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International Journal of Automation and Computing 2018, Vol. 15 Issue (4) :377-401    DOI: 10.1007/s11633-018-1124-0
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Study on Information Diffusion Analysis in Social Networks and Its Applications
Biao Chang, Tong Xu, Qi Liu, En-Hong Chen
Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science, University of Science and Technology of China, Hefei 230026, China
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Abstract Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximization and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffusion analysis are listed for further study.
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KeywordsInformation diffusion   influence evaluation   influence maximization   information source detection   social network     
Received: 2017-11-28;
Fund:

This research was supported by National Natural Science Foundation of China (Nos. 61703386, U1605251 and 91546103), the Anhui Provincial Natural Science Foundation (No. 1708085QF140), the Fundamental Research Funds for the Central Universities (No. WK2150110006), and the Youth Innovation Promotion Association of Chinese Academy of Sciences (No. 2014299).

Corresponding Authors: En-Hong Chen     Email: cheneh@ustc.edu.cn
About author: Biao Chang received the B.Sc. degree in computer science from University of Science and Technology of China (USTC), China in 2012.E-mail: changb110@gmail.com;Tong Xu received the Ph.D. degree in University of Science and Technology of China (USTC), China in 2016.E-mail: qiliuql@ustc.edu.cn;En-Hong Chen received the Ph.D. degree from University of Science and Technology of China. He is a professor and vice dean of School of Computer Science, USTC. He has published more than 150 papers in refereed conferences and journals, including IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Industrial Electronics, KDD, ICDM, NIPS, and CIKM.E-mail: cheneh@ustc.edu.cn
Cite this article:   
Biao Chang, Tong Xu, Qi Liu, En-Hong Chen. Study on Information Diffusion Analysis in Social Networks and Its Applications[J]. International Journal of Automation and Computing , vol. 15, no. 4, pp. 377-401, 2018.
URL:  
http://www.ijac.net/EN/10.1007/s11633-018-1124-0      或     http://www.ijac.net/EN/Y2018/V15/I4/377
 
[1] Zephoria. The top 20 valuable Facebook statistics-updated April 2018. [Online], Available: https://zephoria.com/top-15-valuable-facebook-statistics/, October 1, 2017.
[2] H. Kwak, C. Lee, H. Park, S. Moon. What is Twitter, a social network or a news media? In Proceedings of the 19th International Conference on World Wide Web, New York, USA, pp. 591-600, 2010.
[3] H. P. Zhang, R. Q. Zhang, Y. P. Zhao, B. J. Ma. Big data modeling and analysis of microblog ecosystem. International Journal of Automation and Computing, vol. 11, no. 2, pp. 119-127, 2014. DOI: 10.1007/s11633-014-0774-9. [DOI:10.1007/s11633-014-0774-9]
[4] D. Kempe, J. Kleinberg, É. Tardos. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Washington DC, USA, pp. 137-146, 2003. [DOI:10.1145/956750.956769]
[5] J. Leskovec, L. A. Adamic, B. A. Huberman. The dynamics of viral marketing. ACM Transactions on the Web, vol. 1, no. 1, pp. 5, 2007. DOI: 10.1145/1232722.1232727. [DOI:10.1145/1232722.1232727]
[6] M. Richardson, P. Domingos. Mining knowledge-sharing sites for viral marketing. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Edmonton, Canada, pp. 61-70, 2002. [DOI:10.1145/775047.775057]
[7] C. Ma, C. Zhu, Y. J. Fu, H. S. Zhu, G. Q. Liu, E. H. Chen. Social user profiling: A social-aware topic modeling perspective. In Proceedings of the 22nd International Conference on Database Systems for Advanced Applications, Springer, Suzhou, China, pp. 610-622, 2017. [DOI:10.1007/978-3-319-55699-4_38]
[8] T. Xu, H. S. Zhu, X. Y. Zhao, Q. Liu, H. Zhong, E. H. Chen, H. Xiong. Taxi driving behavior analysis in latent vehicle-to-vehicle networks: A social influence perspective. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco, USA, pp. 1285-1294, 2016. [DOI:10.1145/2939672.2939799]
[9] X. Y. Zhao, T. Xu, Q. Liu, H. Guo. Exploring the choice under conflict for social event participation. In Proceedings of the 21st International Conference on Database Systems for Advanced Applications, Springer, Dallas, USA, pp. 396-411, 2016. [DOI:10.1007/978-3-319-32025-0_25]
[10] L. Backstrom, J. Leskovec. Supervised random walks: Predicting and recommending links in social networks. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining, ACM, Hong Kong, China, pp. 635-644, 2011. [DOI:10.1145/1935826.1935914]
[11] D. Liben-Nowell, J. Kleinberg. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, vol. 58, no. 7, pp. 1019-1031, 2007. DOI: 10.1002/asi.v58:7. [DOI:10.1002/asi.v58:7]
[12] T. Xu, H. S. Zhu, E. H. Chen, B. X. Huai, H. Xiong, J. L. Tian. Learning to annotate via social interaction analytics. Knowledge and Information Systems, vol. 41, no. 2, pp. 251-276, 2014. DOI: 10.1007/s10115-013-0717-8. [DOI:10.1007/s10115-013-0717-8]
[13] S. Fortunato. Community detection in graphs. Physics Reports, vol. 486, no. 3, pp. 75-174, 2010. DOI: 10.1016/j.physrep.2009.11.002. [DOI:10.1016/j.physrep.2009.11.002]
[14] S. Fortunato, M. Barthlemy. Resolution limit in community detection. Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 1, pp. 36-41, 2007. DOI: 10.1073/pnas.0605965104. [DOI:10.1073/pnas.0605965104]
[15] M. Girvan, M. E. J. Newman. Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 12, pp. 7821-7826, 2002.
