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Network embedding: taxonomies, frameworks and applications. (English) Zbl 1484.68163

Summary: Networks are a general language for describing complex systems of interacting entities. In the real world, a network always contains massive nodes, edges and additional complex information which leads to high complexity in computing and analyzing tasks. Network embedding aims at transforming one network into a low dimensional vector space which benefits the downstream network analysis tasks. In this survey, we provide a systematic overview of network embedding techniques in addressing challenges appearing in networks. We first introduce concepts and challenges in network embedding. Afterwards, we categorize network embedding methods using three categories, including static homogeneous network embedding methods, static heterogeneous network embedding methods and dynamic network embedding methods. Next, we summarize the datasets and evaluation tasks commonly used in network embedding. Finally, we discuss several future directions in this field.

MSC:

68R10 Graph theory (including graph drawing) in computer science
05C82 Small world graphs, complex networks (graph-theoretic aspects)
68T05 Learning and adaptive systems in artificial intelligence
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[1] B. Perozzi, R. Al-Rfou, S. Skiena, DeepWalk: Online learning of social representations, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, pp. 701-710.
[2] M. Ou, P. Cui, J. Pei, Z. Zhang, W. Zhu, Asymmetric transitivity preserving graph embedding, in: ACM SIGKDD International Conference, 2016, pp. 1105-1114.
[3] Bedru, H. D.; Yu, S.; Xiao, X.; Zhang, D.; Wan, L.; Guo, H.; Xia, F., Big networks: A survey, Comp. Sci. Rev., 37, Article 100247 pp. (2020) · Zbl 1478.05141
[4] Kong, X.; Mao, M.; Wang, W.; Liu, J.; Xu, B., VOPRec: Vector representation learning of papers with text information and structural identity for recommendation, IEEE Trans. Emerg. Top. Comput., PP, 99, 1 (2018)
[5] Liu, J.; Xia, F.; Wang, L.; Xu, B.; Kong, X.; Tong, H.; King, I., Shifu2: A network representation learning based model for advisor-advisee relationship mining, IEEE Trans. Knowl. Data Eng., 1 (2019)
[6] W. Wang, J. Liu, F. Xia, I. King, H. Tong, Shifu: Deep learning based advisor-advisee relationship mining in scholarly big data, in: Proceedings of the 26th International Conference on World Wide Web Companion, 2017, pp. 303-310.
[7] J. Ma, P. Cui, W. Zhu, DepthLGP: Learning embeddings of out-of-sample nodes in dynamic networks, in: Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
[8] Bhagat, S.; Cormode, G.; Muthukrishnan, S., Node classification in social networks, (Social Network Data Analytics (2011), Springer), 115-148
[9] Wang, L.; Ren, J.; Xu, B.; Li, J.; Luo, W.; Xia, F., MODEL: Motif-based deep feature learning for link prediction, IEEE Trans. Comput. Soc. Syst., 7, 2, 503-516 (2020)
[10] C.H.Q. Ding, X. He, H. Zha, M. Gu, H.D. Simon, A min-max cut algorithm for graph partitioning and data clustering, in: IEEE International Conference on Data Mining, 2001, pp. 107-114.
[11] Kong, X.; Xia, F.; Ning, Z.; Rahim, A.; Cai, Y.; Gao, Z.; Ma, J., Mobility dataset generation for vehicular social networks based on floating car data, IEEE Trans. Veh. Technol., 67, 5, 3874-3886 (2018)
[12] Kong, X.; Shi, Y.; Yu, S.; Liu, J.; Xia, F., Academic social networks: Modeling, analysis, mining and applications, J. Netw. Comput. Appl., 132, 86-103 (2019)
[13] Li, J.; Dani, H.; Hu, X.; Tang, J.; Chang, Y.; Liu, H., Attributed network embedding for learning in a dynamic environment, (Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (2017), ACM), 387-396
[14] Tenenbaum, J. B.; Silva, V. D.; Langford, J. C., A global geometric framework for nonlinear dimensionality reduction, Science, 290, 5500, 2319-2323 (2000)
[15] Roweis, S. T.; Saul, L. K., Nonlinear dimensionality reduction by locally linear embedding, Science, 290, 5500, 2323-2326 (2000)
[16] Belkin, M.; Niyogi, P., Laplacian eigenmaps and spectral techniques for embedding and clustering, Adv. Neural Inf. Process. Syst., 14, 6, 585-591 (2001)
[17] Haussler, D., Convolution kernels on discrete structures, Tech. Rep., 7, 95-114 (1999)
[18] Liu, J.; Kong, X.; Feng, X.; Bai, X.; Lei, W.; Qing, Q.; Lee, I., Artificial intelligence in the 21st century, IEEE Access, 6, 99, 34403-34421 (2018)
[19] Peng, C.; Xiao, W.; Jian, P.; Zhu, W., A survey on network embedding, IEEE Trans. Knowl. Data Eng., PP, 99, 1 (2017)
[20] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, Q. Mei, LINE: Large-scale information network embedding, in: International Conference on World Wide Web, 2015.
