Graph embedding techniques, applications, and performance: A survey
Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform...
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| Vydáno v: | Knowledge-based systems Ročník 151; s. 78 - 94 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.07.2018
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform the analysis. Recently, methods which use the representation of graph nodes in vector space have gained traction from the research community. In this survey, we provide a comprehensive and structured analysis of various graph embedding techniques proposed in the literature. We first introduce the embedding task and its challenges such as scalability, choice of dimensionality, and features to be preserved, and their possible solutions. We then present three categories of approaches based on factorization methods, random walks, and deep learning, with examples of representative algorithms in each category and analysis of their performance on various tasks. We evaluate these state-of-the-art methods on a few common datasets and compare their performance against one another. Our analysis concludes by suggesting some potential applications and future directions. We finally present the open-source Python library we developed, named GEM (Graph Embedding Methods, available at https://github.com/palash1992/GEM), which provides all presented algorithms within a unified interface to foster and facilitate research on the topic. |
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| AbstractList | Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform the analysis. Recently, methods which use the representation of graph nodes in vector space have gained traction from the research community. In this survey, we provide a comprehensive and structured analysis of various graph embedding techniques proposed in the literature. We first introduce the embedding task and its challenges such as scalability, choice of dimensionality, and features to be preserved, and their possible solutions. We then present three categories of approaches based on factorization methods, random walks, and deep learning, with examples of representative algorithms in each category and analysis of their performance on various tasks. We evaluate these state-of-the-art methods on a few common datasets and compare their performance against one another. Our analysis concludes by suggesting some potential applications and future directions. We finally present the open-source Python library we developed, named GEM (Graph Embedding Methods, available at https://github.com/palash1992/GEM), which provides all presented algorithms within a unified interface to foster and facilitate research on the topic. |
| Author | Goyal, Palash Ferrara, Emilio |
| Author_xml | – sequence: 1 givenname: Palash orcidid: 0000-0003-2455-2160 surname: Goyal fullname: Goyal, Palash email: palashgo@usc.edu – sequence: 2 givenname: Emilio surname: Ferrara fullname: Ferrara, Emilio |
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| ContentType | Journal Article |
| Copyright | 2018 Elsevier B.V. |
| Copyright_xml | – notice: 2018 Elsevier B.V. |
| DOI | 10.1016/j.knosys.2018.03.022 |
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| Discipline | Computer Science |
| EISSN | 1872-7409 |
| EndPage | 94 |
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| Keywords | Python graph embedding methods GEM library Graph embedding applications Graph embedding techniques |
