embedding that learns representations of network nodes plays a critical role in network analysis, since it enables many downstream learning tasks. Although various network embedding methods have been proposed, they are mainly designed for a single network scenario. This paper considers a “multiple network” scenario by studying the problem of fusing the node embeddings and incomplete attributes from two different networks. To address this problem, we propose to complement the incomplete attributes, so as to conduct data fusion via concatenation. Specifically, we first propose a simple inductive method, in which attributes are defined as a parametric function of the given node embedding vectors. We then propose its transductive variant by adaptively learning an adjacency graph to approximate the original network structure. Additionally, we also provide a light version of this transductive variant. Experimental results on four datasets demonstrate the superiority of our methods.
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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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Celikkanat, Abdulkadir, and Fragkiskos D. Malliaros. "Exponential Family Graph Embeddings." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3357–64. http://dx.doi.org/10.1609/aaai.v34i04.5737.

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Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks for sampling context nodes and the traditional Skip-Gram model to capture center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic exponential family graph embedding model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. We study three particular instances of this model, analyzing their properties and showing their relationship to existing unsupervised learning models. Our experimental evaluation on real-world datasets demonstrates that the proposed techniques outperform well-known baseline methods in two downstream machine learning tasks.
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Zhou, Sheng, Xin Wang, Jiajun Bu, Martin Ester, Pinggang Yu, Jiawei Chen, Qihao Shi, and Can Wang. "DGE: Deep Generative Network Embedding Based on Commonality and Individuality." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6949–56. http://dx.doi.org/10.1609/aaai.v34i04.6178.

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Network embedding plays a crucial role in network analysis to provide effective representations for a variety of learning tasks. Existing attributed network embedding methods mainly focus on preserving the observed node attributes and network topology in the latent embedding space, with the assumption that nodes connected through edges will share similar attributes. However, our empirical analysis of real-world datasets shows that there exist both commonality and individuality between node attributes and network topology. On the one hand, similar nodes are expected to share similar attributes and have edges connecting them (commonality). On the other hand, each information source may maintain individual differences as well (individuality). Simultaneously capturing commonality and individuality is very challenging due to their exclusive nature and existing work fail to do so. In this paper, we propose a deep generative embedding (DGE) framework which simultaneously captures commonality and individuality between network topology and node attributes in a generative process. Stochastic gradient variational Bayesian (SGVB) optimization is employed to infer model parameters as well as the node embeddings. Extensive experiments on four real-world datasets show the superiority of our proposed DGE framework in various tasks including node classification and link prediction.
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Dissertations / Theses on the topic "Node embeddings"

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Li, Mengzhen. "Integration of Node Embeddings for Multiple Versions of A Network." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1595435155975104.

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Yandrapally, Aruna Harini. "Combining Node Embeddings From Multiple Contexts Using Multi Dimensional Scaling." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627658906149105.

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Sabo, Jozef. "Aplikace metody učení bez učitele na hledání podobných grafů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445517.

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Goal of this master's thesis was in cooperation with the company Avast to design a system, which can extract knowledge from a database of graphs. Graphs, used for data mining, describe behaviour of computer systems and they are anonymously inserted into the company's database from systems of the company's products users. Each graph in the database can be assigned with one of two labels: clean or malware (malicious) graph. The task of the proposed self-learning system is to find clusters of graphs in the graph database, in which the classes of graphs do not mix. Graph clusters with only one class of graphs can be interpreted as different types of clean or malware graphs and they are a useful source of further analysis on the graphs. To evaluate the quality of the clusters, a custom metric, named as monochromaticity, was designed. The metric evaluates the quality of the clusters based on how much clean and malware graphs are mixed in the clusters. The best results of the metric were obtained when vector representations of graphs were created by a deep learning model (variational  graph autoencoder with two relation graph convolution operators) and the parameterless method MeanShift was used for clustering over vectors.
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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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Zhu, Xiaoting. "Systematic Assessment of Structural Features-Based Graph Embedding Methods with Application to Biomedical Networks." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592394966493963.

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Jmila, Houda. "Dynamic resource allocation and management in virtual networks and Clouds." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0023/document.