[16] J. Goldenberg, B. Libai, E. Muller. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, vol. 12, no. 3, pp. 211-223, 2001. DOI: 10.1023/A:1011122126881. [DOI:10.1023/A:1011122126881]
[17] M. Granovetter. Threshold models of collective behavior. American Journal of Sociology, vol. 83, no. 6, pp. 1420-1443, 1978. DOI: 10.1086/226707. [DOI:10.1086/226707]
[18] W. O. Kermack, A. G. McKendrick. Contributions to the mathematical theory of epidemics - Ⅱ. The problem of endemicity. Bulletin of Mathematical Biology, vol. 53, no. 1-2, pp. 57-87, 1991. [DOI:10.1007/BF02464424]
[19] M. Cataldi, L. Di Caro, C. Schifanella. Emerging topic detection on Twitter based on temporal and social terms evaluation. In Proceedings of the 10th International Workshop on Multimedia Data Mining, ACM, Washington DC, USA, 2010. [DOI:10.1145/1814245.1814249]
[20] M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, H. A. Makse. Identification of influential spreaders in complex networks. Nature Physics, vol. 6, no. 11, pp. 888-893, 2010. DOI: 10.1038/nphys1746. [DOI:10.1038/nphys1746]
[21] J. Zhang, J. Tang, J. Z. Li. Expert finding in a social network. In Proceedings of the 12th International Conference on Database Systems for Advanced Applications, Springer, Bangkok, Thailand, pp. 1066-1069, 2007. [DOI:10.1007/978-3-540-71703-4_106]
[22] H. S. Zhu, E. H. Chen, H. Xiong, H. H. Cao, J. L. Tian. Ranking user authority with relevant knowledge categories for expert finding. World Wide Web, vol. 17, no. 5, pp. 1081-1107, 2014. DOI: 10.1007/s11280-013-0217-5. [DOI:10.1007/s11280-013-0217-5]
[23] H. S. Zhu, E. H. Chen, H. H. Cao. Finding experts in tag based knowledge sharing communities. In Proceedings of the 5th International Conference on Knowledge Science, Engineering and Management, Springer, Irvine, USA, pp. 183-195, 2011. [DOI:10.1007/978-3-642-25975-3_17]
[24] J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Jose, USA, pp. 420-429, 2007. [DOI:10.1145/1281192.1281239]
[25] L. Zou, Z. D. Wang, D. H. Zhou. Event-based control and filtering of networked systems: A survey. International Journal of Automation and Computing, vol. 14, no. 3, pp. 239-253, 2017. DOI: 10.1007/s11633-017-1077-8. [DOI:10.1007/s11633-017-1077-8]
[26] B. Chang, F. D. Zhu, E. H. Chen, Q. Liu. Information source detection via maximum a posteriori estimation. In Proceedings of IEEE International Conference on Data Mining, IEEE, Atlantic City, USA, pp. 21-30, 2015. [DOI:10.1109/ICDM.2015.116]
[27] V. Fioriti, M. Chinnici. Predicting the sources of an outbreak with a spectral technique. Applied Mathematical Sciences, vol. 8, pp. 6775-6782, 2014. DOI: 10.12988/ams.2014.49693. [DOI:10.12988/ams.2014.49693]
[28] B. A. Prakash, J. Vreeken, C. Faloutsos. Spotting culprits in epidemics: How many and which ones? In Proceedings of the 12th International Conference on Data Mining, IEEE, Brussels, Belgium, pp. 11-20, 2012. [DOI:10.1109/ICDM.2012.136]
[29] P. Gundecha, Z. Feng, H. Liu. Seeking provenance of information using social media. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, ACM, San Francisco, USA, pp. 1691-1696, 2013. [DOI:10.1145/2505515.2505633] Proceedings of the 22nd ACM International Conference on Information target="_blank">
[30] D. Shah, T. Zaman. Rumors in a network: Who's the culprit? IEEE Transactions on Information Theory, vol. 57, no. 8, pp. 5163-5181, 2011. DOI: 10.1109/TIT.2011.2158885. [DOI:10.1109/TIT.2011.2158885]
[31] A. Goyal, F. Bonchi, L. V. S. Lakshmanan. Learning influence probabilities in social networks. In Proceedings of the 3rd ACM International Conference on Web Search and Web Data Mining, ACM, New York, USA, pp. 241-250, 2010. [DOI:10.1145/1718487.1718518]
[32] B. Ryan, N. C. Gross. The diffusion of hybrid seed corn in two Iowa communities. Rural Sociology, vol. 8, no. 1, pp. 15-24, 1943.
