Готові списки джерел за темами / Embedding de graph

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Добірка наукової літератури з теми "Embedding de graph"

Автор: Grafiati

Опубліковано: 22 лютого 2025

Оновлено: 13 липня 2025

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Статті в журналах з теми "Embedding de graph"

Liang, Jiongqian, Saket Gurukar, and Srinivasan Parthasarathy. "MILE: A Multi-Level Framework for Scalable Graph Embedding." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 361–72. http://dx.doi.org/10.1609/icwsm.v15i1.18067.

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Анотація:

Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework – a generic methodology allowing contemporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarsest graph and refines the embeddings to the original graph through a graph convolution neural network that it learns. The proposed MILE framework is agnostic to the underlying graph embedding techniques and can be applied to many existing graph embedding methods without modifying them. We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs. Experimental results on five large-scale datasets demonstrate that MILE significantly boosts the speed (order of magnitude) of graph embedding while generating embeddings of better quality, for the task of node classification. MILE can comfortably scale to a graph with 9 million nodes and 40 million edges, on which existing methods run out of memory or take too long to compute on a modern workstation. Our code and data are publicly available with detailed instructions for adding new base embedding methods: https://github.com/jiongqian/MILE.

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Duong, Chi Thang, Trung Dung Hoang, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, and Karl Aberer. "Scalable robust graph embedding with Spark." Proceedings of the VLDB Endowment 15, no. 4 (2021): 914–22. http://dx.doi.org/10.14778/3503585.3503599.

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Анотація:

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. While several techniques to scale graph embedding using compute clusters have been proposed, they require continuous communication between the compute nodes and cannot handle node failure. We therefore propose a framework for scalable and robust graph embedding based on the MapReduce model, which can distribute any existing embedding technique. Our method splits a graph into subgraphs to learn their embeddings in isolation and subsequently reconciles the embedding spaces derived for the subgraphs. We realize this idea through a novel distributed graph decomposition algorithm. In addition, we show how to implement our framework in Spark to enable efficient learning of effective embeddings. Experimental results illustrate that our approach scales well, while largely maintaining the embedding quality.

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Zhou, Houquan, Shenghua Liu, Danai Koutra, Huawei Shen, and Xueqi Cheng. "A Provable Framework of Learning Graph Embeddings via Summarization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4946–53. http://dx.doi.org/10.1609/aaai.v37i4.25621.

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Given a large graph, can we learn its node embeddings from a smaller summary graph? What is the relationship between embeddings learned from original graphs and their summary graphs? Graph representation learning plays an important role in many graph mining applications, but learning em-beddings of large-scale graphs remains a challenge. Recent works try to alleviate it via graph summarization, which typ-ically includes the three steps: reducing the graph size by combining nodes and edges into supernodes and superedges,learning the supernode embedding on the summary graph and then restoring the embeddings of the original nodes. How-ever, the justification behind those steps is still unknown. In this work, we propose GELSUMM, a well-formulated graph embedding learning framework based on graph sum-marization, in which we show the theoretical ground of learn-ing from summary graphs and the restoration with the three well-known graph embedding approaches in a closed form.Through extensive experiments on real-world datasets, we demonstrate that our methods can learn graph embeddings with matching or better performance on downstream tasks.This work provides theoretical analysis for learning node em-beddings via summarization and helps explain and under-stand the mechanism of the existing works.

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Fang, Peng, Arijit Khan, Siqiang Luo, et al. "Distributed Graph Embedding with Information-Oriented Random Walks." Proceedings of the VLDB Endowment 16, no. 7 (2023): 1643–56. http://dx.doi.org/10.14778/3587136.3587140.

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Анотація:

Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, DistGER, which can scale to embed billion-edge graphs. DistGER incrementally computes information-centric random walks. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines. DistGER also improves the distributed Skip-Gram learning model to generate node embeddings by optimizing the access locality, CPU throughput, and synchronization efficiency. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including KnightKing, DistDGL, and Pytorch-BigGraph, DistGER exhibits 2.33×--129× acceleration, 45% reduction in cross-machines communication, and >10% effectiveness improvement in downstream tasks.

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Mao, Yuqing, and Kin Wah Fung. "Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts." Journal of the American Medical Informatics Association 27, no. 10 (2020): 1538–46. http://dx.doi.org/10.1093/jamia/ocaa136.

