doi:10.1017/S0269888924000109

Abdullah , S. & Wu , X. 2011. An epidemic model for news spreading on twitter. In 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, 163–169. IEEE.

Aggarwal , C. C., Lin , S. & Yu , P. S. 2012. On influential node discovery in dynamic social networks. In SDM.

Al Hasan , M., Chaoji , V., Salem , S. & Zaki , M. 2006. Link prediction using supervised learning. In SDM06: Workshop on Link Analysis, Counter-Terrorism and Security, 30, 798–805.

Aleta , A., Tuninetti , M., Paolotti , D., Moreno , Y. & Starnini , M. 2020. Link prediction in multiplex networks via triadic closure. Physical Review Research 2(4), 042029.

Ally , A. F. & Zhang , N. 2018. Effects of rewiring strategies on information spreading in complex dynamic networks. Communications in Nonlinear Science and Numerical Simulation 57, 97–110.

Alvari , H., Hajibagheri , A. & Sukthankar , G. 2014. Community detection in dynamic social networks: A game-theoretic approach. In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 101–107. IEEE.

Arora , A., Galhotra , S. & Ranu , S. 2017. Debunking the myths of influence maximization: An in-depth benchmarking study. In Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD’17, 651–666. ACM. http://doi.acm.org/10.1145/3035918.3035924

Aslan , S., Kaya , B. & Kaya , M. 2019. Predicting potential links by using strengthened projections in evolving bipartite networks. Physica A: Statistical Mechanics and its Applications 525, 998–1011.

Ayoub , J., Lotfi , D., El Marraki , M. & Hammouch , A. 2020. Accurate link prediction method based on path length between a pair of unlinked nodes and their degree. Social Network Analysis and Mining 10(1), 1–13.

Azaouzi , M., Mnasri , W. & Romdhane , L. B. 2021. New trends in influence maximization models. Computer Science Review 40, 100393.

Backstrom , L. & Leskovec , J. 2011. Supervised random walks: Predicting and recommending links in social networks. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, 635–644.

Barbieri , N., Bonchi , F. & Manco , G. 2012. Topic-aware social influence propagation models. In 2012 IEEE 12th International Conference on Data Mining, 81–90.

Beigi , G., Ranganath , S. & Liu , H. 2019. Signed link prediction with sparse data: The role of personality information. In Companion Proceedings of The 2019 World Wide Web Conference, 1270–1278.

Bhagat , S., Goyal , A. & Lakshmanan , L. V. 2012. Maximizing product adoption in social networks. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM’12, 603–612. ACM. http://doi.acm.org/10.1145/2124295.2124368

Bharathi , S., Kempe , D. & Salek , M. 2007. Competitive influence maximization in social networks. In Internet and Network Economics, Deng , X. & Graham , F. C. (eds). Springer Berlin Heidelberg, 306–311.

Bhattacharya , S. & Sarkar , D. 2021. Study on information diffusion in online social network. In Proceedings of International Conference on Frontiers in Computing and Systems, 279–288. Springer.

Bhattacherjee , A. & Sanford , C. 2006. Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly 30(4), 805–825.

Bliss , C. A., Frank , M. R., Danforth , C. M. & Dodds , P. S. 2014. An evolutionary algorithm approach to link prediction in dynamic social networks. Journal of Computational Science 5(5), 750–764.

Bo , C., Tang , X.-y., Ling, Y. & LIU, Y.-s. 2014. Identifying method for opinion leaders in social network based on competency model. Journal on Communications 35(11), 12.

Borgs , C., Brautbar , M., Chayes , J. & Lucier , B. 2014. Maximizing social influence in nearly optimal time. In Proceedings of the Twenty-fifth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA’14. Society for Industrial and Applied Mathematics, 946–957. http://dl.acm.org/citation.cfm?id=2634074.2634144

Borodin , A., Filmus , Y. & Oren , J. 2010. Threshold models for competitive influence in social networks. In Internet and Network Economics, Saberi, A. (ed.), 539–550. Springer Berlin Heidelberg.

Bozorgi , A., Samet , S., Kwisthout , J. & Wareham , T. 2017. Community-based influence maximization in social networks under a competitive linear threshold model. Knowledge-Based Systems 134, 149–158. http://www.sciencedirect.com/science/article/article/pii/S0950705117303519

Braun , N. 1995. Individual thresholds and social diffusion. Rationality and Society 7(2), 167–182.