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| References | Zachary (bib0091) 1977; 33 W.L. Hamilton, R. Ying, J. Leskovec, Inductive representation learning on large graphs, arXiv Rissanen (bib0073) 1978; 14 J. Leskovec, A. Krevl, SNAP datasets: Stanford large network dataset collection, 2014 Tang, Liu (bib0093) 2009 Wang, Wong (bib0090) 1987; 82 T.N. Kipf, M. Welling, Variational graph auto-encoders, arXiv Friedman, Getoor, Koller, Pfeffer (bib0017) 1999 Lin, Liu, Chen (bib0043) 2005 White, Boorman, Breiger (bib0016) 1976; 81 Zhang, Yin, Zhu, Zhang (bib0047) 2016 Jaccard (bib0013) 1901 Neville, Jensen (bib0087) 2000 Fouss, Pirotte, Renders, Saerens (bib0050) 2007; 19 Van Loan (bib0034) 1976; 13 McCallum, Nigam (bib0089) 1998; 752 Tenenbaum, De Silva, Langford (bib0038) 2000; 290 Freeman (bib0002) 2000; 1 Azran (bib0009) 2007 Huang, Li, Hu (bib0048) 2017 (2013). Martínez, Kak (bib0037) 2001; 23 Gehrke, Ginsparg, Kleinberg (bib0094) 2003; 5 . Lu, Getoor (bib0012) 2003; 3 (2017). Kruskal, Wish (bib0039) 1978; 11 Pan, Wu, Zhu, Zhang, Wang (bib0055) 2016; 11 Bhagat, Cormode, Muthukrishnan (bib0006) 2011 Duvenaud, Maclaurin, Iparraguirre, Bombarell, Hirzel, Aspuru-Guzik, Adams (bib0061) 2015 Wang, Cui, Zhu (bib0023) 2016 H. Dai, Y. Wang, R. Trivedi, L. Song, Deep coevolutionary network: embedding user and item features for recommendation (2017). Ou, Cui, Pei, Zhang, Zhu (bib0024) 2016 Di Battista, Eades, Tamassia, Tollis (bib0076) 1994; 4 Leskovec, Kleinberg, Faloutsos (bib0004) 2007; 1 Belkin, Niyogi (bib0025) 2001; 14 Hornik, Stinchcombe, White (bib0067) 1990; 3 W.L. Hamilton, R. Ying, J. Leskovec, Representation learning on graphs: methods and applications, arXiv preprint arXiv Li, Zhu, Zhang (bib0054) 2016 i Cancho, Solé (bib0003) 2001; 268 Martínez, Kak (bib0042) 2008; 23 Wright, Ma, Mairal, Sapiro, Huang, Yan (bib0102) 2010; 98 Clauset, Moore, Newman (bib0015) 2008; 453 Niepert, Ahmed, Kutzkov (bib0057) 2016 Herman, Melançon, Marshall (bib0078) 2000; 6 (2016). Feder, Motwani (bib0068) 1991 Bengio, Courville, Vincent (bib0058) 2013; 35 Tang, Qu, Wang, Zhang, Yan, Mei (bib0022) 2015 Chang, Han, Tang, Qi, Aggarwal, Huang (bib0045) 2015 He, Niyogi (bib0040) 2004 Bunke, Riesen (bib0103) 2011; 44 Tian, Hankins, Patel (bib0070) 2008 Yang, Tang, Cohen (bib0053) 2016 Katz (bib0085) 1953; 18 Hosmer Jr, Lemeshow, Sturdivant (bib0088) 2013; 398 Eades, Xuemin (bib0077) 1989 J. Bruna, W. Zaremba, A. Szlam, Y. LeCun, Spectral networks and locally connected networks on graphs, arXiv Riesen, Neuhaus, Bunke (bib0101) 2007 Maaten, Hinton (bib0008) 2008; 9 T.N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, arXiv B. Perozzi, V. Kulkarni, S. Skiena, Walklets: multiscale graph embeddings for interpretable network classification, arXiv Shaw, Jebara (bib0033) 2009 Z. Yang, W.W. Cohen, R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, arXiv H. Chen, B. Perozzi, Y. Hu, S. Skiena, Harp: hierarchical representation learning for networks, arXiv Defferrard, Bresson, Vandergheynst (bib0063) 2016 M. Henaff, J. Bruna, Y. LeCun, Deep convolutional networks on graph-structured data, arXiv Navlakha, Rastogi, Shrivastava (bib0072) 2008 (2015). Shi, Malik (bib0020) 2000; 22 Liben-Nowell, Kleinberg (bib0005) 2007; 58 Cao, Lu, Xu (bib0030) 2016 Yu, Chu, Yu, Tresp, Xu (bib0086) 2006 Brand (bib0041) 2003 Jungnickel, Schade (bib0074) 2005 P. Goyal, N. Kamra, X. He, Y. Liu, Dyngem: deep embedding method for dynamic graphs. Zhu, Guo, Yin, Ver Steeg, Galstyan (bib0099) 2016; 28 Baluja, Seth, Sivakumar, Jing, Yagnik, Kumar, Ravichandran, Aly (bib0010) 2008 Al Hasan, Zaki (bib0084) 2011 Yan, Xu, Zhang, Zhang, Yang, Lin (bib0035) 2007; 29 Roweis, Saul (bib0026) 2000; 290 Yang, Liu, Zhao, Sun, Chang (bib0044) 2015 Lü, Zhou (bib0083) 2011; 390 Tang, Liu (bib0092) 2009 Newman (bib0049) 2005; 27 Perozzi, Al-Rfou, Skiena (bib0028) 2014 Ding, He, Zha, Gu, Simon (bib0007) 2001 Heckerman, Meek, Koller (bib0018) 2007 Toivonen, Zhou, Hartikainen, Hinkka (bib0071) 2011 Pearson (bib0079) 1901; 2 H. Cai, V.W. Zheng, K.C.