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Abstract Objective The study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts. Materials and Methods Concept sentence embeddings were generated for UMLS concepts by applying the word embedding models BioWordVec and various flavors of BERT to concept sentences formed by concatenating UMLS terms. Graph embeddings were generated by the graph convolutional networks and 4 knowledge graph embedding models, using graphs built from UMLS hierarchical relations. Semantic relatedness was measured by the cosine between the concepts’ embedding vectors. Performance was compared with 2 traditional path-based (shortest path and Leacock-Chodorow) measurements and the publicly available concept embeddings, cui2vec, generated from large biomedical corpora. The concept sentence embeddings were also evaluated on a word sense disambiguation (WSD) task. Reference standards used included the semantic relatedness and semantic similarity datasets from the University of Minnesota, concept pairs generated from the Standardized MedDRA Queries and the MeSH (Medical Subject Headings) WSD corpus. Results Sentence embeddings generated by BioWordVec outperformed all other methods used individually in semantic relatedness measurements. Graph convolutional network graph embedding uniformly outperformed path-based measurements and was better than some word embeddings for the Standardized MedDRA Queries dataset. When used together, combined word and graph embedding achieved the best performance in all datasets. For WSD, the enhanced versions of BERT outperformed BioWordVec. Conclusions Word and graph embedding techniques can be used to harness terms and relations in the UMLS to measure semantic relatedness between concepts. Concept sentence embedding outperforms path-based measurements and cui2vec, and can be further enhanced by combining with graph embedding.

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Makarov, Ilya, Dmitrii Kiselev, Nikita Nikitinsky, and Lovro Subelj. "Survey on graph embeddings and their applications to machine learning problems on graphs." PeerJ Computer Science 7 (February 4, 2021): e357. http://dx.doi.org/10.7717/peerj-cs.357.

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Анотація:

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

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FRIESEN, TYLER, and VASSILY OLEGOVICH MANTUROV. "EMBEDDINGS OF *-GRAPHS INTO 2-SURFACES." Journal of Knot Theory and Its Ramifications 22, no. 12 (2013): 1341005. http://dx.doi.org/10.1142/s0218216513410058.

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This paper considers *-graphs in which all vertices have degree 4 or 6, and studies the question of calculating the genus of orientable 2-surfaces into which such graphs may be embedded. A *-graph is a graph endowed with a formal adjacency structure on the half-edges around each vertex, and an embedding of a *-graph is an embedding under which the formal adjacency relation on half-edges corresponds to the adjacency relation induced by the embedding. *-graphs are a natural generalization of four-valent framed graphs, which are four-valent graphs with an opposite half-edge structure. In [Embeddings of four-valent framed graphs into 2-surfaces, Dokl. Akad. Nauk424(3) (2009) 308–310], the question of whether a four-valent framed graph admits a ℤ2-homologically trivial embedding into a given surface was shown to be equivalent to a problem on matrices. We show that a similar result holds for *-graphs in which all vertices have degree 4 or 6. This gives an algorithm in quadratic time to determine whether a *-graph admits an embedding into the plane.

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Mohar, Bojan. "Combinatorial Local Planarity and the Width of Graph Embeddings." Canadian Journal of Mathematics 44, no. 6 (1992): 1272–88. http://dx.doi.org/10.4153/cjm-1992-076-8.

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Анотація:

AbstractLet G be a graph embedded in a closed surface. The embedding is “locally planar” if for each face, a “large” neighbourhood of this face is simply connected. This notion is formalized, following [RV], by introducing the width ρ(ψ) of the embedding ψ. It is shown that embeddings with ρ(ψ) ≥ 3 behave very much like the embeddings of planar graphs in the 2-sphere. Another notion, “combinatorial local planarity”, is introduced. The criterion is independent of embeddings of the graph, but it guarantees that a given cycle in a graph G must be contractible in any minimal genus embedding of G (either orientable, or non-orientable). It generalizes the width introduced before. As application, short proofs of some important recently discovered results about embeddings of graphs are given and generalized or improved. Uniqueness and switching equivalence of graphs embedded in a fixed surface are also considered.

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Chen, Mingyang, Wen Zhang, Zhen Yao, et al. "Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4182–90. http://dx.doi.org/10.1609/aaai.v37i4.25535.