Buccafurri , F., Lax , G., Nocera , A. & Ursino , D. 2012. Discovering links among social networks. In Machine Learning and Knowledge Discovery in Databases, Flach , P. A., De Bie , T. & Cristianini , N. (eds). Springer Berlin Heidelberg, 467–482.

Cao , Z., Wang , L. & De Melo , G. 2018. Link prediction via subgraph embedding-based convex matrix completion. In Proceedings of the AAAI Conference on Artificial Intelligence , 32.

Carnes , T., Nagarajan , C., Wild , S. M. & van Zuylen , A. 2007. Maximizing influence in a competitive social network: A follower’s perspective. In Proceedings of the Ninth International Conference on Electronic Commerce, ICEC’07, ACM, 351–360. http://doi.acm.org/10.1145/1282100.1282167

Chakraborty , T., Dalmia , A., Mukherjee , A. & Ganguly , N. 2017. Metrics for community analysis: A survey. ACM Computing Surveys (CSUR) 50(4), 1–37.

Chakraborty , T., Srinivasan , S., Ganguly , N., Mukherjee , A. & Bhowmick , S. 2014. On the permanence of vertices in network communities. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1396–1405.

Chang , B., Xu , T., Liu , Q. & Chen , E.-H. 2018. Study on information diffusion analysis in social networks and its applications. International Journal of Automation and Computing 15(4), 377–401.

Chen , J., Xu , X., Wu , Y. & Zheng , H. 2018. Gc-lstm: Graph convolution embedded lstm for dynamic link prediction. arXiv preprint arXiv:1812.04206.

Chen , M., Mao , S. & Liu , Y. 2014. Big data: A survey. Mobile Networks and Applications 19(2), 171–209.

Chen , W. 2011. Discovering communities by information diffusion. In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2, 1123–1132. IEEE.

Chen , W., Collins , A., Cummings , R., Ke , T., Liu , Z., Rincon , D., Sun , X., Wei , W., Wang , Y. & Yuan , Y. 2011. Influence maximization in social networks when negative opinions may emerge and propagate. In Proceedings of the 2011 SIAM International Conference on Data Mining (SDM’2011).

Chen , W., Lu , W. & Zhang , N. 2012. Time-critical influence maximization in social networks with time-delayed diffusion process.

Chen , W., Wang , C. & Wang , Y. 2010. 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, KDD’10, 1029–1038. ACM.

Chen , W., Wang , Y. & Yang , S. 2009. Efficient influence maximization in social networks. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’09, 199–208. ACM.

Chen , W., Yuan , Y. & Zhang , L. 2010. Scalable influence maximization in social networks under the linear threshold model. In 2010 IEEE International Conference on Data Mining , 88–97.

Cheng , S., Shen , H., Huang , J., Chen , W. & Cheng , X. 2014. Imrank: Influence maximization via finding self-consistent ranking. CoRR abs/1402.3939. http://arxiv.org/abs/1402.3939

Cheng , S., Shen , H., Huang , J., Zhang , G. & Cheng , X. 2013. Staticgreedy: Solving the scalability-accuracy dilemma in influence maximization. In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management, CIKM’13, 509–518. ACM. http://doi.acm.org/10.1145/2505515.2505541

Chhabra , A., Vashishth , V. & Sharma , D. K. 2017. A game theory based secure model against black hole attacks in opportunistic networks. In 2017 51st Annual Conference on Information Sciences and Systems (CISS), 1–6. IEEE.

Chicco , D., Tötsch , N. & Jurman , G. 2021. The matthews correlation coefficient (mcc) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Mining 14(1), 1–22.

Clauset , A., Moore , C. & Newman , M. E. 2008. Hierarchical structure and the prediction of missing links in networks. Nature 453(7191), 98–101.

Cohen , E., Delling , D., Pajor , T. & Werneck , R. F. 2014. Sketch-based influence maximization and computation: Scaling up with guarantees. CoRR abs/1408.6282. http://arxiv.org/abs/1408.6282

Daneshmand , S. M., Javari , A., Abtahi , S. E. & Jalili , M. 2015. A time-aware recommender system based on dependency network of items. The Computer Journal 58(9), 1955–1966.

Das , A. & Kempe , D. 2011. Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection. Computing Research Repository - CORR.