[21] C. Yang, D. Zhao, D. Zhao, E.Y. Chang, E.Y. Chang, Network representation learning with rich text information, in: International Conference on Artificial Intelligence, 2015, pp. 2111-2117.
[22] X. Huang, J. Li, X. Hu, Label informed attributed network embedding, in: Tenth ACM International Conference on Web Search and Data Mining, 2017, pp. 731-739.
[23] Yan, S.; Xu, D.; Zhang, B.; Zhang, H.; Yang, Q.; Lin, S., Graph embedding and extensions: A general framework for dimensionality reduction, IEEE Trans. Pattern Anal. Mach. Intell., 29, 1, 40-51 (2007)
[24] Xia, F.; Liu, L.; Li, J.; Ahmed, A. M.; Yang, L. T.; Ma, J., BEEINFO: Interest-based forwarding using artificial bee colony for socially aware networking, IEEE Trans. Veh. Technol., 64, 3, 1188-1200 (2015)
[25] Kong, X.; Zhang, J.; Zhang, D.; Bu, Y.; Ding, Y.; Xia, F., The gene of scientific success, ACM Trans. Knowl. Discov. Data (TKDD), 14, 4, 19 (2020)
[26] Cai, H.; Zheng, V. W.; Chang, K. C.-C., A comprehensive survey of graph embedding: Problems, techniques, and applications, IEEE Trans. Knowl. Data Eng., 30, 9, 1616-1637 (2018)
[27] Goyal, P.; Ferrara, E., Graph embedding techniques, applications, and performance: A survey, Knowl.-Based Syst., 151, 78-94 (2018)
[28] Xia, F.; Liu, J.; Nie, H.; Fu, Y.; Wan, L.; Kong, X., Random walks: A review of algorithms and applications, IEEE Trans. Emerg. Top. Comput. Intell., 4, 2, 95-107 (2019)
[29] Xia, F.; Chen, Z.; Wang, W.; Li, J.; Yang, L. T., MVCWalker: Random walk-based most valuable collaborators recommendation exploiting academic factors, IEEE Trans. Emerg. Top. Comput., 2, 3, 364-375 (2014)
[30] Wang, Q.; Mao, Z.; Wang, B.; Guo, L., Knowledge graph embedding: A survey of approaches and applications, IEEE Trans. Knowl. Data Eng., 29, 12, 2724-2743 (2017)
[31] Bordes, A.; Usunier, N.; Garcia-Duran, A.; Weston, J.; Yakhnenko, O., Translating embeddings for modeling multi-relational data, (Advances in Neural Information Processing Systems (2013)), 2787-2795
[32] S. Cao, W. Lu, Q. Xu, GraRep: Learning graph representations with global structural information, in: ACM International on Conference on Information and Knowledge Management, 2015, pp. 891-900.
[33] J. Xu, S. Yu, K. Sun, J. Ren, I. Lee, S. Pan, F. Xia, Multivariate relations aggregation learning in social networks, in: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, 2020, pp. 77-86.
[34] Wang, W.; Yu, S.; Bekele, T. M.; Kong, X.; Xia, F., Scientific collaboration patterns vary with scholars’academic ages, Scientometrics, 112, 1, 329-343 (2017)
[35] Wan, L.; Yuan, Y.; Xia, F.; Liu, H., To your surprise: Identifying serendipitous collaborators, IEEE Trans. Big Data, 1 (2019)
[36] S. Yu, F. Xia, K. Zhang, Z. Ning, J. Zhong, C. Liu, Team recognition in big scholarly Data: Exploring collaboration intensity, in: 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, DASC/PiCom/DataCom/CyberSciTech, 2017, pp. 925-932.