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We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs. Conventional knowledge graph embedding methods map elements in a knowledge graph, including entities and relations, into continuous vector spaces by assigning them one or multiple specific embeddings (i.e., vector representations). Thus the number of embedding parameters increases linearly as the growth of knowledge graphs. In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities. To obtain the embeddings for the full set of entities, we encode their distinguishable information from their connected relations, k-nearest reserved entities, and multi-hop neighbors. We learn universal and entity-agnostic encoders for transforming distinguishable information into entity embeddings. This approach allows our proposed EARL to have a static, efficient, and lower parameter count than conventional knowledge graph embedding methods. Experimental results show that EARL uses fewer parameters and performs better on link prediction tasks than baselines, reflecting its parameter efficiency.

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Xie, Anze, Anders Carlsson, Jason Mohoney, et al. "Demo of marius." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2759–62. http://dx.doi.org/10.14778/3476311.3476338.

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Анотація:

Graph embeddings have emerged as the de facto representation for modern machine learning over graph data structures. The goal of graph embedding models is to convert high-dimensional sparse graphs into low-dimensional, dense and continuous vector spaces that preserve the graph structure properties. However, learning a graph embedding model is a resource intensive process, and existing solutions rely on expensive distributed computation to scale training to instances that do not fit in GPU memory. This demonstration showcases Marius: a new open-source engine for learning graph embedding models over billion-edge graphs on a single machine. Marius is built around a recently-introduced architecture for machine learning over graphs that utilizes pipelining and a novel data replacement policy to maximize GPU utilization and exploit the entire memory hierarchy (including disk, CPU, and GPU memory) to scale to large instances. The audience will experience how to develop, train, and deploy graph embedding models using Marius' configuration-driven programming model. Moreover, the audience will have the opportunity to explore Marius' deployments on applications including link-prediction on WikiKG90M and reasoning queries on a paleobiology knowledge graph. Marius is available as open source software at https://marius-project.org.

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Більше джерел

Дисертації з теми "Embedding de graph"

Zhang, Zheng. "Explorations in Word Embeddings : graph-based word embedding learning and cross-lingual contextual word embedding learning." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS369/document.

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Les plongements lexicaux sont un composant standard des architectures modernes de traitement automatique des langues (TAL). Chaque fois qu'une avancée est obtenue dans l'apprentissage de plongements lexicaux, la grande majorité des tâches de traitement automatique des langues, telles que l'étiquetage morphosyntaxique, la reconnaissance d'entités nommées, la recherche de réponses à des questions, ou l'inférence textuelle, peuvent en bénéficier. Ce travail explore la question de l'amélioration de la qualité de plongements lexicaux monolingues appris par des modèles prédictifs et celle de la mise en correspondance entre langues de plongements lexicaux contextuels créés par des modèles préentraînés de représentation de la langue comme ELMo ou BERT.Pour l'apprentissage de plongements lexicaux monolingues, je prends en compte des informations globales au corpus et génère une distribution de bruit différente pour l'échantillonnage d'exemples négatifs dans word2vec. Dans ce but, je précalcule des statistiques de cooccurrence entre mots avec corpus2graph, un paquet Python en source ouverte orienté vers les applications en TAL : il génère efficacement un graphe de cooccurrence à partir d'un grand corpus, et lui applique des algorithmes de graphes tels que les marches aléatoires. Pour la mise en correspondance translingue de plongements lexicaux, je relie les plongements lexicaux contextuels à des plongements de sens de mots. L'algorithme amélioré de création d'ancres que je propose étend également la portée des algorithmes de mise en correspondance de plongements lexicaux du cas non-contextuel au cas des plongements contextuels<br>Word embeddings are a standard component of modern natural language processing architectures. Every time there is a breakthrough in word embedding learning, the vast majority of natural language processing tasks, such as POS-tagging, named entity recognition (NER), question answering, natural language inference, can benefit from it. This work addresses the question of how to improve the quality of monolingual word embeddings learned by prediction-based models and how to map contextual word embeddings generated by pretrained language representation models like ELMo or BERT across different languages.For monolingual word embedding learning, I take into account global, corpus-level information and generate a different noise distribution for negative sampling in word2vec. In this purpose I pre-compute word co-occurrence statistics with corpus2graph, an open-source NLP-application-oriented Python package that I developed: it efficiently generates a word co-occurrence network from a large corpus, and applies to it network algorithms such as random walks. For cross-lingual contextual word embedding mapping, I link contextual word embeddings to word sense embeddings. The improved anchor generation algorithm that I propose also expands the scope of word embedding mapping algorithms from context independent to contextual word embeddings

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Ahmed, Algabli Shaima. "Learning the Graph Edit Distance through embedding the graph matching." Doctoral thesis, Universitat Rovira i Virgili, 2020. http://hdl.handle.net/10803/669612.