Das , S. & Biswas , A. 2021a. Community detection in social networks using local topology and information exchange. In 2021 International Conference on Intelligent Technologies (CONIT), 1–7. IEEE.

Das , S. & Biswas , A. 2021b. Deployment of information diffusion for community detection in online social networks: A comprehensive review. IEEE Transactions on Computational Social Systems.

Devi , K. & Tripathi , R. 2020. Information diffusion within a limited budget using node centralities and community detection. In 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS), 197–202. IEEE.

Dey , P., Goel , K. & Agrawal , R. 2020. P-simrank: Extending simrank to scale-free bipartite networks. In Proceedings of The Web Conference 2020, 3084–3090.

Divakaran , A. & Mohan , A. 2020. Temporal link prediction: A survey. New Generation Computing 38(1), 213–258.

Easley , D., Kleinberg , J. et al. 2012. Networks, crowds, and markets: Reasoning about a highly connected world. Significance 9(1), 43–44.

Eiselt , H. & Laporte , G. 1989. Competitive spatial models. European Journal of Operational Research 39(3), 231–242. http://www.sciencedirect.com/science/article/pii/0377221789901616

Elsweiler , D., Ruthven , I. & Jones , C. 2007. Towards memory supporting personal information management tools. Journal of the American Society for Information Science and Technology 58(7), 924–946.

Erlandsson , F., Bródka , P. & Borg , A. 2017. Seed selection for information cascade in multilayer networks. CoRR abs/1710.04391. http://arxiv.org/abs/1710.04391

Fan , T., Xiong , S., Zhao , W. & Yu , T. 2019. Information spread link prediction through multi-layer of social network based on trusted central nodes. Peer-to-Peer Networking and Applications 12, 1028–1040.

Fawcett , T. 2006. An introduction to roc analysis. Pattern Recognition Letters 27(8), 861–874.

Fire , M., Tenenboim-Chekina , L., Puzis , R., Lesser , O., Rokach , L. & Elovici , Y. 2014. Computationally efficient link prediction in a variety of social networks. ACM Transactions on Intelligent Systems and Technology (TIST) 5(1), 1–25.

Fortunato , S. 2007. ‘Barthã©’ lemy m, Resolution limit in community detection. Proceedings of the National Academy of Sciences of the United States of America 104(1), 36–41.

Fortunato , S. 2010. Community detection in graphs. Physics reports 486(3–5), 75–174.

Freeman , L. C. 1978. Centrality in social networks conceptual clarification. Social Networks 1(3), 215–239. http://www.sciencedirect.com/science/article/pii/0378873378900217

Fukumizu , K., Bach , F. R. & Jordan , M. I. 2004. Dimensionality reduction for supervised learning with reproducing kernel hilbert spaces. Journal of Machine Learning Research 5, 73–99.

Galhotra , S., Arora , A. & Roy , S. 2016. Holistic influence maximization: Combining scalability and efficiency with opinion-aware models. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD’16, 743–758. ACM. http://doi.acm.org/10.1145/2882903.2882929

Ge , H., Huang , J., Di , C., Li , J. & Li , S. 2017. Learning automata based approach for influence maximization problem on social networks. In 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC), 108–117.

Gionis , A., Terzi , E. & Tsaparas , P. 2013. Opinion maximization in social networks, CoRR abs/1301.7455. http://arxiv.org/abs/1301.7455

Girvan , M. & Newman , M. E. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12), 7821–7826.

Goldenberg , J., Libai , B. & Muller , E. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12(3), 211–223.

GoldenbergJ , L. 2002. Mullere. Ridingthe Saddle: How Cross-marketcommunicationscan Createa Majorslumpinsales 66(2), 1–16.

Golzardi , E., Sheikhahmadi , A. & Abdollahpouri , A. 2019. Detection of trust links on social networks using dynamic features. Physica A: Statistical Mechanics and its Applications 527, 121269.

Gomez-Rodriguez , M., Balduzzi , D. & Schölkopf , B. 2011. Uncovering the temporal dynamics of diffusion networks. CoRR abs/1105.0697. http://arxiv.org/abs/1105.0697

Gong , M., Yan , J., Shen , B., Ma , L. & Cai , Q. 2016. Influence maximization in social networks based on discrete particle swarm optimization. Information Sciences 367-368, 600–614. http://www.sciencedirect.com/science/article/pii/S002002551630500X

Goyal , A., Lu , W. & Lakshmanan , L. V. 2011a. Celf++: Optimizing the greedy algorithm for influence maximization in social networks. In Proceedings of the 20th International Conference Companion on World Wide Web, WWW’11, 47–48. ACM.