[33] H. Y. Zhang, S. Mishra, M. T. Thai. Recent advances in information diffusion and influence maximization in complex social networks. Opportunistic Mobile Social Networks, J. Wu, Y. S. Wang, Eds., USA: CRC Press, 2014.
[34] T. Lappas, E. Terzi, D. Gunopulos, H. Mannila. Finding effectors in social networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Washington DC, USA, pp. 1059-1068, 2010. [DOI:10.1145/1835804.1835937]
[35] D. Shah, T. Zaman. Detecting sources of computer viruses in networks: Theory and experiment. In Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, ACM, New York, USA, pp. 203-214, 2010. [DOI:10.1145/1811039.1811063]
[36] R. Zafarani, M. A. Abbasi, H. Liu. Social Media Mining: An Introduction, Cambridge, UK: Cambridge University Press, 2014.
[37] Y. Yao, X. R. Luo, F. X. Gao, S. L. Ai. Research of a potential worm propagation model based on pure P2P principle. In Proceedings of International Conference on Communication Technology, IEEE, Guilin, China, 2006. [DOI:10.1109/ICCT.2006.342006]
[38] H. W. Hethcote. The mathematics of infectious diseases. SIAM Review, vol. 42, no. 4, pp. 599-653, 2000. DOI: 10.1137/S0036144500371907. [DOI:10.1137/S0036144500371907]
[39] K. L. Cooke, P. van den Driessche. Analysis of an SEIRS epidemic model with two delays. Journal of Mathematical Biology, vol. 35, no. 2, pp. 240-260, 1996. DOI: 10.1007/s002850050051. [DOI:10.1007/s002850050051]
[40] Y. Xiang, X. Fan, W. T. Zhu. Propagation of active worms: A survey. International Journal of Computer Systems Science and Engineering, vol. 24, no. 3, pp. 157-172, 2009.
[41] H. S. Zhu, E. H. Chen, H. H. Cao, J. L. Tian. Context-aware expert finding in tag based knowledge sharing communities. International Journal of Knowledge and Systems Science, vol. 3, no. 1, pp. 48-63, 2012. DOI: 10.4018/jkss.2012010104. [DOI:10.4018/jkss.2012010104]
[42] H. S. Zhu, H. H. Cao, H. Xiong, E. H. Chen, J. L. Tian. Towards expert finding by leveraging relevant categories in authority ranking. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, ACM, Glasgow, UK, pp. 2221-2224, 2011. [DOI:10.1145/2063576.2063931]
[43] B. A. Prakash, C. Faloutsos. Understanding and managing cascades on large graphs. Proceedings of the VLDB Endowment, vol. 5, no. 12, pp. 2024-2025, 2012. DOI: 10.14778/2367502.2367567. [DOI:10.14778/2367502.2367567]
[44] B. Xiang, Q. Liu, E. H. Chen, H. Xiong, Y. Zheng, Y. Yang. PageRank with priors: An influence propagation perspective. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, pp. 2740-2746, 2013.
[45] S. Y. Lin, W. X. Hong, D. D. Wang, T. Li. A survey on expert finding techniques. Journal of Intelligent Information Systems, vol. 49, no. 2, pp. 255-279, 2017. DOI: 10.1007/s10844-016-0440-5. [DOI:10.1007/s10844-016-0440-5]
[46] A. Bavelas. Communication patterns in task-oriented groups. The Journal of the Acoustical Society of America, vol. 22, no. 6, pp. 725-730, 1950. DOI: 10.1121/1.1906679. [DOI:10.1121/1.1906679]
[47] L. C. Freeman. A set of measures of centrality based on betweenness. Sociometry, vol. 40, no. 1, pp. 35-41, 1977. DOI: 10.2307/3033543. [DOI:10.2307/3033543]
[48] C. Jordan. Sur les assemblages de lignes. Journal fr die reine und angewandte Mathematik, vol. 1869, no. 70, pp. 185-190, 1869. DOI: 10.1515/crll.1869.70.185. (in French) [DOI:10.1515/crll.1869.70.185]
[49] L. Page, S. Brin, R. Motwani, T. Winograd. The PageRank citation ranking: Bringing order to the web, Technical Report SIDL-WP-1999-0120, Stanford University, USA, 1999.