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Els gràfics són estructures de dades abstractes que s’utilitzen per modelar problemes reals amb dues entitats bàsiques: nodes i vores. Cada node o vèrtex representa un punt d'interès rellevant d'un problema i cada vora representa la relació entre aquests punts. Es poden atribuir nodes i vores per augmentar la precisió del model, cosa que significa que aquests atributs podrien variar des de vectors de característiques fins a etiquetes de descripció. A causa d'aquesta versatilitat, s'han trobat moltes aplicacions en camps com la visió per ordinador, la biomèdica i l'anàlisi de xarxa, etc., la primera part d'aquesta tesi presenta un mètode general per aprendre automàticament els costos d'edició que comporta l'edició de gràfics. Distància. El mètode es basa en incrustar parells de gràfics i el seu mapeig de node a node de veritat terrestre en un espai euclidià. D’aquesta manera, l’algoritme d’aprenentatge no necessita calcular cap concordança de gràfics tolerant als errors, que és l’inconvenient principal d’altres mètodes a causa de la seva intrínseca complexitat computacional exponencial. No obstant això, el mètode d’aprenentatge té la principal restricció que els costos d’edició han de ser constants. A continuació, posem a prova aquest mètode amb diverses bases de dades gràfiques i també l’aplicem per realitzar el registre d’imatges. A la segona part de la tesi, aquest mètode es particularitza a la verificació d’empremtes dactilars. Les dues diferències principals respecte a l’altre mètode són que només definim els costos d’edició de substitució als nodes. Per tant, suposem que els gràfics no tenen arestes. I també, el mètode d’aprenentatge no es basa en una classificació lineal sinó en una regressió lineal.<br>Los gráficos son estructuras de datos abstractas que se utilizan para modelar problemas reales con dos entidades básicas: nodos y aristas. Cada nodo o vértice representa un punto de interés relevante de un problema, y cada borde representa la relación entre estos puntos. Se podrían atribuir nodos y bordes para aumentar la precisión del modelo, lo que significa que estos atributos podrían variar de vectores de características a etiquetas de descripción. Debido a esta versatilidad, se han encontrado muchas aplicaciones en campos como visión por computadora, biomédicos y análisis de redes, etc. La primera parte de esta tesis presenta un método general para aprender automáticamente los costos de edición involucrados en la Edición de Gráficos Distancia. El método se basa en incrustar pares de gráficos y su mapeo de nodo a nodo de verdad fundamental en un espacio euclidiano. De esta manera, el algoritmo de aprendizaje no necesita calcular ninguna coincidencia de gráfico tolerante a errores, que es el principal inconveniente de otros métodos debido a su complejidad computacional exponencial intrínseca. Sin embargo, el método de aprendizaje tiene la principal restricción de que los costos de edición deben ser constantes. Luego probamos este método con varias bases de datos de gráficos y también lo aplicamos para realizar el registro de imágenes. En la segunda parte de la tesis, este método se especializa en la verificación de huellas digitales. Las dos diferencias principales con respecto al otro método son que solo definimos los costos de edición de sustitución en los nodos. Por lo tanto, suponemos que los gráficos no tienen aristas. Y también, el método de aprendizaje no se basa en una clasificación lineal sino en una regresión lineal.<br>Graphs are abstract data structures used to model real problems with two basic entities: nodes and edges. Each node or vertex represents a relevant point of interest of a problem, and each edge represents the relationship between these points. Nodes and edges could be attributed to increase the accuracy of the model, which means that these attributes could vary from feature vectors to description labels. Due to this versatility, many applications have been found in fields such as computer vision, biomedics, and network analysis, and so on .The first part of this thesis presents a general method to automatically learn the edit costs involved in the Graph Edit Distance. The method is based on embedding pairs of graphs and their ground-truth node-tonode mapping into a Euclidean space. In this way, the learning algorithm does not need to compute any Error-Tolerant Graph Matching, which is the main drawback of other methods due to its intrinsic exponential computational complexity. Nevertheless, the learning method has the main restriction that edit costs have to be constant. Then we test this method with several graph databases and also we apply it to perform image registration. In the second part of the thesis, this method is particularized to fingerprint verification. The two main differences with respect to the other method are that we only define the substitution edit costs on the nodes. Thus, we assume graphs do not have edges. And also, the learning method is not based on a linear classification but on a linear regression.