[37] Lin, T.; Zha, H., Riemannian manifold learning, IEEE Trans. Pattern Anal. Mach. Intell., 30, 5, 796-809 (2008)
[38] Cox, M. A.A.; Cox, T. F., Multidimensional scaling, J. R. Stat. Soc., 46, 2, 1050-1057 (2001) · Zbl 1004.91067
[39] Smola, A., Kernels and regularization on graphs, Lecture Notes in Comput. Sci., 2777, 144-158 (2003) · Zbl 1274.68351
[40] Mikolov, T.; Chen, K.; Corrado, G.; Dean, J., Efficient estimation of word representations in vector space (2013)
[41] Huffman, D. A., A method for the construction of minimum-redundancy codes, Resonance, 11, 2, 91-99 (2006)
[42] A. Grover, J. Leskovec, node2vec: Scalable feature learning for networks, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 855-864.
[43] Hamilton, W. L.; Ying, R.; Leskovec, J., Representation learning on graphs: Methods and applications (2017)
[44] Bengio, Y., Learning Deep Architectures for AI (2009), Now Publishers Inc · Zbl 1192.68503
[45] D. Wang, P. Cui, W. Zhu, Structural deep network embedding, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1225-1234.
[46] Davis, C., The norm of the Schur product operation, Numer. Math., 4, 1, 343-344 (1962) · Zbl 0111.01504
[47] Deng, L. Y., The cross-entropy method: A unified approach to combinatorial optimization, Monte-Carlo simulation and machine learning, Technometrics, 48, 1, 147-148 (2004) · Zbl 1140.90005
[48] Eckart, C.; Young, G., The approximation of one matrix by another of lower rank, Psychometrika, 1, 3, 211-218 (1936) · JFM 62.1075.02
[49] S. Cao, W. Lu, Q. Xu, Deep neural networks for learning graph representations, in: Thirtieth AAAI Conference on Artificial Intelligence, 2016, pp. 1145-1152.
[50] L. Page, The pagerank citation ranking: Bringing order to the web, in: Stanford Digital Libraries Working Paper, Vol. 9, No. 1, 1998, pp. 1-14.
[51] P. Hanks, P. Hanks, Word association norms, mutual information, and lexicography, in: Meeting on Association for Computational Linguistics, 1990, pp. 76-83.
[52] Katz, L., A new status index derived from sociometric analysis, Psychometrika, 18, 1, 39-43 (1953) · Zbl 0053.27606
[53] Liben-Nowell, D.; Kleinberg, J., The Link-Prediction Problem for Social Networks, 1019-1031 (2007), John Wiley and Sons, Inc.
[54] A. A.damic, L.; Adar, E., Friends and neighbors on the web, Social Networks, 25, 3, 211-230 (2003)
[55] J. Leskovec, D. Huttenlocher, J. Kleinberg, Predicting positive and negative links in online social networks, in: International Conference on World Wide Web, 2010, pp. 641-650.
[56] H. Ma, M.R. Lyu, I. King, Learning to recommend with trust and distrust relationships, in: ACM Conference on Recommender Systems, 2009, pp. 189-196.
[57] Wang, S.; Tang, J.; Aggarwal, C.; Chang, Y.; Liu, H., Signed network embedding in social media, (Proceedings of the 2017 SIAM International Conference on Data Mining (2017), SIAM), 327-335
[58] HEIDERF, S., Attitudes and cognitive organization, J. Psychol., 21, 1, 3-8 (1977)
[59] Cartwright, D.; Harary, F., Structural balance: a generalization of Heider’s theory, Psychol. Rev., 63, 5, 9-25 (1977)
[60] Kullback, S.; Leibler, R. A., On information and sufficiency, Ann. Math. Stat., 22, 1, 79-86 (1951) · Zbl 0042.38403
[61] Liu, J.; Kong, X.; Xia, F.; Bai, X.; Wang, L.; Qing, Q.; Lee, I., Artificial intelligence in the 21st century, IEEE Access, 6, 34403-34421 (2018)
[62] Yu, H. F.; Jain, P.; Kar, P.; Dhillon, I. S., Large-scale multi-label learning with missing labels, 593-601 (2013)
[63] Natarajan, N.; Dhillon, I. S., Inductive matrix completion for predicting gene-disease associations, Bioinformatics, 30, 12, i60-i68 (2014)
[64] C. Tu, W. Zhang, Z. Liu, M. Sun, Max-margin deepwalk: Discriminative learning of network representation, in: International Joint Conference on Artificial Intelligence, 2016, pp. 3889-3895.