[50] P. Berkhin. A survey on PageRank computing. Internet Mathematics, vol. 2, no. 1, pp. 73-120, 2005. DOI: 10.1080/15427951.2005.10129098. [DOI:10.1080/15427951.2005.10129098]
[51] T. H. Haveliwala. Topic-sensitive PageRank. In Proceedings of the 11th International Conference on World Wide Web, ACM, New York, USA, pp. 517-526, 2002.
[52] J. M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, vol. 46, no. 5, pp. 604-632, 1999. DOI: 10.1145/324133.324140. [DOI:10.1145/324133.324140]
[53] J. S. Weng, E. P. Lim, J. Jiang, Q. He. TwitterRank: Finding topic-sensitive influential twitterers. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, ACM, New York, USA, pp. 261-270, 2010. [DOI:10.1145/1718487.1718520]
[54] Q. Liu, B. Xiang, E. H. Chen, Y. Ge, H. Xiong, T. F. Bao, Y. Zheng. Influential seed items recommendation. In Proceedings of the 6th ACM Conference on Recommender Systems, ACM, Dublin, Ireland, pp. 245-248, 2012. [DOI:10.1145/2365952.2366005]
[55] W. Chen, C. Wang, Y. J. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Washington DC, USA, pp. 1029-1038, 2010. [DOI:10.1145/1835804.1835934]
[56] W. Chen, Y. F. Yuan, L. Zhang. Scalable influence maximization in social networks under the linear threshold model. In Proceedings of the 10th International Conference on Data Mining, IEEE, Sydney, Australia, pp. 88-97, 2010. [DOI:10.1109/ICDM.2010.118]
[57] C. C. Aggarwal, A. Khan, X. F. Yan. On flow authority discovery in social networks. In Proceedings of SIAM International Conference on Data Mining, SIAM, Mesa, USA, pp. 522-533, 2011. [DOI:10.1137/1.9781611972818.45]
[58] Y. Yang, E. H. Chen, Q. Liu, B. Xiang, T. Xu, S. A. Shad. On approximation of real-world influence spread. In Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Bristol, UK, pp. 548-564, 2012. [DOI:10.1007/978-3-642-33486-3_35]
[59] J. Yang, J. Leskovec. Modeling information diffusion in implicit networks. In Proceedings of the10th International Conference on Data Mining, IEEE, Sydney, Australia, pp. 599-608, 2010. [DOI:10.1109/ICDM.2010.22]
[60] Q. Liu, B. Xiang, N. J. Yuan, E. H. Chen, H. Xiong, Y. Zheng, Y. Yang. An influence propagation view of PageRank. ACM Transactions on Knowledge Discovery from Data, vol. 11, no. 3, pp. 30, 2017. DOI: 10.1145/3046941. [DOI:10.1145/3046941]
[61] J. Tang, J. M. Sun, C. Wang, Z. Yang. Social influence analysis in large-scale networks. In Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining, ACM, Paris, France, pp. 807-816, 2009. [DOI:10.1145/1557019.1557108]
[62] Q. Liu, B. Xiang, L. Zhang, E. H. Chen, C. Tan, J. Chen. Linear computation for independent social influence. In Proceedings of the 13th International Conference on Data Mining, IEEE, Dallas, USA, pp. 468-477, 2013. [DOI:10.1109/ICDM.2013.48]
[63] Q. Liu, B. Xiang, E. H. Chen, H. Xiong, F. S. Tang, J. X. Yu. Influence maximization over large-scale social networks: A bounded linear approach. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, ACM, Shanghai, China, pp. 171-180, 2014. [DOI:10.1145/2661829.2662009]
[64] G. L. Nemhauser, L. A. Wolsey, M. L. Fisher. An analysis of approximations for maximizing submodular set functions - I. Mathematical Programming, vol. 14, pp. 265-294, 1978.