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Carroll, Douglas Edmonds. "Embedding parameterized graph classes into normed spaces." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1324389171&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Rocha, Mário. "The embedding of complete bipartite graphs onto grids with a minimum grid cutwidth." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2311.

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Dube, Matthew P. "An Embedding Graph for 9-Intersection Topological Spatial Relations." Fogler Library, University of Maine, 2009. http://www.library.umaine.edu/theses/pdf/DubeMP2009.pdf.

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MONDAL, DEBAJYOTI. "Embedding a Planar Graph on a Given Point Set." Springer-Verlag Berlin, 2012. http://hdl.handle.net/1993/8869.

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Анотація:

A point-set embedding of a planar graph G with n vertices on a set S of n points is a planar straight-line drawing of G, where each vertex of G is mapped to a distinct point of S. We prove that the point-set embeddability problem is NP-complete for 3-connected planar graphs, answering a question of Cabello [20]. We give an O(nlog^3n)-time algorithm for testing point-set embeddability of plane 3-trees, improving the algorithm of Moosa and Rahman [60]. We prove that no set of 24 points can support all planar 3-trees with 24 vertices, partially answering a question of Kobourov [55]. We compute 2-bend point-set embeddings of plane 3-trees in O(W^2) area, where W is the length of longest edge of the bounding box of S. Finally, we design algorithms for testing convex point-set embeddability of klee graphs and arbitrary planar graphs.

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Mitropolitsky, Milko. "On the Impact of Graph Embedding on Device Placement." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280435.

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Modern neural network (NN) models require more data and parameters in or- der to perform ever more complex tasks. When an NN model becomes too massive to fit on a single machine, it may need to be distributed across multi- ple machines. What policies should be used when distributing an NN model, and more concretely how different parts of the model should be disseminated across the various machines is called the device placement problem. Tackling the matter is the focus of this thesis.Previous approaches have required the placement policies to be created manually by human experts. Since that method does not scale well, the cur- rent efforts to tackling the device placement problem focus on automating the process using reinforcement learning (RL). Most of the RL systems contain different kinds of graph embedding modules.Our work tries to increase the knowledge about how to tackle the device placement problem by measuring and assessing the impact of graph embed- dings on the quality of the device placement policies. We compare the dif- ferent approaches in two main ways: runtime improvement and computation time. The former is a metric of how much faster is the new placement policy compared to a baseline. The latter describes how much time is required by the system to achieve that runtime improvement. In this thesis, we build on previous efforts in the device placement field in order to explore how different state-of-the-art graph embedding architectures affect device placement poli- cies. The graph embedding architectures we compare are Placeto (used as a baseline), GraphSAGE and P-GNN.In terms of runtime improvement, we achieve an increase of 23.967% when using P-GNN compared to Placeto, and 31.164% absolute improvement from the initial placement. GraphSAGE produces 1.165% better results than Placeto with the same setup. Regarding computation time, GraphSAGE has a gain of 11.560% compared to Placeto, whereas P-GNN is 6.950% slower than the baseline.Given our results, we can confirm that graph embedding architecture can have a significant impact on device placement policies and their performance. More complex graph embedding architectures that capture more data about the graph and its topology provide better runtime improvements. However, that complexity may come at the cost of the computation time required to train the placement system.<br>Moderna neurala nätverk (NN) -modeller kräver mer data och parametrar för att utföra allt mer komplexa uppgifter. När en NN-modell blir för stor för att rymmas på en dator, kan den behöva distribueras över flera datorer. Vilka vil- kor som ska användas vid distributionen av en NN-modell, och mer konkret hur olika delar av modellen ska spridas över olika datorer kallas enhetsplats- problemet. Avhandlingen kommer att fokusera på detta problem. Tidigare till- vägagångssätt har krävt att placeringspolicyn skapas manuellt av människor med expertis i detta område. Eftersom den metoden inte går att skala upp fo- kuserar man på att hantera enhetsplaceringsproblemet genom att automatisera processen med reinforcement learning (RL). De flesta av RL-systemen inne- håller olika typer av grafinbäddningsmoduler.I arbetet försöker vi öka kunskapen om hur man hanterar problem med enhetsplacering genom att mäta och bedöma effekterna av grafinbäddningar på kvaliteten på villkoren för enhetsplacering. Vi jämför de olika metoderna på två sätt: runtime improvement and computation time. Den förstnämnda är ett värde för hur mycket snabbare den nya placeringspolicyn är i jämförelse med en baslinje. Det andra beskriver hur mycket tid som krävs av systemet för att uppnå den förbättrade runtime.Den här avhandlingen bygger på tidigare forskning inom området av enhetsplacering för att undersöka hur olika topp- moderna metoder till enhetsplaceringsprinciper. Grafinbäddningsarkitekturer som vi jämför i avhandlignen är Placeto (används som en baslinje), Graph- SAGE och P-GNN.Vi uppnår en förbättring av runtime med en ökning på 23.967% när vi använder P-GNN jämfört med Placeto och 31.164% ökning från baslinjen. GraphSAGE ger 1.165% bättre resultat än Placeto med samma installation. När det gäller beräkningstiden har GraphSAGE en förbättring på 11.560% jämfört med Placeto, medan P-GNN är 6.950% långsammare än baslinjen.Med resultaten kan vi bekräfta att grafinbäddningsarkitektur kan ha en be- tydande inverkan på enhetsplaceringsprinciper och deras prestanda. Desto mer invecklad grafinbäddningsarkitekturer fångar mer data om grafen och dess to- pologi ger runtime improvment. Däremot blir kan komplexiteten kosta i com- putation time på grund av det tid som krävs för att utbilda placeringssystemet.