[65] Hearst, M. A.; Dumais, S. T.; Osuna, E.; Platt, J.; Scholkopf, B., Support vector machines, IEEE Intell. Syst. Appl., 13, 4, 18-28 (1998)
[66] Liu, J.; Tian, J.; Kong, X.; Lee, I.; Xia, F., Two decades of information systems: a bibliometric review, Scientometrics, 118, 2, 617-643 (2019)
[67] J. Tang, M. Qu, Q. Mei, PTE: predictive text embedding through large-scale heterogeneous text networks, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015 pp. 1165-1174.
[68] Jacob, Y.; Denoyer, L.; Gallinari, P., Learning latent representations of nodes for classifying in heterogeneous social networks, (Proceedings of the 7th ACM International Conference on Web Search and Data Mining (2014), ACM), 373-382
[69] Vorontsov, M. A.; Sivokon, V. P., Stochastic parallel-gradient-descent technique for high-resolution wave-front phase-distortion correction, J. Opt. Soc. Amer. A, 15, 10, 2745-2758 (1998)
[70] Sun, Y.; Han, J., Mining heterogeneous information networks: Principles and methodologies, Acm Sigkdd Explor. Newslett., 14, 2, 20-28 (2012)
[71] A. Swami, A. Swami, A. Swami, metapath2vec: Scalable representation learning for heterogeneous networks, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 135-144. · Zbl 0864.94007
[72] T.-y. Fu, W.-C. Lee, Z. Lei, Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning, in: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017, pp. 1797-1806.
[73] S. Chang, W. Han, J. Tang, G.J. Qi, C.C. Aggarwal, T.S. Huang, Heterogeneous network embedding via deep architectures, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 119-128.
[74] B. Schölkopf, J. Platt, T. Hofmann, Greedy layer-wise training of deep networks, in: International Conference on Neural Information Processing Systems, 2006, pp. 153-160.
[75] Hinton, G.; Salakhutdinov, R., Reducing the dimensionality of data with neural networks, Science, 313, 5786, 504-507 (2006) · Zbl 1226.68083
[76] Bordes, X. G.A.; Bengio, Y., Deep Sparse Rectifier Networks (2011), AISTATS
[77] A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in: International Conference on Neural Information Processing Systems, 2012, pp. 1097-1105.
[78] Wang, H.; Zhang, F.; Hou, M.; Xie, X.; Guo, M.; Liu, Q., Shine: Signed heterogeneous information network embedding for sentiment link prediction, (Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (2018), ACM), 592-600
[79] Z. Wang, Y. Zhang, H. Chen, Z. Li, F. Xia, Deep user modeling for content-based event recommendation in event-based social networks, in: IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, 2018, pp. 1304-1312.
[80] F. Zhang, N.J. Yuan, D. Lian, X. Xie, W.Y. Ma, Collaborative knowledge base embedding for recommender systems, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 353-362.
[81] P. Wang, J. Guo, Y. Lan, J. Xu, S. Wan, X. Cheng, Learning hierarchical representation model for nextbasket recommendation, in: International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 403-412.
[82] Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G., Human-level control through deep reinforcement learning, Nature, 518, 7540, 529 (2015)
[83] F. Wu, J. Song, Y. Yang, X. Li, Z. Zhang, Y. Zhuang, Structured embedding via pairwise relations and long-range interactions in knowledge base, in: Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015, pp. 1663-1670.
[84] J. Liu, J. Ren, W. Zheng, L. Chi, I. Lee, F. Xia, Web of scholars: A scholar knowledge graph, in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 2153-2156.
[85] S. Gelly, D. Silver, Combining online and offline knowledge in UCT, in: International Conference on Machine Learning, 2007, pp. 273-280.
