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Understanding graph embedding methods and their applications. (English) Zbl 07421047

MSC:

68T07 Artificial neural networks and deep learning
05C62 Graph representations (geometric and intersection representations, etc.)
94A15 Information theory (general)
68T37 Reasoning under uncertainty in the context of artificial intelligence
68R10 Graph theory (including graph drawing) in computer science
68T30 Knowledge representation
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[1] H. Ashoor, X. Chen, W. Rosikiewicz, J. Wang, A. Cheng, P. Wang, Y. Ruan, and S. Li, Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data, Nat. Comm., 11 (2020), pp. 1-11.
[2] M. Belkin and P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in Proc. of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, NeurIPS, MIT Press, 2002, pp. 585-591.
[3] G. Bertasius, L. Torresani, S. X. Yu, and J. Shi, Convolutional random walk networks for semantic image segmentation, in Proc. of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017, pp. 6137-6145.
[4] A. Bojchevski and S. Günnemann, Deep Gaussian embedding of graphs: Unsupervised inductive learning via ranking, in Proc. of the 6th International Conference on Learning Representations (ICLR), 2018.
[5] K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, Freebase: A collaboratively created graph database for structuring human knowledge, in Proc. of the ACM SIGMOD International Conference on Management of Data, 2008, pp. 1247-1250.
[6] S. Brin and L. Page, The anatomy of a large-scale hypertextual Web search engine, in Proc. of the 7th International World-Wide Web Conference, 1998.
[7] C. B. Bruss, A. Khazane, J. Rider, R. Serpe, A. Gogoglou, and K. E. Hines, DeepTrax: Embedding Graphs of Financial Transactions, preprint, https://arxiv.org/abs/1907.07225, 2019.
[8] C. B. Bruss, A. Khazane, J. Rider, R. Serpe, S. Nagrecha, and K. E. Hines, Graph embeddings at scale, in Proc. of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2019, pp. 1-9.
[9] H. Cai, V. W. Zheng, and K. C.-C. Chang, A comprehensive survey of graph embedding: Problems, techniques, and applications, IEEE Trans. Knowl. Data Eng., 30 (2018), pp. 1616-1637.
[10] S. Cao, W. Lu, and Q. Xu, GraRep: Learning graph representations with global structural information, in Proc. of the 24th ACM International Conference on Information and Knowledge Management, 2015, pp. 891-900.
[11] I. Chami, A. Wolf, D.-C. Juan, F. Sala, S. Ravi, and C. Ré, Low-dimensional hyperbolic knowledge graph embeddings, in Proc. of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 6901-6914.
[12] P. Cui, X. Wang, J. Pei, and W. Zhu, A survey on network embedding, IEEE Trans. Knowl. Data Eng., 31 (2018), pp. 833-852.
[13] K. Ding, J. Li, R. Bhanushali, and H. Liu, Deep anomaly detection on attributed networks, in Proc. of the 2019 SIAM International Conference on Data Mining (SDM), SIAM, 2019, pp. 594-602, https://doi.org/10.1137/1.9781611975673.67.
[14] M. Fey and J. E. Lenssen, Fast graph representation learning with PyTorch Geometric, in ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.
[15] D. Filonik, T. Feng, K. Sun, R. Nock, A. Collins, and T. Bednarz, Non-Euclidean embeddings for graph analytics and visualisation, in SIGGRAPH Asia 2019, 2019, art. 47.
[16] P. Goyal, S. R. Chhetri, and A. Canedo, dyngraph2vec: Capturing network dynamics using dynamic graph representation learning, Knowledge-Based Syst., 187 (2020), art. 104816.
[17] P. Goyal, N. Kamra, X. He, and Y. Liu, DynGEM: Deep embedding method for dynamic graphs, in the 3rd International Workshop on Representation Learning for Graphs (ReLiG), IJCAI, 2017.
[18] A. Grover and J. Leskovec, node2vec: Scalable feature learning for networks, in Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 855-864.
[19] S. He, K. Liu, G. Ji, and J. Zhao, Learning to represent knowledge graphs with Gaussian embedding, in Proc. of the 24th ACM Internationaln Conference on Information and Knowledge Management, 2015, pp. 623-632.