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Behzadi, Lila. "An improved spring-based graph embedding algorithm and LayoutShow, a Java environment for graph drawing." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq43368.pdf.

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Tahraoui, Mohammed Amin. "Coloring, packing and embedding of graphs." Phd thesis, Université Claude Bernard - Lyon I, 2012. http://tel.archives-ouvertes.fr/tel-00995041.

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In this thesis, we investigate some problems in graph theory, namelythe graph coloring problem, the graph packing problem and tree pattern matchingfor XML query processing. The common point between these problems is that theyuse labeled graphs.In the first part, we study a new coloring parameter of graphs called the gapvertex-distinguishing edge coloring. It consists in an edge-coloring of a graph G whichinduces a vertex distinguishing labeling of G such that the label of each vertex isgiven by the difference between the highest and the lowest colors of its adjacentedges. The minimum number of colors required for a gap vertex-distinguishing edgecoloring of G is called the gap chromatic number of G and is denoted by gap(G).We will compute this parameter for a large set of graphs G of order n and we evenprove that gap(G) 2 fn E 1; n; n + 1g.In the second part, we focus on graph packing problems, which is an area ofgraph theory that has grown significantly over the past several years. However, themajority of existing works focuses on unlabeled graphs. In this thesis, we introducefor the first time the packing problem for a vertex labeled graph. Roughly speaking,it consists of graph packing which preserves the labels of the vertices. We studythe corresponding optimization parameter on several classes of graphs, as well asfinding general bounds and characterizations.The last part deal with the query processing of a core subset of XML query languages:XML twig queries. An XML twig query, represented as a small query tree,is essentially a complex selection on the structure of an XML document. Matching atwig query means finding all the occurrences of the query tree embedded in the XMLdata tree. Many holistic twig join algorithms have been proposed to match XMLtwig pattern. Most of these algorithms find twig pattern matching in two steps. Inthe first one, a query tree is decomposed into smaller pieces, and solutions againstthese pieces are found. In the second step, all of these partial solutions are joinedtogether to generate the final solutions. In this part, we propose a novel holistictwig join algorithm, called TwigStack++, which features two main improvementsin the decomposition and matching phase. The proposed solutions are shown to beefficient and scalable, and should be helpful for the future research on efficient queryprocessing in a large XML database.

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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.

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Книги з теми "Embedding de graph"

Fu, Yun, and Yunqian Ma, eds. Graph Embedding for Pattern Analysis. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4457-2.