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L’informatique en nuage (Cloud computing) est une technologie prometteuse facilitant la réservation et de l'utilisation des ressources d’une manière flexible et dynamique. En plus des ressources informatiques traditionnelles, les utilisateurs du Cloud attendent à ce que des ressources réseaux leurs soient dédiées afin de faciliter le déploiement des fonctions et services réseau. Ils souhaitent pouvoir gérer l'ensemble d'un réseau virtuel (VN) ou infrastructure. Ainsi, les fournisseurs du Cloud doivent déployer des solutions de provisionnement des ressources dynamiques et adaptatives afin d’allouer des réseaux virtuels qui reflètent les besoins variables dans le temps des applications hébergés dans le Cloud. L’état de l’art sur l’allocation des réseaux virtuels s’est uniquement intéressé au problème de mapping des nœuds et liens virtuels composant une demande de réseau virtuel dans les nœuds et chemins du réseau de physique (infrastructure Cloud), connu sous le nom du problème de virtual network embedding (VNE). Peu d'attention a été accordée à la gestion des ressources allouées pour répondre en permanence aux besoins variables des réseaux virtuels hébergés dans le réseau physique et afin d'assurer une utilisation efficace des ressources. L'objectif de cette thèse est de permettre l'allocation des réseaux virtuels d’une manière dynamique et préventive pour faire face aux fluctuations de la demande au cours de la durée de vie du réseau virtuel, et pour améliorer l'utilisation des ressources du substrat. Pour atteindre ces objectifs, la thèse propose d'adaptation des algorithmes d'allocation des ressources pour répondre à l’évolution des demandes du réseau virtuel. Premièrement, nous allons étudier en profondeur l'extension d'un nœud virtuel, à savoir le cas où un nœud virtuel hébergé nécessite plus de ressources alors le nœud physique qui l’héberge n'a pas assez de ressources disponibles. Deuxièmement, nous allons améliorer la proposition précédente afin de considérer la rentabilité du réseau de substrat. Et enfin, nous allons gérer la variation de la demande en bande passante dans les liens virtuels. Par conséquent, la première partie de cette thèse fournit un algorithme heuristique qui traite la fluctuation de la demande dans les nœuds virtuels. L'idée principale de l'algorithme est de réallouer un ou plusieurs nœuds virtuels co-localisés dans du nœud de substrat, qui héberge le nœud en évolution pour libérer des ressources (ou faire de la place) pour le nœud en évolution. En plus de réduire le coût de réaffectation, notre proposition prend en compte et réduit l'interruption de service pendant la migration. L'algorithme précédent a été étendu pour concevoir un algorithme de reconfiguration préventif pour améliorer la rentabilité du réseau physique. En fait, notre proposition profite de la perturbation de la demande de ressources pour ranger le réseau physique à un coût minimal et sans perturbations. Lors de la réaffectation des nœuds virtuels pour faire place pour le nœud en extension, nous réaffectant les liens virtuels les plus congestionnées dans des ressources physiques moins saturées afin d’équilibrer la charge sur le réseau. Notre proposition offre le meilleur compromis entre le coût de réaffectation et l'équilibrage des charges. Enfin, un framework distribué, parallèle et à vue locale a été mis au point pour traiter toutes les formes de fluctuations de la demande en bande passante dans les liens virtuels. Elle se compose d'un contrôleur et trois algorithmes exécutés dans chaque nœud du substrat d'une manière distribuée et parallèle. Le framework est basé sur l'auto-stabilisation, et peut gérer de nombreuses et différentes formes de variations de la demande de bande passante simultanément
Cloud computing is a promising technology enabling IT resources reservation and utilization on a pay-as-you-go manner. In addition to the traditional computing resources, cloud tenants expect compete networking of their dedicated resources to easily deploy network functions and services. They need to manage an entire Virtual Network (VN) or infrastructure. Thus, Cloud providers should deploy dynamic and adaptive resource provisioning solutions to allocate virtual networks that reflect the time-varying needs of Cloud-hosted applications. Prior work on virtual network resource provisioning only focused on the problem of mapping the virtual nodes and links composing a virtual network request to the substrate network nodes and paths, known as the Virtual network embedding (VNE) problem. Little attention was paid to the resource management of the allocated resources to continuously meet the varying demands of embedded virtual networks and to ensure efficient substrate resource utilization. The aim of this thesis is to enable dynamic and preventive virtual network resources provisioning to deal with demand fluctuation during the virtual network lifetime, and to enhance the substrate resources usage. To reach these goals, the thesis proposes adaptive resource allocation algorithms for evolving virtual network requests. We adress the extension of an embedded virtual node requiring more resources and consider the substrate network profitability. We also deal with the bandwidth demand variation in embedded virtual links. We first provide a heuristic algorithm to deal with virtual nodes demand fluctuation. The work is extended by designing a preventive re-configuration scheme to enhance substrate network profitability. Finally, a distributed, local-view and parallel framework was devised to handle embedded virtual links bandwidth fluctuations. The approach is composed of a controller and three algorithms running in each substrate node in a distributed and parallel manner. The framework is based on the self-stabilization approach, and can manage various forms of bandwidth demand variations simultaneously
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Lin, Christy. "Unsupervised random walk node embeddings for network block structure representation." Thesis, 2021. https://hdl.handle.net/2144/43083.