[86] Sutton, R.; Barto, A., Reinforcement learning: An introduction, bradford book, Mach. Learn., 16, 1, 285-286 (2005)
[87] S. Tang, B. Andres, M. Andriluka, B. Schiele, Multi-person tracking by multicut and deep matching, in: European Conference on Computer Vision, 2016, pp. 100-111.
[88] Xia, F.; Rahim, A.; Kong, X.; Wang, M.; Cai, Y.; Wang, J., Modeling and analysis of large-scale urban mobility for green transportation, IEEE Trans. Ind. Inf., 14, 4, 1469-1481 (2018)
[89] Xia, F.; Wang, J.; Kong, X.; Zhang, D.; Wang, Z., Ranking station importance with human mobility patterns using subway network datasets, IEEE Trans. Intell. Transp. Syst., 21, 7, 2840-2852 (2020)
[90] Wang, W.; Liu, J.; Tang, T.; Tuarob, S.; Xia, F.; Gong, Z.; King, I., Attributed collaboration network embedding for academic relationship mining, ACM Trans. Web, 1, 1 (2020)
[91] L. Zhou, Y. Yang, X. Ren, F. Wu, Y. Zhuang, Dynamic network embedding by modeling triadic closure process, in: Thirty-Second AAAI Conference on Artificial Intelligence.
[92] Yu, S.; Xu, J.; Zhang, C.; Xia, F.; Almakhadmeh, Z.; Tolba, A., Motifs in big networks: Methods and applications, IEEE Access, 7, 183322-183338 (2019)
[93] Xia, F.; Wei, H.; Yu, S.; Zhang, D.; Xu, B., A survey of measures for network motifs, IEEE Access, 7, 106576-106587 (2019)
[94] B. Schölkopf, J. Platt, T. Hofmann, Relational learning with Gaussian processes, in: Conference on Advances in Neural Information Processing Systems, 2005, pp. 137-144.
[95] K. Yu, W. Chu, Gaussian process models for link analysis and transfer learning, in: International Conference on Neural Information Processing Systems, 2007, pp. 1657-1664.
[96] Seeger, M., Gaussian processes for machine learning, Publ. Am. Stat. Assoc., 103, 481, 429 (2008)
[97] Xia, F.; Wang, W.; Bekele, T. M.; Liu, H., Big scholarly data: A survey, IEEE Trans. Big Data, 3, 1, 18-35 (2017)
[98] Xia, F.; Asabere, N. Y.; Liu, H.; Chen, Z.; Wang, W., Socially aware conference participant recommendation with personality traits, IEEE Syst. J., 11, 4, 2255-2266 (2014)
[99] D. Zhang, T. Guo, H. Pan, J. Hou, Z. Feng, L. Yang, H. Lin, F. Xia, Judging a book by its cover: The effect of facial perception on centrality in social networks, in: The World Wide Web Conference, 2019, pp. 2290-2300.
[100] Yu, S.; Bedru, H. D.; Lee, I.; Xia, F., Science of scientific team science: A survey, Comp. Sci. Rev., 31, 72-83 (2019)
[101] Xu, B.; Li, K.; Zheng, W.; Liu, X.; Zhang, Y.; Zhao, Z.; He, Z., Protein complexes identification based on go attributed network embedding, BMC Bioinform., 19, 1, 535 (2018)
[102] L. Tang, H. Liu, Relational learning via latent social dimensions, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28-July, 2009 pp. 817-826.
[103] McAuley, J.; Leskovec, J., Image labeling on a network: using social-network metadata for image classification, (European Conference on Computer Vision (2012), Springer), 828-841
[104] Yang, J.; Leskovec, J., Defining and evaluating network communities based on ground-truth, Knowl. Inf. Syst., 42, 1, 181-213 (2015)
[105] Mccallum, A. K.; Nigam, K.; Rennie, J.; Seymore, K., Automating the construction of internet portals with machine learning, Inf. Retr., 3, 2, 127-163 (2000)
[106] J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, Z. Su, ArnetMiner: extraction and mining of academic social networks, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008, pp. 990-998.
[107] Leskovec, J.; Kleinberg, J.; Faloutsos, C., Graph evolution: Densification and shrinking diameters, ACM Trans. Knowl. Discov. Data (TKDD), 1, 1, 2 (2007)
[108] D. Milne, I.H. Witten, Learning to link with wikipedia, in: Proceedings of the 17th ACM Conference on Information and Knowledge Management, 2008, pp. 509-518.