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Shaw, Blake. Graph Embedding and Nonlinear Dimensionality Reduction. [publisher not identified], 2011.

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Guattery, Stephen. Graph embedding techniques for bounding condition numbers of incomplete factor preconditioners. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1997.

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Center, Langley Research, ed. Graph embedding techniques for bounding condition numbers of incomplete factor preconditioners. National Aeronautics and Space Administration, Langley Research Center, 1997.

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Riesen, Kaspar. Graph classification and clustering based on vector space embedding. World Scientific, 2010.

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Yanpei, Liu. Embeddability in graphs. Science Press, 1995.

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L, Miller Gary, and Langley Research Center, eds. Graph embeddings and Laplacian eigenvalues. National Aeronautics and Space Administration, Langley Research Center, 1998.

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L, Miller Gary, and Langley Research Center, eds. Graph embeddings and Laplacian eigenvalues. National Aeronautics and Space Administration, Langley Research Center, 1998.

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Paulheim, Heiko, Petar Ristoski, and Jan Portisch. Embedding Knowledge Graphs with RDF2vec. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30387-6.

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Institute for Computer Applications in Science and Engineering., ed. Graph embeddings, symmetric real matrices, and generalized inverses. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1998.

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Частини книг з теми "Embedding de graph"

Goyal, Palash. "Graph Embedding." In Machine Learning for Data Science Handbook. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24628-9_15.

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Rokka Chhetri, Sujit, and Mohammad Abdullah Al Faruque. "Dynamic Graph Embedding." In Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37962-9_10.

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Krause, Franz, Kabul Kurniawan, Elmar Kiesling, et al. "Leveraging Semantic Representations via Knowledge Graph Embeddings." In Artificial Intelligence in Manufacturing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_5.

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AbstractThe representation and exploitation of semantics has been gaining popularity in recent research, as exemplified by the uptake of large language models in the field of Natural Language Processing (NLP) and knowledge graphs (KGs) in the Semantic Web. Although KGs are already employed in manufacturing to integrate and standardize domain knowledge, the generation and application of corresponding KG embeddings as lean feature representations of graph elements have yet to be extensively explored in this domain. Existing KGs in manufacturing often focus on top-level domain knowledge and thus ignore domain dynamics, or they lack interconnectedness, i.e., nodes primarily represent non-contextual data values with single adjacent edges, such as sensor measurements. Consequently, context-dependent KG embedding algorithms are either restricted to non-dynamic use cases or cannot be applied at all due to the given KG characteristics. Therefore, this work provides an overview of state-of-the-art KG embedding methods and their functionalities, identifying the lack of dynamic embedding formalisms and application scenarios as the key obstacles that hinder their implementation in manufacturing. Accordingly, we introduce an approach for dynamizing existing KG embeddings based on local embedding reconstructions. Furthermore, we address the utilization of KG embeddings in the Horizon2020 project Teaming.AI (www.teamingai-project.eu.) focusing on their respective benefits.

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Estrella-Balderrama, Alejandro, J. Joseph Fowler, and Stephen G. Kobourov. "Graph Simultaneous Embedding Tool, GraphSET." In Graph Drawing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00219-9_17.

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Auer, Christopher, Christian Bachmaier, Franz Josef Brandenburg, and Andreas Gleißner. "Classification of Planar Upward Embedding." In Graph Drawing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25878-7_39.

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Jouili, Salim, and Salvatore Tabbone. "Graph Embedding Using Constant Shift Embedding." In Recognizing Patterns in Signals, Speech, Images and Videos. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17711-8_9.

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Harel, David, and Yehuda Koren. "Graph Drawing by High-Dimensional Embedding." In Graph Drawing. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36151-0_20.

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Katz, Bastian, Marcus Krug, Ignaz Rutter, and Alexander Wolff. "Manhattan-Geodesic Embedding of Planar Graphs." In Graph Drawing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11805-0_21.

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Di Giacomo, Emilio, Fabrizio Frati, Radoslav Fulek, Luca Grilli, and Marcus Krug. "Orthogeodesic Point-Set Embedding of Trees." In Graph Drawing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25878-7_6.

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Dornheim, Christoph. "Graph Embedding with Topological Cycle-Constraints." In Graph Drawing. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46648-7_16.