Goyal , A., Lu , W. & Lakshmanan , L. V. S. 2011b. Simpath: An efficient algorithm for influence maximization under the linear threshold model. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining, ICDM’11, 211–220. IEEE Computer Society.

Granovetter , M. 1978. Threshold models of collective behavior. American Journal of Sociology 83(6), 1420–1443.

Granovetter , M. S. 1973. The strength of weak ties. American Journal of sociology 78(6), 1360–1380.

Greenhalgh , T., Stramer , K., Bratan , T., Byrne , E., Mohammad , Y. & Russell , J. 2008. Introduction of shared electronic records: Multi-site case study using diffusion of innovation theory. BMJ 337.

Grover , A. & Leskovec , J. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 855–864.

Guille , A., Hacid , H., Favre , C. & Zighed , D. A. 2013. Information diffusion in online social networks: A survey. ACM Sigmod Record 42(2), 17–28.

Guo , J., Zhang , P., Zhou , C., Cao , Y. & Guo , L. 2013. Personalized influence maximization on social networks. In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management, CIKM’13, ACM, 199–208. http://doi.acm.org/10.1145/2505515.2505571

Guo , Y., Huang , Z., Kong , Y. & Wang , Q. 2021. Modularity and mutual information in networks: Two sides of the same coin. arXiv preprint arXiv:2103.02542.

Hajibagheri , A., Alvari , H., Hamzeh , A. & Hashemi , S. 2012. Community detection in social networks using information diffusion. In 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 702–703. IEEE.

Hajibagheri , A., Hamzeh , A. & Sukthankar , G. 2013. Modeling information diffusion and community membership using stochastic optimization. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 175–182.

Han , K., Xu , C., Gui , F., Tang , S., Huang , H. & Luo , J. 2018. Discount allocation for revenue maximization in online social networks. In Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, Mobihoc’18, 121–130, ACM. http://doi.acm.org/10.1145/3209582.3209595

Hargittai , E. & Walejko , G. 2008. The participation divide: Content creation and sharing in the digital age. Information, Community and Society 11(2), 239–256.

He , L., Guo , W., Chen , Y., Guo , K. & Zhuang , Q. 2021. Discovering overlapping communities in dynamic networks based on cascade information diffusion. IEEE Transactions on Computational Social Systems 9(3), 794–806.

He , X. & Kempe , D. 2014. Stability of influence maximization. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’14 , 1256–1265. ACM. http://doi.acm.org/10.1145/2623330.2623746

He , X. & Kempe , D. 2016. Robust influence maximization. CoRR abs/1602.05240. http://arxiv.org/abs/1602.05240

Jiang , C., Chen , Y. & Liu , K. R. 2014. Graphical evolutionary game for information diffusion over social networks. IEEE Journal of Selected Topics in Signal Processing 8(4), 524–536.

Jiang , Q., Song , G., Cong , G., Wang , Y., Si , W. & Xie , K. 2011. Simulated annealing based influence maximization in social networks. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI’11, 127–132. AAAI Press. http://dl.acm.org/citation.cfm?id=2900423.2900443

Jung , K., Heo , W. & Chen , W. 2012. Irie: Scalable and robust influence maximization in social networks. In Proceedings of the 2012 IEEE 12th International Conference on Data Mining, ICDM’12, 918–923. IEEE Computer Society.

Kalantari , H., Ghazanfari , M., Fathian , M. & Shahanaghi , K. 2020. Multi-objective optimization model in a heterogeneous weighted network through key nodes identification in overlapping communities. Computers & Industrial Engineering 144, 106413.

Kashima , H. & Abe , N. 2006. A parameterized probabilistic model of network evolution for supervised link prediction. In Sixth International Conference on Data Mining (ICDM’06) , 340–349. IEEE.

Katz , L. 1953. A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43.

Kempe , D., Kleinberg , J. & Tardos , E. 2003. Maximizing the spread of influence through a social network. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’03, 137–146. ACM.