[20] G. E. Hinton and S. T. Roweis, Stochastic neighbor embedding, in Proc. of the 15th International Conference on Neural Information Processing Systems, 2003, pp. 857-864.
[21] S. Kashyap, S. Kumar, V. Agarwal, D. P. Misra, S. R. Phadke, and A. Kapoor, Protein-protein interaction network analysis of differentially expressed genes to understand involved biological processes in coronary artery disease and its different severity, Gene Rep., 12 (2018), pp. 50-60.
[22] J. Kurose, On computing per-session performance bounds in high-speed multi-hop computer networks, ACM SIGMETRICS Perform. Eval. Rev., 20 (1992), pp. 128-139.
[23] Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F. Huang, A tutorial on energy-based learning, in Predicting Structured Data, MIT Press, 2006.
[24] J. Leskovec and A. Krevl, SNAP Datasets: Stanford Large Network Dataset Collection, http://snap.stanford.edu/data, 2019.
[25] J. Leskovec and J. J. Mcauley, Learning to discover social circles in ego networks, in Proc. of the 25th International Conference on Neural Information Processing Systems, 2012, pp. 539-547.
[26] K. Liu, X. Sun, L. Jia, J. Ma, H. Xing, J. Wu, H. Gao, Y. Sun, F. Boulnois, and J. Fan, Chemi-Net: A molecular graph convolutional network for accurate drug property prediction, Int. J. Mol. Sci., 20 (2019), art. 3389.
[27] J. McAuley and J. Leskovec, Image labeling on a network: Using social-network metadata for image classification, in European Conference on Computer Vision (ECCV 2012), Springer, 2012, pp. 828-841.
[28] A. K. McCallum, K. Nigam, J. Rennie, and K. Seymore, Automating the construction of internet portals with machine learning, Inf. Retr., 3 (2000), pp. 127-163.
[29] D. McDonald and S. He, Heat: Hyperbolic embedding of attributed networks, in Intelligent Data Engineering and Automated Learning (IDEAL 2020), Springer, 2020, pp. 28-40.
[30] A. Mheich, F. Wendling, and M. Hassan, Brain network similarity: Methods and applications, Netw. Neurosci., 4 (2020), pp. 507-527.
[31] T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient Estimation of Word Representations in Vector Space, preprint, https://arxiv.org/abs/1301.3781, 2013.
[32] G. A. Miller, WordNet: An Electronic Lexical Database, MIT Press, 1998. · Zbl 0913.68054
[33] P. Nerurkar, M. Chandane, and S. Bhirud, Survey of network embedding techniques for social networks, Turkish J. Elec. Eng. Comput. Sci., 27 (2019), pp. 4768-4782.
[34] G. H. Nguyen, J. B. Lee, R. A. Rossi, N. K. Ahmed, E. Koh, and S. Kim, Dynamic network embeddings: From random walks to temporal random walks, in the 2018 IEEE International Conference on Big Data, IEEE, 2018, pp. 1085-1092.
[35] M. Nickel and D. Kiela, Poincaré embeddings for learning hierarchical representations, in Proc. of the 31st Conference on Neural Information Processing Systems, 2017, pp. 6338-6347.
[36] A. N. Nikolakopoulos and G. Karypis, RecWalk: Nearly uncoupled random walks for top-n recommendation, in Proc. of the 12th ACM International Conference on Web Search and Data Mining, 2019, pp. 150-158.
[37] M. Okuda, S. Satoh, Y. Sato, and Y. Kidawara, Community detection using restrained random-walk similarity, IEEE Trans. Pattern Anal. Mach. Intell., 43 (2021), pp. 89-103.
[38] M. Ou, P. Cui, J. Pei, Z. Zhang, and W. Zhu, Asymmetric transitivity preserving graph embedding, in Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1105-1114.
[39] B. Perozzi, R. Al-Rfou, and S. Skiena, DeepWalk: Online learning of social representations, in Proc. of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, pp. 701-710.
[40] M. Ringnér, What is principal component analysis?, Nat. Biotechnol., 26 (2008), pp. 303-304.
[41] G. Rosenthal, F. Váša, A. Griffa, P. Hagmann, E. Amico, J. Gon͂i, G. Avidan, and O. Sporns, Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes, Nat. Comm., 9 (2018), pp. 1-12.
[42] S. T. Roweis and L. K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science, 290 (2000), pp. 2323-2326.