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Тези доповідей конференцій з теми "Embedding de graph"

Giri, Pulak Ranjan, Mori Kurokawa, and Kazuhiro Saito. "Fast Variational Knowledge Graph Embedding." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.10318.

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Bai, Yunsheng, Hao Ding, Yang Qiao, et al. "Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/275.

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We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGraphEmb, is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised and inductive manner. The learned neural network can be considered as a function that receives any graph as input, either seen or unseen in the training set, and transforms it into an embedding. A novel graph-level embedding generation mechanism called Multi-Scale Node Attention (MSNA), is proposed. Experiments on five real graph datasets show that UGraphEmb achieves competitive accuracy in the tasks of graph classification, similarity ranking, and graph visualization.

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Luo, Gongxu, Jianxin Li, Hao Peng, et al. "Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. 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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Pan, Shirui, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. "Adversarially Regularized Graph Autoencoder for Graph Embedding." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/362.

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Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in real-world graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding, two variants of adversarial approaches, adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction, graph clustering, and graph visualization tasks.

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Rahman, Tahleen, Bartlomiej Surma, Michael Backes, and Yang Zhang. "Fairwalk: Towards Fair Graph Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/456.

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Graph embeddings have gained huge popularity in the recent years as a powerful tool to analyze social networks. However, no prior works have studied potential bias issues inherent within graph embedding. In this paper, we make a first attempt in this direction. In particular, we concentrate on the fairness of node2vec, a popular graph embedding method. Our analyses on two real-world datasets demonstrate the existence of bias in node2vec when used for friendship recommendation. We, therefore, propose a fairness-aware embedding method, namely Fairwalk, which extends node2vec. Experimental results demonstrate that Fairwalk reduces bias under multiple fairness metrics while still preserving the utility.

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Sun, Zequn, Wei Hu, Qingheng Zhang, and Yuzhong Qu. "Bootstrapping Entity Alignment with Knowledge Graph Embedding." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/611.

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Embedding-based entity alignment represents different knowledge graphs (KGs) as low-dimensional embeddings and finds entity alignment by measuring the similarities between entity embeddings. Existing approaches have achieved promising results, however, they are still challenged by the lack of enough prior alignment as labeled training data. In this paper, we propose a bootstrapping approach to embedding-based entity alignment. It iteratively labels likely entity alignment as training data for learning alignment-oriented KG embeddings. Furthermore, it employs an alignment editing method to reduce error accumulation during iterations. Our experiments on real-world datasets showed that the proposed approach significantly outperformed the state-of-the-art embedding-based ones for entity alignment. The proposed alignment-oriented KG embedding, bootstrapping process and alignment editing method all contributed to the performance improvement.

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Wan, Hai, Yonghao Luo, Bo Peng, and Wei-Shi Zheng. "Representation Learning for Scene Graph Completion via Jointly Structural and Visual Embedding." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/132.

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This paper focuses on scene graph completion which aims at predicting new relations between two entities utilizing existing scene graphs and images. By comparing with the well-known knowledge graph, we first identify that each scene graph is associated with an image and each entity of a visual triple in a scene graph is composed of its entity type with attributes and grounded with a bounding box in its corresponding image. We then propose an end-to-end model named Representation Learning via Jointly Structural and Visual Embedding (RLSV) to take advantages of structural and visual information in scene graphs. In RLSV model, we provide a fully-convolutional module to extract the visual embeddings of a visual triple and apply hierarchical projection to combine the structural and visual embeddings of a visual triple. In experiments, we evaluate our model on two scene graph completion tasks: link prediction and visual triple classification, and further analyze by case studies. Experimental results demonstrate that our model outperforms all baselines in both tasks, which justifies the significance of combining structural and visual information for scene graph completion.

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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}. 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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Zhang, Hengtong, Tianhang Zheng, Jing Gao, et al. "Data Poisoning Attack against Knowledge Graph Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/674.

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Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its effectiveness in a benign environment, KGE's robustness to adversarial attacks is not well-studied. Existing attack methods on graph data cannot be directly applied to attack the embeddings of knowledge graph due to its heterogeneity. To fill this gap, we propose a collection of data poisoning attack strategies, which can effectively manipulate the plausibility of arbitrary targeted facts in a knowledge graph by adding or deleting facts on the graph. The effectiveness and efficiency of the proposed attack strategies are verified by extensive evaluations on two widely-used benchmarks.

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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}. 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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