Kempe , D., Kleinberg , J. & Tardos , É. 2005. Influential nodes in a diffusion model for social networks. In Automata, Languages and Programming, Caires , L., Italiano , G. F., Monteiro , L., Palamidessi , C. & Yung , M. (eds). Springer Berlin Heidelberg, 1127–1138.

Kermack , W. & McKendrick , A. 1991. Contributions to the mathematical theory of epidemics–i. Bulletin of Mathematical Biology 53(1), 33–55. http://www.sciencedirect.com/science/article/pii/S0092824005800400

Kermack , W. O. & McKendrick , A. G. 1927. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 115(772), 700–721. http://www.jstor.org/stable/94815

Kermack , W. O. & McKendrick , A. G. 1932. Contributions to the mathematical theory of epidemics. ii.–the problem of endemicity. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 138(834), 55–83.

Khan , A., Zehnder , B. & Kossmann , D. 2016. Revenue maximization by viral marketing: A social network host’s perspective. In 2016 IEEE 32nd International Conference on Data Engineering (ICDE), 37–48.

Khelil , A., Becker , C., Tian , J. & Rothermel , K. 2002. An epidemic model for information diffusion in manets. In Proceedings of the 5th ACM International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems, 54–60.

Kim , C. & Galliers , R. D. 2004. Toward a diffusion model for internet systems. Internet Research 14(2), 155–166.

Kim , J., Kim , S. K. & Yu , H. 2013. Scalable and parallelizable processing of influence maximization for large-scale social networks? In 2013 IEEE 29th International Conference on Data Engineering (ICDE), 266–277.

Kimura , M. & Saito , K. 2006. Tractable models for information diffusion in social networks. In Knowledge Discovery in Databases: PKDD 2006, Fürnkranz , J., Scheffer , T. & Spiliopoulou , M. (eds). Springer Berlin Heidelberg, 259–271.

Koren , Y., Bell , R. & Volinsky , C. 2009. Matrix factorization techniques for recommender systems. Computer 42(8), 30–37.

Kuhnle , A., Alim , M. A., Li , X., Zhang , H. & Thai , M. T. 2018. Multiplex influence maximization in online social networks with heterogeneous diffusion models. IEEE Transactions on Computational Social Systems 5, 418–429.

Kumar , A., Mishra , S., Singh , S. S., Singh , K. & Biswas , B. 2019a. Link prediction in complex networks based on significance of higher-order path index (shopi). Physica A: Statistical Mechanics and its Applications, 123790. http://www.sciencedirect.com/science/article/pii/S0378437119321107

Kumar , A., Singh , S. S., Singh , K. & Biswas , B. 2019b. Level-2 node clustering coefficient-based link prediction. Applied Intelligence 49(7), 2762–2779.

Kumar , A., Singh , S. S., Singh , K. & Biswas , B. 2020. Link prediction techniques, applications, and performance: A survey. Physica A: Statistical Mechanics and its Applications, 124289. http://www.sciencedirect.com/science/article/pii/S0378437120300856

Kumar , M., Mishra , S., Singh , S. S. & Biswas , B. 2024. Community-enhanced link prediction in dynamic networks. ACM Transactions on the Web 18(2). https://doi.org/10.1145/3580513

Kundu , S., Murthy , C. A. & Pal , S. K. 2011. A new centrality measure for influence maximization in social networks. In Pattern Recognition and Machine Intelligence, Kuznetsov , S. O., Mandal , D. P., Kundu , M. K. & Pal , S. K. (eds). Springer Berlin Heidelberg, 242–247.

Lee , J. & Chung , C. 2015. A query approach for influence maximization on specific users in social networks. IEEE Transactions on Knowledge and Data Engineering 27(2), 340–353.

Lee , W., Kim , J. & Yu , H. 2012. Ct-ic: Continuously activated and time-restricted independent cascade model for viral marketing. In 2012 IEEE 12th International Conference on Data Mining, 960–965.

Lei , S., Maniu , S., Mo , L., Cheng , R. & Senellart , P. 2015. Online influence maximization (extended version). CoRR abs/1506.01188. http://arxiv.org/abs/1506.01188

Leicht , E. A., Holme , P. & Newman , M. E. 2006. Vertex similarity in networks. Physical Review E–Statistical, Nonlinear, and Soft Matter Physics 73(2), 026120.