[109] Lehmann, J., DBpedia - A large-scale, multilingual knowledge base extracted from wikipedia, Seman. Web, 6, 2, 167-195 (2015)
[110] Szklarczyk, D.; Morris, J. H.; Cook, H.; Kuhn, M.; Wyder, S.; Simonovic, M.; Santos, A.; Doncheva, N. T.; Roth, A.; Bork, P.; Jensen, L. J.; vonMering, C., The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible, Nucleic Acids Res., 45, D1, D362-D368 (2016)
[111] Liberzon, A.; Subramanian, A.; Pinchback, R.; Thorvaldsdóttir, H.; Tamayo, P.; Mesirov, J. P., Molecular signatures database (MSigDB) 3.0, Bioinformatics, 27, 12, 1739 (2011)
[112] A. Turpin, F. Scholer, User performance versus precision measures for simple search tasks, in: International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006, pp. 11-18.
[113] J. Macqueen, Some methods for classification and analysis of multivariate observations, in: Proc. of Berkeley Symposium on Mathematical Statistics and Probability, 1966, pp. 281-297. · Zbl 0214.46201
[114] Han, J.; Pei, J.; Kamber, M., Data Mining: Concepts and Techniques (2011), Elsevier
[115] Xia, F.; Liu, H.; Lee, I.; Cao, L., Scientific article recommendation: Exploiting common author relations and historical preferences, IEEE Trans. Big Data, 2, 2, 101-112 (2016)
[116] Yu, S.; Xia, F.; Liu, H., Academic team formulation based on Liebig’s barrel: Discovery of anticask effect, IEEE Trans. Comput. Soc. Syst., 6, 5, 1083-1094 (2019)
[117] Hartigan, J. A.; Wong, M. A., Algorithm AS 136: A K-means clustering algorithm, J. R. Stat. Soc., 28, 1, 100-108 (1979) · Zbl 0447.62062
[118] Xia, F.; Liu, L.; Jedari, B.; Das, S. K., PIS: A multi-dimensional routing protocol for socially-aware networking, IEEE Trans. Mob. Comput., 15, 11, 2825-2836 (2016)
[119] Xia, F.; Liaqat, H. B.; Deng, J.; Wan, J.; Das, S. K., Overhead control with reliable transmission of popular packets in ad-hoc social networks, IEEE Trans. Veh. Technol., 65, 9, 7647-7661 (2016)
[120] Xu, B.; Li, L.; Liu, J.; Wan, L.; Kong, X.; Xia, F., Disappearing link prediction in scientific collaboration networks, IEEE Access, 6, 69702-69712 (2018)
[121] Maaten, L.v.d.; Hinton, G., Visualizing data using t-SNE, J. Mach. Learn. Res., 9, Nov, 2579-2605 (2008) · Zbl 1225.68219
[122] X. Wang, P. Cui, J. Wang, J. Pei, W. Zhu, S. Yang, Community preserving network embedding, in: The AAAI Conference on Artificial Intelligence, 2017.
[123] Xia, F.; Ahmed, A. M.; Yang, L. T.; Luo, Z., Community-based event dissemination with optimal load balancing, IEEE Trans. Comput., 64, 7, 1857-1869 (2015) · Zbl 1360.68220
[124] A. Paranjape, A.R. Benson, J. Leskovec, Motifs in temporal networks, in: Tenth ACM International Conference on Web Search and Data Mining, 2017, pp. 601-610.
[125] Boldi, P.; Bonchi, F.; Gionis, A.; Tassa, T., Injecting uncertainty in graphs for identity obfuscation, Proc. VLDB Endow., 5, 11, 1376-1387 (2012)
[126] Xia, F.; Jedari, B.; Yang, L. T.; Ma, J.; Huang, R., A signaling game for uncertain data delivery in selfish mobile social networks, IEEE Trans. Comput. Soc. Syst., 3, 2, 100-112 (2016)
[127] J. Hu, R. Cheng, Z. Huang, Y. Fang, S. Luo, On embedding uncertain graphs, in: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017, pp. 157-166.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.