Готові списки джерел за темами / Embedding de graph

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  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій

Добірка наукової літератури з теми "Embedding de graph"

Автор: Grafiati
Опубліковано: 22 лютого 2025
Оновлено: 31 липня 2025

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Статті в журналах з теми "Embedding de graph"

1

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 coarses
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2

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 embeddin
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3

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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Анотація:
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 th
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4

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
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5

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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Анотація:
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 cosin
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6

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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Анотація:
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 [Embeddi
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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 graph
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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 (
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9

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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Анотація:
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 smal
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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
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Дисертації з теми "Embedding de graph"

1

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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Анотація:
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
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2

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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Анотація:
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., l
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3

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-
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7

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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Анотація:
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 me
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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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Анотація:
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
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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"

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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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2

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

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3

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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5

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

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6

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

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7

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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8

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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9

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"

1

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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2

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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Анотація:
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
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4

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"

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Liang, Qiuyu, Weihua Wang, Cunda Wang, Feilong Bao, and Jie Yu. "Hyperbolic Multimodal Knowledge Graph Embedding." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887677.

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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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Zhang, Linlin, Yaliang Zhao, and Jinke Wang. "Structural Embedding Contrastive Graph Clustering." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00080.

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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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Анотація:
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-Sca
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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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Анотація:
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 grap
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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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Анотація:
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 representa
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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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Анотація:
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 result
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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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Анотація:
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 redu
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9

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
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10

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 di
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