Leskovec , J., Adamic , L. & Huberman , B. A. 2007. The dynamics of viral marketing. In Proceedings of the 7th ACM Conference on Electronic Commerce, 228–237.

Leskovec , J., Huttenlocher , D. & Kleinberg , J. 2010. Predicting positive and negative links in online social networks. In Proceedings of the 19th International Conference on World Wide Web, 641–650.

Leskovec , J., Krause , A., Guestrin , C., Faloutsos , C., VanBriesen , J. & Glance , N. 2007. Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’07, 420–429. ACM.

Li , C.-T., Lin , S.-D. & Shan , M.-K. 2012. Influence propagation and maximization for heterogeneous social networks. In Proceedings of the 21st International Conference on World Wide Web, WWW’12 Companion, ACM, 559–560. http://doi.acm.org/10.1145/2187980.2188126

Li , D., Xu , Z.-M., Chakraborty , N., Gupta , A., Sycara , K. & Li , S. 2014. Polarity related influence maximization in signed social networks. PLOS ONE 9, 1–12. https://doi.org/10.1371/journal.pone.0102199

Li , D., Zhang , Y., Xu , Z., Chu , D. & Li , S. 2016. Exploiting information diffusion feature for link prediction in sina weibo. Scientific Reports 6(1), 1–8.

Li , G., Chen , S., Feng , J., Tan , K.-l. & Li, W.-s. 2014. Efficient location-aware influence maximization. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD’14, 87–98. ACM. http://doi.acm.org/10.1145/2588555.2588561

Li , H., Bhowmick , S. S. & Sun , A. 2011. Casino: Towards conformity-aware social influence analysis in online social networks. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM’11, ACM, 1007–1012. http://doi.acm.org/10.1145/2063576.2063721

Li , H., Bhowmick , S. S. & Sun , A. 2013. Cinema: Conformity-aware greedy algorithm for influence maximization in online social networks. In Proceedings of the 16th International Conference on Extending Database Technology, EDBT’13, 323–334. ACM. http://doi.acm.org/10.1145/2452376.2452415

Li , L., Liu , Y., Zhou , Q., Yang , W. & Yuan , J. 2020. Targeted influence maximization under a multifactor-based information propagation model. Information Sciences 519, 124–140. http://www.sciencedirect.com/science/article/pii/S0020025520300438

Li , S., Huang , J., Zhang , Z., Liu , J., Huang , T. & Chen , H. 2018. Similarity-based future common neighbors model for link prediction in complex networks. Scientific Reports 8(1), 1–11.

Li , W., Liu , W., Chen , T., Qu , X., Fang , Q. & Ko , K.-I. 2017. Competitive profit maximization in social networks. Theoretical Computer Science 694, 1–9. http://www.sciencedirect.com/science/article/pii/S0304397517305388

Li , Y., Chen , W., Wang , Y. & Zhang , Z. 2011. Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships. CoRR abs/1111.4729. http://arxiv.org/abs/1111.4729

Li , Y., Zhang , D. & Tan , K.-L. 2015. Real-time targeted influence maximization for online advertisements. Proceedings of the VLDB Endowment 8(10), 1070–1081.

Lin , T. Y., Ohsuga , S., Liau , C.-J. & Hu , X. 2005. Foundations and Novel Approaches in Data Mining, 9. Springer Science & Business Media.

Liu , B., Cong , G., Zeng , Y., Xu , D. & Chee , Y. M. 2014. Influence spreading path and its application to the time constrained social influence maximization problem and beyond. IEEE Transactions on Knowledge and Data Engineering 26(8), 1904–1917.

Liu , Q., Xiang , B., Chen , E., Xiong , H., Tang , F. & Yu , J. X. 2014. 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, CIKM’14, 171–180. ACM. http://doi.acm.org/10.1145/2661829.2662009

Liu , S. & Wang , S. 2016. Trajectory community discovery and recommendation by multi-source diffusion modeling. IEEE Transactions on Knowledge and Data Engineering 29(4), 898–911.

Loeliger , H.-A., Dauwels , J., Hu , J., Korl , S., Ping , L. & Kschischang , F. R. 2007. The factor graph approach to model-based signal processing. Proceedings of the IEEE 95(6), 1295–1322.