-C. Chang, A comprehensive survey of graph embedding: problems, techniques and applications, arXiv preprint arXiv Tu, Zhang, Liu, Sun (bib0046) 2016 Theocharidis, Van Dongen, Enright, Freeman (bib0001) 2009; 4 Breitkreutz, Stark, Reguly, Boucher, Breitkreutz, Livstone, Oughtred, Lackner, Bähler, Wood (bib0096) 2008; 36 Zhou, Cheng, Yu (bib0019) 2009; 2 Jolliffe (bib0036) 1986 Adamic, Adar (bib0014) 2003; 25 Luo, Nie, Huang, Ding (bib0032) 2011 Grover, Leskovec (bib0029) 2016 Newman, Girvan (bib0080) 2004; 69 White, Smyth (bib0082) 2005 Y. Li, D. Tarlow, M. Brockschmidt, R. Zemel, Gated graph sequence neural networks, arXiv Xu, Yuruk, Feng, Schweiger (bib0081) 2007 Pardalos, Xue (bib0069) 1994; 4 Holland, Laskey, Leinhardt (bib0100) 1983; 5 Ahmed, Shervashidze, Narayanamurthy, Josifovski, Smola (bib0021) 2013 D.P. Kingma, M. Welling, Auto-encoding variational bayes, arXiv Gansner, North (bib0075) 2000; 30 Cao, Lu, Xu (bib0027) 2015 Bhagat, Rozenbaum, Cormode (bib0011) 2007 |
| References_xml | – reference: H. Cai, V.W. Zheng, K.C.-C. Chang, A comprehensive survey of graph embedding: problems, techniques and applications, arXiv preprint arXiv: – start-page: 201 year: 2007 end-page: 238 ident: bib0018 article-title: Probabilistic entity-relationship models, prms, and plate models publication-title: Intro. Stat. Relational Learn. – start-page: 1225 year: 2016 end-page: 1234 ident: bib0023 article-title: Structural deep network embedding publication-title: Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining – reference: ). – start-page: 547 year: 2003 end-page: 554 ident: bib0041 article-title: Continuous nonlinear dimensionality reduction by kernel eigenmaps publication-title: IJCAI – start-page: 383 year: 2007 end-page: 393 ident: bib0101 article-title: Graph embedding in vector spaces by means of prototype selection publication-title: International Workshop on Graph-Based Representations in Pattern Recognition – start-page: 1300 year: 1999 end-page: 1309 ident: bib0017 article-title: Learning probabilistic relational models publication-title: IJCAI – volume: 25 start-page: 211 year: 2003 end-page: 230 ident: bib0014 article-title: Friends and neighbors on the web publication-title: Soc. Netw. – start-page: 2224 year: 2015 end-page: 2232 ident: bib0061 article-title: Convolutional networks on graphs for learning molecular fingerprints publication-title: Advances in neural information processing systems – start-page: 817 year: 2009 end-page: 826 ident: bib0092 article-title: Relational learning via latent social dimensions publication-title: Proceedings of the 15th international conference on Knowledge discovery and data mining – start-page: 1553 year: 2006 end-page: 1560 ident: bib0086 article-title: Stochastic relational models for discriminative link prediction publication-title: NIPS – start-page: 37 year: 2013 end-page: 48 ident: bib0021 article-title: Distributed large-scale natural graph factorization publication-title: Proceedings of the 22nd international conference on World Wide Web – start-page: 965 year: 2011 end-page: 973 ident: bib0071 article-title: Compression of weighted graphs publication-title: Proc. 17th international conference on Knowledge discovery and data mining – volume: 58 start-page: 1019 year: 2007 end-page: 1031 ident: bib0005 article-title: The link-prediction problem for social networks publication-title: J. Assoc. Inf. Sci. Technol. – reference: T.N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, arXiv: – volume: 398 year: 2013 ident: bib0088 article-title: Applied logistic regression – volume: 2 start-page: 559 year: 1901 end-page: 572 ident: bib0079 article-title: Liii. on lines and planes of closest fit to systems of points in space publication-title: Lond., Edinburgh, Dublin Philos. Mag. J. Sci. – start-page: 249 year: 2005 end-page: 258 ident: bib0043 article-title: Semantic manifold learning for image retrieval publication-title: Proceedings of the 13th annual ACM international