[43] C. Su, J. Tong, Y. Zhu, P. Cui, and F. Wang, Network embedding in biomedical data science, Brief. Bioinform., 21 (2020), pp. 182-197.
[44] Q. Tan, N. Liu, and X. Hu, Deep representation learning for social network analysis, Front. Big Data, 2 (2019), art. 2.
[45] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, LINE: Large-scale information network embedding, in Proc. of the 24th International Conference on World Wide Web, 2015, pp. 1067-1077.
[46] J. B. Tenenbaum, V. De Silva, and J. C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science, 290 (2000), pp. 2319-2323.
[47] L. Vilnis and A. McCallum, Word representations via Gaussian embedding, in Proc. of the 3rd International Conference on Learning Representations (ICLR 2015), 2015.
[48] D. Wang, P. Cui, and W. Zhu, Structural deep network embedding, in Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1225-1234.
[49] M. Wang, L. Yu, D. Zheng, Q. Gan, Y. Gai, Z. Ye, M. Li, J. Zhou, Q. Huang, C. Ma, et al., Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs, preprint, https://arxiv.org/abs/1909.01315, 2019.
[50] S. Wang, E. R. Flynn, and R. B. Altman, Gaussian embedding for large-scale gene set analysis, Nat. Mach. Intell., 2 (2020), pp. 387-395.
[51] Y. Wang, C. Feng, L. Chen, H. Yin, C. Guo, and Y. Chu, User identity linkage across social networks via linked heterogeneous network embedding, World Wide Web, 22 (2019), pp. 2611-2632.
[52] Z. Wang, J. Zhang, J. Feng, and Z. Chen, Knowledge graph embedding by translating on hyperplanes, in Proc. of the 28th AAAI Conference on Artificial Intelligence, 2014, pp. 1112-1119.
[53] J. D. West, I. Wesley-Smith, and C. T. Bergstrom, A recommendation system based on hierarchical clustering of an article-level citation network, IEEE Trans. Big Data, 2 (2016), pp. 113-123.
[54] R. C. Wilson, E. R. Hancock, E. Pekalska, and R. P. Duin, Spherical and hyperbolic embeddings of data, IEEE Trans. Pattern Anal. Mach. Intell., 36 (2014), pp. 2255-2269.
[55] M. Xu, D. P. Papageorgiou, S. Z. Abidi, M. Dao, H. Zhao, and G. E. Karniadakis, A deep convolutional neural network for classification of red blood cells in sickle cell anemia, PLoS Comput. Biol., 13 (2017), art. e1005746.
[56] M. Xu, D. L. Sanz, P. Garces, F. Maestu, Q. Li, and D. Pantazis, A graph Gaussian embedding method for predicting Alzheimer’s disease progression with MEG brain networks, IEEE Trans. Biomed. Eng., 68 (2021), pp. 1579-1588.
[57] M. Xu, Z. Wang, H. Zhang, D. Pantazis, H. Wang, and Q. Li, Gaussian Embedding-Based Functional Brain Connectomic Analysis for Amnestic Mild Cognitive Impairment Patients with Cognitive Training, preprint, https://doi.org/10.1101/779744, 2019.
[58] M. Xu, Z. Wang, H. Zhang, D. Pantazis, H. Wang, and Q. Li, A new graph Gaussian embedding method for analyzing the effects of cognitive training, PLoS Comput. Biol., 16 (2020), art. e1008186.
[59] W. W. Zachary, An information flow model for conflict and fission in small groups, J. Anthropol. Res., 33 (1977), pp. 452-473.
[60] D. Zhang, J. Yin, X. Zhu, and C. Zhang, User profile preserving social network embedding, in Proc. of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), 2017, pp. 3378-3384.
[61] Y. Zhou and H. Li, Asset diversification and systemic risk in the financial system, J. Econ. Interact. Coord., 14 (2019), pp. 247-272.
[62] D. Zhu, P. Cui, D. Wang, and W. Zhu, Deep variational network embedding in Wasserstein space, in Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018, pp. 2827-2836.
[63] Z. Zhu, S. Xu, J. Tang, and M. Qu, GraphVite: A high-performance CPU-GPU hybrid system for node embedding, in WWW ’19: The World Wide Web Conference, 2019, pp. 2494-2504.
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