Lü , L., Jin , C.-H. & Zhou , T. 2009. Similarity index based on local paths for link prediction of complex networks. Physical Review E–Statistical, Nonlinear, and Soft Matter Physics 80(4), 046122.

Lu , W. & Lakshmanan , L. V. S. 2012. Profit maximization over social networks, CoRR abs/1210.4211.

Ma , C., Zhu , C., Fu , Y., Zhu , H., Liu , G. & Chen , E. 2017. Social user profiling: A social-aware topic modeling perspective. In International Conference on Database Systems for Advanced Applications, 610–622. Springer.

Mack , P. 1985. Diffusion of innovations by everett m. rogers. Technology and Culture 26(1), 109–110.

Magal , P. & Ruan , S. 2014. Susceptible-infectious-recovered models revisited: From the individual level to the population level. Mathematical Biosciences 250, 26–40.

Manapat , M. L. & Rand , D. G. 2012. Delayed and inconsistent information and the evolution of trust. Dynamic Games and Applications 2(4), 401–410.

Mehmood , Y., Bonchi , F. & Garca-Soriano , D. 2016. Spheres of influence for more effective viral marketing. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD’16, 711–726. ACM. http://doi.acm.org/10.1145/2882903.2915250

Meng , Y., Yi , Y., Xiong , F. & Pei , C. 2019. Txonehop approach for dynamic influence maximization problem. Physica A: Statistical Mechanics and its Applications 515, 575–586. http://www.sciencedirect.com/science/article/pii/S0378437118312937

Mishra , S., Singh , S. S., Kumar , A. & Biswas , B. 2022. Mnerlp-mul: Merged node and edge relevance based link prediction in multiplex networks. Journal of Computational Science 60, 101606. https://www.sciencedirect.com/science/article/pii/S1877750322000369

Mishra , S., Singh , S. S., Mishra , S. & Biswas , B. 2024. Multi-objective based unbiased community identification in dynamic social networks. Computer Communications 214, 18–32. https://www.sciencedirect.com/science/article/pii/S0140366423004188

Morstatter , F., Wu , L., Nazer , T. H., Carley , K. M. & Liu , H. 2016. A new approach to bot detection: Striking the balance between precision and recall. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) , 533–540, IEEE.

Mozafari , N. & Hamzeh , A. 2015. An enriched social behavioural information diffusion model in social networks. Journal of Information Science 41(3), 273–283.

Muhuri , S., Chakraborty , S. & Chakraborty , S. N. 2018. Extracting social network and character categorization from bengali literature. IEEE Transactions on Computational Social Systems 5(2), 371–381.

Muhuri , S. & Mukhopadhyay , D. 2021. A hypergraph clustering-based technique for detecting fake news from broadcasting network. In 2021 Asian Conference on Innovation in Technology (ASIANCON), 1–5. IEEE.

Nasiri , E., Berahmand , K., Rostami , M. & Dabiri , M. 2021. A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding. Computers in Biology and Medicine 137, 104772.

Nazemian , A. & Taghiyareh , F. 2012. Influence maximization in independent cascade model with positive and negative word of mouth. In 6th International Symposium on Telecommunications (IST), 854–860. IEEE.

Nguyen , D. T., Das , S. & Thai , M. T. 2013. Influence maximization in multiple online social networks. In 2013 IEEE Global Communications Conference (GLOBECOM), 3060–3065.

Nguyen , H. T., Dinh , T. N. & Thai , M. T. 2016. Cost-aware targeted viral marketing in billion-scale networks. In IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, 1–9.

Nguyen , H. T., Thai , M. T. & Dinh , T. N. 2016. Stop-and-stare: Optimal sampling algorithms for viral marketing in billion-scale networks. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD’16, 695–710. ACM. http://doi.acm.org/10.1145/2882903.2915207

Obregon , J., Song , M. & Jung , J.-Y. 2019. Infoflow: Mining information flow based on user community in social networking services. IEEE Access 7, 48024–48036.