conference on Multimedia – start-page: 895 year: 2008 end-page: 904 ident: bib0010 article-title: Video suggestion and discovery for youtube: taking random walks through the view graph publication-title: Proc. 17th int. conference on World Wide Web – year: 2016 ident: bib0057 article-title: Learning convolutional neural networks for graphs publication-title: Proceedings of the 33rd annual international conference on machine learning. ACM – volume: 5 start-page: 109 year: 1983 end-page: 137 ident: bib0100 article-title: Stochastic blockmodels: first steps publication-title: Soc. Netw. – volume: 3 start-page: 496 year: 2003 end-page: 503 ident: bib0012 article-title: Link-based classification publication-title: ICML – reference: (2017). – volume: 2 start-page: 718 year: 2009 end-page: 729 ident: bib0019 article-title: Graph clustering based on structural/attribute similarities publication-title: Proc. VLDB Endow. – start-page: 274 year: 2005 end-page: 285 ident: bib0082 article-title: A spectral clustering approach to finding communities in graphs publication-title: Proceedings of the 2005 SIAM international conference on data mining – volume: 290 start-page: 2323 year: 2000 end-page: 2326 ident: bib0026 article-title: Nonlinear dimensionality reduction by locally linear embedding publication-title: Science – reference: Y. Li, D. Tarlow, M. Brockschmidt, R. Zemel, Gated graph sequence neural networks, arXiv: – year: 1901 ident: bib0013 article-title: Etude comparative de la distribution florale dans une portion des Alpes et du Jura – reference: H. Dai, Y. Wang, R. Trivedi, L. Song, Deep coevolutionary network: embedding user and item features for recommendation (2017). – reference: (2017). – start-page: 855 year: 2016 end-page: 864 ident: bib0029 article-title: node2vec: scalable feature learning for networks publication-title: Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining – volume: 290 start-page: 2319 year: 2000 end-page: 2323 ident: bib0038 article-title: A global geometric framework for nonlinear dimensionality reduction publication-title: Science – volume: 453 start-page: 98 year: 2008 end-page: 101 ident: bib0015 article-title: Hierarchical structure and the prediction of missing links in networks publication-title: Nature – start-page: 243 year: 2011 end-page: 275 ident: bib0084 article-title: A survey of link prediction in social networks publication-title: Social network data analytics – volume: 11 start-page: 12 year: 2016 ident: bib0055 article-title: Tri-party deep network representation publication-title: Network – start-page: 115 year: 2011 end-page: 148 ident: bib0006 article-title: Node classification in social networks publication-title: Social network data analytics – volume: 11 year: 1978 ident: bib0039 article-title: Multidimensional scaling – reference: W.L. Hamilton, R. Ying, J. Leskovec, Representation learning on graphs: methods and applications, arXiv preprint arXiv: – volume: 5 year: 2003 ident: bib0094 article-title: Overview of the 2003 kdd cup publication-title: ACM SIGKDD Expl. – reference: T.N. Kipf, M. Welling, Variational graph auto-encoders, arXiv: – volume: 30 start-page: 1203 year: 2000 end-page: 1233 ident: bib0075 article-title: An open graph visualization system and its applications to software engineering publication-title: Softw. Pract. Exp. – start-page: 13 year: 2000 end-page: 20 ident: bib0087 article-title: Iterative classification in relational data publication-title: Proc. Workshop on Learning Statistical Models from Relational Data – volume: 28 start-page: 2765 year: 2016 end-page: 2777 ident: bib0099 article-title: Scalable temporal latent space inference for link prediction in dynamic social networks publication-title: IEEE Trans. Knowl. Data Eng. – volume: 9 start-page: 2579 year: 2008 end-page: 2605 ident: bib0008 article-title: Visualizing data using t-sne publication-title: J. Mach. Learn. Res. – start-page: 2111 year: 2015 end-page: 2117 ident: bib0044 article-title: Network representation learning with rich text information. publication-title: IJCAI – start-page: 119 year: 2015 end-page: 128 ident: bib0045 article-title: Heterogeneous network embedding via deep architectures publication-title: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – volume: 6 start-page: 24 year: 2000 end-page: 43 ident: bib0078 article-title: Graph visualization and navigation in information visualization: a survey publication-title: IEEE Trans. Visual. Comput. Graph. – start-page: 107 year: 2001 end-page: 114 ident: bib0007 article-title: A min-max cut algorithm for graph partitioning and data clustering publication-title: International Conference on Data Mining – start-page: 1067 year: 2015 end-page: 1077 ident: bib0022 article-title: Line: large-scale information network embedding publication-title: Proceedings 24th International Conference on World Wide Web – start-page: 13 year: 1989 end-page: 17 ident: bib0077 article-title: How to draw a directed graph publication-title: Visual Languages, 1989., IEEE Workshop on – start-page: 2287 year: 2016 end-page: 2293 ident: bib0053 article-title: Multi-modal bayesian embeddings for learning social knowledge graphs. publication-title: IJCAI – volume: 3 start-page: 551 year: 1990 end-page: 560 ident: bib0067 article-title: Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks publication-title: Neural Netw. – volume: 19 year: 2007 ident: bib0050 article-title: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation publication-title: IEEE Trans. Knowl. Data Eng. – start-page: 567 year: 2008 end-page: 580 ident: bib0070 article-title: Efficient aggregation for graph summarization publication-title: Proceedings of the SIGMOD international conference on Management of data – start-page: 3889 year: 2016 end-page: 3895 ident: bib0046 article-title: Max-margin deepwalk: discriminative learning of network representation. publication-title: IJCAI – start-page: 824 year: 2007 end-page: 833 ident: bib0081 article-title: Scan: a structural clustering algorithm for networks publication-title: Proceedings 13th international conference on Knowledge discovery and data mining – volume: 4 start-page: 301 year: 1994 end-page: 328 ident: bib0069 article-title: The maximum clique problem publication-title: J. Global Optim. – volume: 4 start-page: 235 year: 1994 end-page: 282 ident: bib0076 article-title: Algorithms for drawing graphs: an annotated bibliography publication-title: Comput. Geom. – start-page: 937 year: 2009 end-page: 944 ident: bib0033 article-title: Structure preserving embedding publication-title: Proceedings of the 26th Annual International Conference on Machine Learning – start-page: 123 year: 1991 end-page: 133 ident: bib0068 article-title: Clique partitions, graph compression and speeding-up algorithms publication-title: Proceedings of the twenty-third annual ACM symposium on Theory of computing – volume: 390 start-page: 1150 year: 2011 end-page: 1170 ident: bib0083 article-title: Link prediction in complex networks: a survey publication-title: Physica A – volume: 29 start-page: 40 year: 2007 end-page: 51 ident: bib0035 article-title: Graph embedding and extensions: a general framework for dimensionality reduction publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: (2016). – volume: 81 start-page: 730 year: 1976 end-page: 780 ident: bib0016 article-title: Social structure from multiple networks. I. Blockmodels of roles and positions publication-title: Am. J. Sociol. – volume: 23 start-page: 228 year: 2001 end-page: 233 ident: bib0037 article-title: Pca versus lda publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 44 start-page: 1057 year: 2011 end-page: 1067 ident: bib0103 article-title: Recent advances in graph-based pattern recognition with applications in document analysis publication-title: Pattern Recognition. – start-page: 49 year: 2007 end-page: 56 ident: bib0009 article-title: The rendezvous algorithm: Multiclass semi-supervised learning with markov random walks publication-title: Proceedings of the 24th international conference on Machine learning – volume: 23 start-page: 1 year: 2008 end-page: 8 ident: bib0042 article-title: Non-negative graph embedding publication-title: IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) – reference: W.L. Hamilton, R. Ying, J. Leskovec, Inductive representation learning on large graphs, arXiv: – volume: 33 start-page: 452 year: 1977 end-page: 473 ident: bib0091 article-title: An information flow model for conflict and fission in small groups publication-title: J. Anthropol. Res. – volume: 1 start-page: 2 year: 2007 ident: bib0004 article-title: Graph evolution: densification and shrinking diameters publication-title: ACM Trans. Knowl. Disc. Data (TKDD) – start-page: 609 year: 2016 end-page: 618 ident: bib0047 article-title: Homophily, structure, and content augmented network representation learning publication-title: Data Mining (ICDM), 2016 IEEE 16th International Conference on – volume: 69 start-page: 026113 year: 2004 ident: bib0080 article-title: Finding and evaluating community structure in networks publication-title: Phys. Rev. E – volume: 14 start-page: 585 year: 2001 end-page: 591 ident: bib0025 article-title: Laplacian eigenmaps and spectral techniques for embedding and clustering publication-title: NIPS – start-page: 419 year: 2008 end-page: 432 ident: bib0072 article-title: Graph summarization with bounded error publication-title: Proceedings of the international conference on Management of data – start-page: 1107 year: 2009 end-page: 1116 ident: bib0093 article-title: Scalable learning of collective behavior based on sparse social dimensions publication-title: Proceedings of the 18th ACM conference on Information and knowledge management – start-page: 701 year: 2014 end-page: 710 ident: bib0028 article-title: Deepwalk: online learning of social representations publication-title: Proceedings 20th international conference on Knowledge discovery and data mining – volume: 13 start-page: 76 year: 1976 end-page: 83 ident: bib0034 article-title: Generalizing the singular value decomposition publication-title: SIAM J. Numer. Anal. – volume: 27 start-page: 39 year: 2005 end-page: 54 ident: bib0049 article-title: A measure of betweenness centrality based on random walks publication-title: Soc. Netw. – start-page: 92 year: 2007 end-page: 101 ident: bib0011 article-title: Applying link-based classification to label blogs publication-title: Proceedings of WebKDD: workshop on Web mining and social network analysis – volume: 14 start-page: 465 year: 1978 end-page: 471 ident: bib0073 article-title: Modeling by shortest data description publication-title: Automatica – volume: 1 start-page: 4 year: 2000 ident: bib0002 article-title: Visualizing social networks publication-title: J. Social Struct. – reference: B. Perozzi, V. Kulkarni, S. Skiena, Walklets: multiscale graph embeddings for interpretable network classification, arXiv: – year: 2005 ident: bib0074 article-title: Graphs, networks and algorithms – volume: 268 start-page: 2261 year: 2001 end-page: 2265 ident: bib0003 article-title: The small world of human language publication-title: Proc. R. Soc. Lond. B – year: 2016 ident: bib0054 article-title: Discriminative deep random walk for network classification. publication-title: ACL (1) – volume: 4 start-page: 1535 year: 2009 end-page: 1550 ident: bib0001 article-title: Network visualization and analysis of gene expression data using biolayout express3d publication-title: Nat. Protoc. – start-page: 115 