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There has been an explosion of network data in the physical, chemical, biological, computational, and social sciences in the last few decades. Node embeddings, i.e., Euclidean-space representations of nodes in a network, make it possible to apply to network data, tools and algorithms from multivariate statistics and machine learning that were developed for Euclidean-space data. Random walk node embeddings are a class of recently developed node embedding techniques where the vector representations are learned by optimizing objective functions involving skip-bigram statistics computed from random walks on the network. They have been applied to many supervised learning problems such as link prediction and node classification and have demonstrated state-of-the-art performance. Yet, their properties remain poorly understood. This dissertation studies random walk based node embeddings in an unsupervised setting within the context of capturing hidden block structure in the network, i.e., learning node representations that reflect their patterns of adjacencies to other nodes. This doctoral research (i) Develops VEC, a random walk based unsupervised node embedding algorithm, and a series of relaxations, and experimentally validates their performance for the community detection problem under the Stochastic Block Model (SBM). (ii) Characterizes the ergodic limits of the embedding objectives to create non-randomized versions. (iii) Analyzes the embeddings for expected SBM networks and establishes certain concentration properties of the limiting ergodic objective in the large network asymptotic regime. Comprehensive experimental results on real world and SBM random networks are presented to illustrate and compare the distributional and block-structure properties of node embeddings generated by VEC and related algorithms. As a step towards theoretical understanding, it is proved that for the variants of VEC with ergodic limits and convex relaxations, the embedding Grammian of the expected network of a two-community SBM has rank at most 2. Further experiments reveal that these extensions yield embeddings whose distribution is Gaussian-like, centered at the node embeddings of the expected network within each community, and concentrate in the linear degree-scaling regime as the number of nodes increases.
2023-09-24T00:00:00Z
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Feng, Ming-Han, and 馮銘漢. "Multi-relational Network Embeddings Considering Link Structures and Node Attributes." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/466w9j.

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碩士
國立臺灣大學
資訊網路與多媒體研究所
105
Multi-relational networks are ubiquitous in real world. It is, however, difficult to be analyzed due to the complex structure of the network. A plausible approach to analyze such network is to embed the entity information as an informative feature vector. However, present embedding methods either consider only single-relational information, or neglect the importance of structural information. In addition, some of them require fine-tuning of hyperparameters, which might not be feasible for an unsupervised embedding generation task. In this work we propose MUSE, a Multi-relational Unsupervised link-Structure preserving Embeddings method, which learns the representations for each node and relation by maximizing the likelihood of observations on the given network. Additional node attributes are also preserved under our design. Besides, MUSE features less sensitive hyperparameters and scalablility by edge-sampling strategy. The extensive experiments on various real-world applications also demonstrate the effectiveness and robustness of our model.
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"Numerical Performance of the Holomorphic Embedding Method." Master's thesis, 2018. http://hdl.handle.net/2286/R.I.50476.

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abstract: Recently, a novel non-iterative power flow (PF) method known as the Holomorphic Embedding Method (HEM) was applied to the power-flow problem. Its superiority over other traditional iterative methods such as Gauss-Seidel (GS), Newton-Raphson (NR), Fast Decoupled Load Flow (FDLF) and their variants is that it is theoretically guaranteed to find the operable solution, if one exists, and will unequivocally signal if no solution exists. However, while theoretical convergence is guaranteed by Stahl’s theorem, numerical convergence is not. Numerically, the HEM may require extended precision to converge, especially for heavily-loaded and ill-conditioned power system models. In light of the advantages and disadvantages of the HEM, this report focuses on three topics: 1. Exploring the effect of double and extended precision on the performance of HEM, 2. Investigating the performance of different embedding formulations of HEM, and 3. Estimating the saddle-node bifurcation point (SNBP) from HEM-based Thévenin-like networks using pseudo-measurements. The HEM algorithm consists of three distinct procedures that might accumulate roundoff error and cause precision loss during the calculations: the matrix equation solution calculation, the power series inversion calculation and the Padé approximant calculation. Numerical experiments have been performed to investigate which aspect of the HEM algorithm causes the most precision loss and needs extended precision. It is shown that extended precision must be used for the entire algorithm to improve numerical performance. A comparison of two common embedding formulations, a scalable formulation and a non-scalable formulation, is conducted and it is shown that these two formulations could have extremely different numerical properties on some power systems. The application of HEM to the SNBP estimation using local-measurements is explored. The maximum power transfer theorem (MPTT) obtained for nonlinear Thévenin-like networks is validated with high precision. Different numerical methods based on MPTT are investigated. Numerical results show that the MPTT method works reasonably well for weak buses in the system. The roots method, as an alternative, is also studied. It is shown to be less effective than the MPTT method but the roots of the Padé approximant can be used as a research tool for determining the effects of noisy measurements on the accuracy of SNBP prediction.
Dissertation/Thesis
Masters Thesis Electrical Engineering 2018
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Liu, Hsien Jen, and 劉獻仁. "Node Fault Tolerant Hamiltonian Cycle Embedding in Honeycomb Rectangular Torus Network." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/41779530434993281960.