[65] A. Goyal, W. Lu, L. V. S. Lakshmanan. CELF++: Optimizing the greedy algorithm for influence maximization in social networks. In Proceedings of the 20th International Conference Companion on World Wide Web, ACM, Hyderabad, India, pp. 47-48, 2011. [DOI:10.1145/1963192.1963217]
[66] K. Jung, W. Heo, W. Chen. IRIE: Scalable and robust influence maximization in social networks. In Proceedings of the 12th International Conference on Data Mining, IEEE, Brussels, Belgium, pp. 918-923, 2012. [DOI:10.1109/ICDM.2012.79]
[67] M. Kimura, K. Saito. Tractable models for information diffusion in social networks. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Springer, Berlin, Germany, pp. 259-271, 2006. [DOI:10.1007/11871637_27]
[68] W. Chen, Y. J. Wang, S. Y. Yang. Efficient influence maximization in social networks. In Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining, ACM, Paris, France, pp. 199-208, 2009. [DOI:10.1145/1557019.1557047]
[69] A. Goyal, W. Lu, L. V. S. Lakshmanan. SIMPATH: An efficient algorithm for influence maximization under the linear threshold model. In Proceedings of the 11th International Conference on Data Mining, IEEE, Vancouver, Canada, pp. 211-220, 2011. [DOI:10.1109/ICDM.2011.132]
[70] C. Borgs, M. Brautbar, J. Chayes, B. Lucier. Maximizing social influence in nearly optimal time. In Proceedings of the 25th Annual ACM-SIAM Symposium on Discrete Algorithms, Illinois, USA, pp. 946-957, 2014. [DOI:10.1137/1.9781611973402.70]
[71] Y. Z. Tang, X. K. Xiao, Y. C. Shi. Influence maximization: Near-optimal time complexity meets practical efficiency. In Proceedings of ACM SIGMOD International Conference on Management of Data, ACM, Snowbird, USA, pp. 75-86, 2014. [DOI:10.1145/2588555.2593670]
[72] V. V. Vazirani. Approximation Algorithms, Berlin Heidelberg, Germany: Springer, 2013. [DOI:10.1007/978-3-662-04565-7]
[73] Y. Z. Tang, Y. C. Shi, X. K. Xiao. Influence maximization in near-linear time: A martingale approach. In Proceedings of ACM SIGMOD International Conference on Management of Data, ACM, Melbourne, Australia, pp. 1539-1554, 2015. [DOI:10.1145/2723372.2723734]
[74] E. Cohen, D. Delling, T. Pajor, R. F. Werneck. Sketch-based influence maximization and computation: Scaling up with guarantees. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, ACM, Shanghai, China, pp. 629-638, 2014. [DOI:10.1145/2661829.2662077]
[75] H. T. Nguyen, M. T. Thai, T. N. Dinh. Stop-and-stare: Optimal sampling algorithms for viral marketing in billion-scale networks. In Proceedings of International Conference on Management of Data, ACM, San Francisco, USA, pp. 695-710, 2016. [DOI:10.1145/2882903.2915207]
[76] K. K. Huang, S. B. Wang, G. Bevilacqua, X. K. Xiao, L. V. S. Lakshmanan. Revisiting the stop-and-stare algorithms for influence maximization. Proceedings of the VLDB Endowment, vol. 10, no. 9, pp. 913-924, 2017. DOI: 10.14778/3099622.3099623. [DOI:10.14778/3099622.3099623]
[77] Y. Wang, G. Cong, G. J. Song, K. Q. Xie. Community-based greedy algorithm for mining top-K influential nodes in mobile social networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Washington DC, USA, pp. 1039-1048, 2010. [DOI:10.1145/1835804.1835935]
[78] Z. F. Wang, H. Wang, Q. Liu, E. H. Chen. Influential nodes selection: A data reconstruction perspective. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, ACM, Gold Coast, Australia, pp. 879-882, 2014. [DOI:10.1145/2600428.2609464] Proceedings of the 37th International ACM SIGIR Conference on Research target="_blank">
[79] Q. Y. Jiang, G. J. Song, G. Cong, Y. Wang, W. J. Si, K. Q. Xie. Simulated annealing based influence maximization in social networks. In Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI, San Francisco, USA, pp. 127-132, 2011.
[80] M. G. Rodriguez, B. Schölkopf. Influence maximization in continuous time diffusion networks. https://arxiv.org/abs/1205.1682, 2012.