year: 1986 end-page: 128 ident: bib0036 article-title: Principal component analysis and factor analysis publication-title: Principal component analysis – reference: H. Chen, B. Perozzi, Y. Hu, S. Skiena, Harp: hierarchical representation learning for networks, arXiv: – start-page: 3844 year: 2016 end-page: 3852 ident: bib0063 article-title: Convolutional neural networks on graphs with fast localized spectral filtering publication-title: Advances in Neural Information Processing Systems – volume: 35 start-page: 1798 year: 2013 end-page: 1828 ident: bib0058 article-title: Representation learning: a review and new perspectives publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 18 start-page: 39 year: 1953 end-page: 43 ident: bib0085 article-title: A new status index derived from sociometric analysis publication-title: Psychometrika – reference: (2015). – start-page: 731 year: 2017 end-page: 739 ident: bib0048 article-title: Label informed attributed network embedding publication-title: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining – reference: M. Henaff, J. Bruna, Y. LeCun, Deep convolutional networks on graph-structured data, arXiv: – volume: 22 start-page: 888 year: 2000 end-page: 905 ident: bib0020 article-title: Normalized cuts and image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: P. Goyal, N. Kamra, X. He, Y. Liu, Dyngem: deep embedding method for dynamic graphs. – volume: 82 start-page: 8 year: 1987 end-page: 19 ident: bib0090 article-title: Stochastic blockmodels for directed graphs publication-title: J. Am. Stat. Assoc. – start-page: 553 year: 2011 end-page: 560 ident: bib0032 article-title: Cauchy graph embedding publication-title: Proceedings of the 28th International Conference on Machine Learning (ICML-11) – reference: Z. Yang, W.W. Cohen, R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, arXiv: – reference: D.P. Kingma, M. Welling, Auto-encoding variational bayes, arXiv: – start-page: 153 year: 2004 end-page: 160 ident: bib0040 article-title: Locality preserving projections publication-title: Advances in neural information processing systems – reference: J. Leskovec, A. Krevl, SNAP datasets: Stanford large network dataset collection, 2014, ( – volume: 36 start-page: D637 year: 2008 end-page: D640 ident: bib0096 article-title: The biogrid interaction database: 2008 update publication-title: Nucleic Acids Res. – reference: J. Bruna, W. Zaremba, A. Szlam, Y. LeCun, Spectral networks and locally connected networks on graphs, arXiv: – reference: (2013). – start-page: 1105 year: 2016 end-page: 1114 ident: bib0024 article-title: Asymmetric transitivity preserving graph embedding publication-title: Proc. of ACM SIGKDD – volume: 752 start-page: 41 year: 1998 end-page: 48 ident: bib0089 article-title: A comparison of event models for naive bayes text classification publication-title: AAAI-98 workshop on learning for text categorization – start-page: 891 year: 2015 end-page: 900 ident: bib0027 article-title: Grarep: learning graph representations with global structural information publication-title: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management – start-page: 1145 year: 2016 end-page: 1152 ident: bib0030 article-title: Deep neural networks for learning graph representations publication-title: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence – volume: 98 start-page: 1031 year: 2010 end-page: 1044 ident: bib0102 article-title: Sparse representation for computer vision and pattern recognition publication-title: Proceedings of the IEEE |
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| SubjectTerms | Graph embedding applications Graph embedding techniques Python graph embedding methods GEM library |
| Title | Graph embedding techniques, applications, and performance: A survey |
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