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碩士
國立交通大學
資訊科學系
89
In this thesis, we propose a variation of Honeycomb Torus, called Honeycomb Rectangular Torus Networks.The Honeycomb Rectangular Torus is Hamlitonian and to remove a pair of nodes from each partite sets of graph, still has Hamlitonian cycle. Honeycomb Rectangular Torus Network is homogeneous graph. This property is very helpful, because we can reduce the complexity of the Honeycomb Rectangular Torus. The Honeycomb Rectangular Torus HReT(m,n) is defined in the paper written by Stojmenovic Honeycomb Rectangular Torus has many good properties including homogeneous graph, 3-regular graph, Bipartite graph , hamiltonian cycle, 1-p hamiltonian cycle, hamiltonian connectivity etc. Since $HReT(m,n)$ is regular of degree 3, it can tolerate at most two node faults in the worst case in order to construct a hamiltonian cycle. In this paper, we will prove that all the honeycomb rectangular torus have hamiltonian cycle, when any one pair nodes fault.
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Books on the topic "Node embeddings"

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M. Le Marc Le Menestrel. A note on embedding von Neumann and Morgenstern utility theory in a qualitative context. Fontainebleau: INSEAD, 1998.

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Book chapters on the topic "Node embeddings"

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Vu, Thuy, and D. Stott Parker. "Mining Community Structure with Node Embeddings." In Lecture Notes in Social Networks, 123–40. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51367-6_6.

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Chekol, Melisachew Wudage, and Giuseppe Pirrò. "Refining Node Embeddings via Semantic Proximity." In Lecture Notes in Computer Science, 74–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62419-4_5.

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Meghashyam, P., and V. Susheela Devi. "Community Based Node Embeddings for Networks." In Communications in Computer and Information Science, 380–87. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36808-1_41.

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Wu, Changmin, Giannis Nikolentzos, and Michalis Vazirgiannis. "Matching Node Embeddings Using Valid Assignment Kernels." In Complex Networks and Their Applications VIII, 810–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36687-2_67.

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Roy, Aman, Vinayak Kumar, Debdoot Mukherjee, and Tanmoy Chakraborty. "Learning Multigraph Node Embeddings Using Guided Lévy Flights." In Advances in Knowledge Discovery and Data Mining, 524–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47426-3_41.

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Idahl, Maximilian, Megha Khosla, and Avishek Anand. "Finding Interpretable Concept Spaces in Node Embeddings Using Knowledge Bases." In Machine Learning and Knowledge Discovery in Databases, 229–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_20.

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Riba, Pau, Josep Lladós, Alicia Fornés, and Anjan Dutta. "Large-Scale Graph Indexing Using Binary Embeddings of Node Contexts." In Graph-Based Representations in Pattern Recognition, 208–17. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18224-7_21.

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Wang, Zheng, Jian Cui, Yingying Chen, and Changjun Hu. "SOLAR: Fusing Node Embeddings and Attributes into an Arbitrary Space." In Database Systems for Advanced Applications, 442–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59419-0_27.

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Khan, Rayyan Ahmad, Muhammad Umer Anwaar, Omran Kaddah, Zhiwei Han, and Martin Kleinsteuber. "Unsupervised Learning of Joint Embeddings for Node Representation and Community Detection." In Machine Learning and Knowledge Discovery in Databases. Research Track, 19–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86520-7_2.