[81] Y. K. Wang, J. H. Zhu, Q. Ming. Incremental influence maximization for dynamic social networks. In Proceedings of the 3rd International Conference of Pioneering Computer Scientists, Engineers and Educators, Springer, Changsha, China, pp. 13-27, 2017. [DOI:10.1007/978-981-10-6388-6_2]
[82] E. Güney. On the optimal solution of budgeted influence maximization problem in social networks. Operational Research, to be published. [DOI:10.1007/s12351-017-0305-x]
[83] H. Nguyen, R. Zheng. On budgeted influence maximization in social networks. IEEE Journal on Selected Areas in Communications, vol. 31, no. 6, pp. 1084-1094, 2013. DOI: 10.1109/JSAC.2013.130610. [DOI:10.1109/JSAC.2013.130610]
[84] Y. Yang, X. B. Mao, J. Pei, X. F. He. Continuous influence maximization: What discounts should we offer to social network users? In Proceedings of International Conference on Management of Data, ACM, San Francisco, USA, pp. 727-741, 2016. [DOI:10.1145/2882903.2882961]
[85] C. Aslay, F. Bonchi, L. V. S. Lakshmanan, W. Lu. Revenue maximization in incentivized social advertising. Proceedings of the VLDB Endowment, vol. 10, no. 11, pp. 1238-1249, 2017. DOI: 10.14778/3137628.3137635. [DOI:10.14778/3137628.3137635]
[86] F. H. Li, C. T. Li, M. K. Shan. Labeled influence maximization in social networks for target marketing. In Proceedings of the 3rd International Conference on Privacy, Security, Risk and Trust and the IEEE 3rd International Conference on Social Computing, IEEE, Boston, USA, pp. 560-563, 2011. [DOI:10.1109/PASSAT/SocialCom.2011.152]
[87] F. S. Tang, Q. Liu, H. S. Zhu, E. H. Chen, F. D. Zhu. Diversified social influence maximization. In Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE, Beijing, China, pp. 455-459, 2014. [DOI:10.1109/ASONAM.2014.6921625]
[88] Q. Liu, Z. Dong, C. R. Liu, X. Xie, E. H. Chen, H. Xiong. Social marketing meets targeted customers: A typical user selection and coverage perspective. In Proceedings of IEEE International Conference on Data Mining, IEEE, Shenzhen, China, pp. 350-359, 2014. [DOI:10.1109/ICDM.2014.93]
[89] Z. F. Wang, E. H. Chen, Q. Liu, Y. Yang, Y. Ge, B. Chang. Maximizing the coverage of information propagation in social networks. In Proceedings of the 24th International Conference on Artificial Intelligence, AAAI, Buenos Aires, Argentina, pp. 2104-2110, 2015.
[90] Z. F. Wang, Y. Yang, J. Pei, L. Y. Chu, E. H. Chen. Activity maximization by effective information diffusion in social networks. IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 11, pp. 2374-2387, 2017. DOI: 10.1109/TKDE.2017.2740284. [DOI:10.1109/TKDE.2017.2740284]
[91] X. R. He, G. J. Song, W. Chen, Q. Y. Jiang. Influence blocking maximization in social networks under the competitive linear threshold model. In Proceedings of SIAM International Conference on Data Mining, SIAM, Anaheim, USA, pp. 463-474, 2012. [DOI:10.1137/1.9781611972825.40]
[92] J. H. Zhao, Q. P. Liu, L. Wang, X. F. Wang. Competitiveness maximization on complex networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, to be published. [DOI:10.1109/TSMC.2016.2636240]
[93] G. W. Ma, Q. Liu, E. H. Chen, B. Xiang. Individual influence maximization via link recommendation. In Proceedings of the 16th International Conference on Web-age Information Management, Springer, Qingdao, China, pp. 42-56, 2015. [DOI:10.1007/978-3-319-21042-1_4]
[94] J. J. Jiang, S. Wen, S. Yu, Y. Xiang, W. L. Zhou. Identifying propagation sources in networks: State-of-the-art and comparative studies. IEEE Communications Surveys & Tutorials, vol. 19, no. 1, pp. 465-481, 2017. DOI: 10.1109/COMST.2016.2615098. [DOI:10.1109/COMST.2016.2615098] IEEE Communications Surveys target="_blank">
[95] W. X. Dong, W. Y. Zhang, C. W. Tan. Rooting out the rumor culprit from suspects. In Proceedings of IEEE International Symposium on Information Theory, IEEE, Istanbul, Turkey, pp. 2671-2675, 2013. [DOI:10.1109/ISIT.2013.6620711]
[96] Z. X. Wang, W. X. Dong, W. Y. Zhang, C. W. Tan. Rooting our rumor sources in online social networks: The value of diversity from multiple observations. IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 4, pp. 663-677, 2015. DOI: 10.1109/JSTSP.2015.2389191. [DOI:10.1109/JSTSP.2015.2389191]
[97] W. Q. Luo, W. P. Tay, M. Leng. Identifying infection sources and regions in large networks. IEEE Transactions on Signal Processing, vol. 61, no. 11, pp. 2850-2865, 2013. DOI: 10.1109/TSP.2013.2256902. [DOI:10.1109/TSP.2013.2256902]
[98] 参考文献
[99] X. M. Zhai, W. L. Wu, W. Xu. Cascade source inference in networks: A Markov chain monte Carlo approach. Computational Social Networks, vol. 2, no. 1, pp. 17, 2015. DOI: 10.1186/s40649-015-0017-4. [DOI:10.1186/s40649-015-0017-4]
[100] L. Zhang, T. Y. Jin, T. Xu, B. Chang, Z. F. Wang, E. H. Chen. A Markov chain monte Carlo approach for source detection in networks. In Proceedings of the 6th National Conference on Social Media Processing, Springer, Beijing, China, pp. 77-88, 2017. [DOI:10.1007/978-981-10-6805-8_7]
[101] H. T. Nguyen, P. Ghosh, M. L. Mayo, T. N. Dinh. Multiple infection sources identification with provable guarantees. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, ACM, Indianapolis, USA, pp. 1663-1672, 2016. [DOI:10.1145/2983323.2983817]
[102] S. Feizi, K. Duffy, M. Kellis, M. Médard. Network infusion to infer information sources in networks, Technical Report MIT-CSAIL-TR-2014-028, Computer Science and Artificial Intelligence Laboratory, USA, 2014.