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Steenwinckel, Bram, Gilles Vandewiele, Pieter Bonte, Michael Weyns, Heiko Paulheim, Petar Ristoski, Filip De Turck, and Femke Ongenae. "Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs." In Communications in Computer and Information Science, 70–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87101-7_8.

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Conference papers on the topic "Node embeddings"

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Singer, Uriel, Ido Guy, and Kira Radinsky. "Node Embedding over Temporal Graphs." 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.2019/640.

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In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e.g., link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at different time points, and eventually adapted for the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and multi-label node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm shows performance improvements across many of the datasets and baselines and is found particularly effective for graphs that are less cohesive, with a lower clustering coefficient.
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Celikkanat, Abdulkadir, and Fragkiskos D. Malliaros. "Kernel Node Embeddings." In 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2019. http://dx.doi.org/10.1109/globalsip45357.2019.8969363.

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Dalmia, Ayushi, Ganesh J, and Manish Gupta. "Towards Interpretation of Node Embeddings." In Companion of the The Web Conference 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3191523.

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Luo, Gongxu, Jianxin Li, Hao Peng, Carl Yang, Lichao Sun, Philip S. Yu, and Lifang He. "Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks." 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.2021/381.

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Graph representation learning has achieved great success in many areas, including e-commerce, chemistry, biology, etc. However, the fundamental problem of choosing the appropriate dimension of node embedding for a given graph still remains unsolved. The commonly used strategies for Node Embedding Dimension Selection (NEDS) based on grid search or empirical knowledge suffer from heavy computation and poor model performance. In this paper, we revisit NEDS from the perspective of minimum entropy principle. Subsequently, we propose a novel Minimum Graph Entropy (MinGE) algorithm for NEDS with graph data. To be specific, MinGE considers both feature entropy and structure entropy on graphs, which are carefully designed according to the characteristics of the rich information in them. The feature entropy, which assumes the embeddings of adjacent nodes to be more similar, connects node features and link topology on graphs. The structure entropy takes the normalized degree as basic unit to further measure the higher-order structure of graphs. Based on them, we design MinGE to directly calculate the ideal node embedding dimension for any graph. Finally, comprehensive experiments with popular Graph Neural Networks (GNNs) on benchmark datasets demonstrate the effectiveness and generalizability of our proposed MinGE.
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Hao, Yu, Xin Cao, Yixiang Fang, Xike Xie, and Sibo Wang. "Inductive Link Prediction for Nodes Having Only Attribute Information." 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.2020/168.

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Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. However, many real-world applications require inductive prediction for new nodes having only attribute information. It is more challenging since the new nodes do not have structure information and cannot be seen during the model training. To solve this problem, we propose a model called DEAL, which consists of three components: two node embedding encoders and one alignment mechanism. The two encoders aim to output the attribute-oriented node embedding and the structure-oriented node embedding, and the alignment mechanism aligns the two types of embeddings to build the connections between the attributes and links. Our model DEAL is versatile in the sense that it works for both inductive and transductive link prediction. Extensive experiments on several benchmark datasets show that our proposed model significantly outperforms existing inductive link prediction methods, and also outperforms the state-of-the-art methods on transductive link prediction.
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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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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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Vu, Thuy, and D. Stott Parker. "Node Embeddings in Social Network Analysis." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808797.2809408.

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Zhang, Yao, Yun Xiong, Xiangnan Kong, and Yangyong Zhu. "Learning Node Embeddings in Interaction Graphs." In CIKM '17: ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132847.3132918.

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Guo, Xuan, Qiang Tian, Wang Zhang, Wenjun Wang, and Pengfei Jiao. "Learning Stochastic Equivalence based on Discrete Ricci Curvature." 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.2021/201.

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Role-based network embedding methods aim to preserve node-centric connectivity patterns, which are expressions of node roles, into low-dimensional vectors. However, almost all the existing methods are designed for capturing a relaxation of automorphic equivalence or regular equivalence. They may be good at structure identification but could show poorer performance on role identification. Because automorphic equivalence and regular equivalence strictly tie the role of a node to the identities of all its neighbors. To mitigate this problem, we construct a framework called Curvature-based Network Embedding with Stochastic Equivalence (CNESE) to embed stochastic equivalence. More specifically, we estimate the role distribution of nodes based on discrete Ricci curvature for its excellent ability to concisely representing local topology. We use a Variational Auto-Encoder to generate embeddings while a degree-guided regularizer and a contrastive learning regularizer are leveraged to improving both its robustness and discrimination ability. The effectiveness of our proposed CNESE is demonstrated by extensive experiments on real-world networks.
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