[103] R. Pena, X. Bresson, P. Vandergheynst. Source localization on graphs via L1 recovery and spectral graph theory. In Proceedings of the 12th Image, Video, and Multidimensional Signal Processing Workshop, IEEE, Bordeaux, France, 2016. [DOI:10.1109/IVMSPW.2016.7528230]
[104] K. Zhu, L. Ying. Information source detection in networks: Possibility and impossibility results. In Proceedings of the 35th Annual IEEE International Conference on Computer Communications, IEEE, San Francisco, USA, 2016. [DOI:10.1109/INFOCOM.2016.7524363]
[105] K. Zhu, L. Ying. Information source detection in the SIR model: A sample-path-based approach. IEEE/ACM Transactions on Networking, vol. 24, no. 1, pp. 408-421, 2016. DOI: 10.1109/TNET.2014.2364972. [DOI:10.1109/TNET.2014.2364972]
[106] K. Zhu, L. Ying. A robust information source estimator with sparse observations. Computational Social Networks, vol. 1, no. 1, pp. 3, 2014. DOI: 10.1186/s40649-014-0003-2. [DOI:10.1186/s40649-014-0003-2]
[107] W. Q. Luo, W. P. Tay. Finding an infection source under the sis model. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Vancouver, Canada, pp. 2930-2934, 2013. [DOI:10.1109/ICASSP.2013.6638194]
[108] W. Q. Luo, W. P. Tay, M. Leng. How to identify an infection source with limited observations. IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 4, pp. 586-597, 2014. DOI: 10.1109/JSTSP.2014.2315533. [DOI:10.1109/JSTSP.2014.2315533]
[109] A. Y. Lokhov, M. Mézard, H. Ohta, L. Zdeborová. Inferring the origin of an epidemic with a dynamic message-passing algorithm. Physical Review E, vol 90, no. 1, Article number 012801, 2014. [DOI:10.1103/PhysRevE.90.012801]
[110] W. H. Hu, W. P. Tay, A. Harilal, G. X. Xiao. Network infection source identification under the SIRI model. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Brisbane, Australia, pp. 1712-1716, 2015. [DOI:10.1109/ICASSP.2015.7178263]
[111] F. Altarelli, A. Braunstein, L. Dall'Asta, A. Lage-Castellanos, R. Zecchina. Bayesian inference of epidemics on networks via belief propagation. Physical Review Letters, vol. 112, no. 11, Article number 118701, 2014. DOI: 10.1103/PhysRevLett.112.118701. [DOI:10.1103/PhysRevLett.112.118701]
[112] F. Altarelli, A. Braunstein, L. Dall'Asta, A. Ingrosso, R. Zecchina. The patient-zero problem with noisy observations. Journal of Statistical Mechanics: Theory and Experiment, vol. 2014, no. 10, Article number 10016, 2014. DOI: 10.1088/1742-5468/2014/10/P10016. [DOI:10.1088/1742-5468/2014/10/P10016]
[113] W. Y. Zang, P. Zhang, C. Zhou, L. Guo. Discovering multiple diffusion source nodes in social networks. Procedia Computer Science, vol. 29, pp. 443-452, 2014. DOI: 10.1016/j.procs.2014.05.040. [DOI:10.1016/j.procs.2014.05.040]
[114] Z. Feng, P. Gundecha, H. Liu. Recovering information recipients in social media via provenance. In Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE, Niagara Falls, Canada, pp. 706-711, 2013. [DOI:10.1109/ASONAM.2013.6785780]
[115] N. Karamchandani, M. Franceschetti. Rumor source detection under probabilistic sampling. In Proceedings of IEEE International Symposium on Information Theory, IEEE, Istanbul, Turkey, pp. 2184-2188, 2013. [DOI:10.1109/ISIT.2013.6620613]
[116] D. Brockmann, D. Helbing. The hidden geometry of complex, network-driven contagion phenomena. Science, vol. 342, no. 6164, pp. 1337-1342, 2013. DOI: 10.1126/science.1245200. [DOI:10.1126/science.1245200]
[117] C. Y. Shi, Q. Zhang, T. G. Chu. Source identification of network diffusion processes with partial observations. In Proceedings of the 36th Chinese Control Conference, IEEE, Dalian, China, pp. 11296-11300, 2017. [DOI:10.23919/ChiCC.2017.8029159]
[118] P. Zhang, J. He, G. D. Long, G. Y. Huang, C. Q. Zhang. Towards anomalous diffusion sources detection in a large network. ACM Transactions on Internet Technology, vol. 16, no. 1, pp. 24, 2016. DOI: 10.1145/2806889 [DOI:10.1145/2806889]
[119] P. C. Pinto, P. Thiran, M. Vetterli. Locating the source of diffusion in large-scale networks. Physical Review Letters, vol. 109, no. 6, Article number 068702, 2012. DOI: 10.1103/PhysRevLett.109.068702. [DOI:10.1103/PhysRevLett.109.068702]
[120] A. Agaskar, Y. M. Lu. A fast Monte Carlo algorithm for source localization on graphs. In Proceedings of SPIE 8858, Wavelets and Sparsity XV, SPIE, San Diego, USA, 2013. [DOI:10.1117/12.2023039]
[121] Z. S. Shen, S. N. Cao, W. X. Wang, Z. R. Di, H. E. Stanley. Locating the source of diffusion in complex networks by time-reversal backward spreading. Physical Review E, vol. 93, no. 3, Article number 032301, 2016. DOI: 10.1103/PhysRevE.93.032301. [DOI:10.1103/PhysRevE.93.032301]
[122] L. Fu, Z. S. Shen, W. X. Wang, Y. Fan, Z. R. Di. Multi-source localization on complex networks with limited observers. Europhysics Letters, vol. 113, no. 1, Article number 18006, 2016. [DOI:10.1209/0295-5075/113/18006]
[123] E. Seo, P. Mohapatra, T. Abdelzaher. Identifying rumors and their sources in social networks. In Proceedings of Volume 8389, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR Ⅲ, SPIE, Baltimore, USA, 2012. [DOI:10.1117/12.919823]
[124] H. S. Wang, P. Zhang, L. Chen, H. Liu, C. Q. Zhang. Online diffusion source detection in social networks. In Proceedings of International Joint Conference on Neural Networks, IEEE, Killarney, Ireland, pp. 1-8, 2015. [DOI:10.1109/IJCNN.2015.7280455]
[125] A. Louni, S. Anand, K. P. Subbalakshmi. Identification of source of rumors in social networks with incomplete information. https://arxiv.org/abs/1509.00557, 2015.
[126] L. Bulteau, S. Fafianie, V. Froese, R. Niedermeier, N. Talmon. The complexity of finding effectors. Theory of Computing Systems, vol. 60, no. 2, pp. 253-279, 2017. DOI: 10.1007/s00224-016-9670-8. [DOI:10.1007/s00224-016-9670-8]
[127] D. T Nguyen, N. P. Nguyen, M. T. Thai. Sources of misinformation in online social networks: Who to suspect? In Proceedings of IEEE Military Communications Conference, IEEE, Orlando, USA, 2012. [DOI:10.1109/MILCOM.2012.6415780]
[128] S. A. Myers, C. G. Zhu, J. Leskovec. Information diffusion and external influence in networks. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Beijing, China, pp. 33-41, 2012. [DOI:10.1145/2339530.2339540]
[129] Q. Y. Zhan, J. W. Zhang, S. Z. Wang, P. S. Yu, J. Y. Xie. Influence maximization across partially aligned heterogenous social networks. In Proceedings of the 19th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Springer, Ho Chi Minh City, Vietnam, pp. 58-69, 2015.
[130] D. F. Du, H. Wang, T. Xu, Y. N. Lu, Q. Liu, E. H. Chen. Solving link-oriented tasks in signed network via an embedding approach. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Banff, Canada, pp. 75-80, 2017.
[131] D. X. Wang, P. Cui, W. W. Zhu. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco, USA, pp. 1225-1234, 2016.
[132] F. Liu, B. Q. Liu, C. J. Sun, M. Liu, X. L. Wang. Deep learning approaches for link prediction in social network services. In Proceedings of the 20th International Conference on Neural Information Processing, Springer, Daegu, Korea, pp. 425-432, 2013.
[133] S. Bourigault, S. Lamprier, P. Gallinari. Learning distributed representations of users for source detection in online social networks. In Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Riva del Garda, Italy, pp. 265-281, 2016.
[134] L. Wu, Y. Ge, Q. Liu, E. H. Chen, R. C. Hong, J. P. Du, M. Wang. Modeling the evolution of users' preferences and social links in social networking services. IEEE Transactions on Knowledge and Data Engineering, vol.29, no.6, pp.1240-1253, 2017. DOI:10.1109/TKDE.2017.2663422
[135] T. Zhang, R. Z. Qin, Q. L. Dong, W. Gao, H. R. Xu, Z. Y. Hu. Physiognomy:Personality traits prediction by learning. International Journal of Automation and Computing, vol.14, no.4, pp.386-395, 2017. DOI:10.1007/s11633-017-1085-8
[136] G. W. Ma, Q. Liu, L. Wu, E. H. Chen. Identifying hesitant and interested customers for targeted social marketing. In Proceedings of the 19th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Springer, Ho Chi Minh City, Vietnam, pp. 576-590, 2015.
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