Ohsaka , N., Akiba , T., Yoshida , Y. & Kawarabayashi , K.-I. 2014. Fast and accurate influence maximization on large networks with pruned monte-carlo simulations. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, AAAI’14 , 138–144. AAAI Press. http://dl.acm.org/citation.cfm?id=2893873.2893897

Ohsaka , N., Akiba , T., Yoshida , Y. & Kawarabayashi , K.-i. 2016. Dynamic influence analysis in evolving networks. Proceedings of the VLDB Endowment 9(12), 1077–1088. https://doi.org/10.14778/2994509.2994525

Page , L., Brin , S., Motwani , R. & Winograd , T. 1999. The pagerank citation ranking: Bringing order to the web, Technical Report 1999-66, Stanford InfoLab. Previous number = SIDL-WP-1999-0120. http://ilpubs.stanford.edu:8090/422/

Parshani , R., Carmi , S. & Havlin , S. 2010. Epidemic threshold for the susceptible-infectious-susceptible model on random networks. Physical Review Letters 104(25), 258701.

Pathak , N., Banerjee , A. & Srivastava , J. 2010. A generalized linear threshold model for multiple cascades. In 2010 IEEE International Conference on Data Mining, 965–970. IEEE.

Pecli , A., Giovanini , B., Pacheco , C. C., Moreira , C., Ferreira , F., Tosta , F., Tesolin , J., Dias , M. V., Lima Filho , S. P., Cavalcanti , M. C. et al. 2015. Dimensionality reduction for supervised learning in link prediction problems. In ICEIS (1), 295–302.

Perozzi , B., Al-Rfou , R. & Skiena , S. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 701–710.

Rajamma , R. K., Zolfagharian , M. A. & Pelton , L. E. 2011. Dimensions and outcomes of b2b relational exchange: A meta-analysis. Journal of Business & Industrial Marketing 26(2), 104–114.

Ramezani , M., Khodadadi , A. & Rabiee , H. R. 2018. Community detection using diffusion information. ACM Transactions on Knowledge Discovery from Data (TKDD) 12(2), 1–22.

Razaque , A., Rizvi , S., Almiani , M., Al Rahayfeh , A. et al. 2019. State-of-art review of information diffusion models and their impact on social network vulnerabilities. Journal of King Saud University-Computer and Information Sciences 34(1), 1275–1294.

Richardson , M. & Domingos , P. 2002. Mining knowledge-sharing sites for viral marketing. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’02, 61–70. ACM. http://doi.acm.org/10.1145/775047.775057

Rodriguez , M. G., Balduzzi , D. & Schölkopf , B. 2011. Uncovering the temporal dynamics of diffusion networks. arXiv preprint arXiv:1105.0697.

Ruan , Y., Fuhry , D. & Parthasarathy , S. 2013. Efficient community detection in large networks using content and links. In Proceedings of the 22nd International Conference on World Wide Web, 1089–1098.

Saito , K., Kimura , M., Ohara , K. & Motoda , H. 2012. Efficient discovery of influential nodes for sis models in social networks. Knowledge and Information Systems 30(3), 613–635.

Saito , K., Nakano , R. & Kimura , M. 2008. Prediction of information diffusion probabilities for independent cascade model. In International conference on knowledge-based and intelligent information and engineering systems, 67–75. Springer.

Saito , T. & Rehmsmeier , M. 2015. The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. PloS One 10(3), e0118432.

Sattari , M. & Zamanifar , K. 2018. A cascade information diffusion based label propagation algorithm for community detection in dynamic social networks. Journal of Computational Science 25, 122–133.

Schelling , T. C. 2006. Micromotives and Macrobehavior, WW Norton & Company.

Scholz , C., Atzmueller , M., Barrat , A., Cattuto , C. & Stumme , G. 2013. New insights and methods for predicting face-to-face contacts. In Seventh International AAAI Conference on Weblogs and Social Media.

Schütz , G. M., Brandaut , M. & Trimper , S. 2008. Exact solution of a stochastic susceptible-infectious-recovered model. Physical Review E 78(6), 061132.

Seddiki , M. S. & Frikha , M. 2012. A non-cooperative game theory model for bandwidth allocation in network virtualization. In 2012 15th International Telecommunications Network Strategy and Planning Symposium (NETWORKS), 1–6. IEEE.

Serban , I., Sordoni , A., Bengio , Y., Courville , A. & Pineau , J. 2016. Building end-to-end dialogue systems using generative hierarchical neural network models. In Proceedings of the AAAI Conference on Artificial Intelligence, 30.

Shen , C., Nishide , R., Piumarta , I., Takada , H. & Liang , W. 2015. Influence maximization in signed social networks. In Web Information Systems Engineering – WISE 2015, Wang , J., Cellary , W., Wang , D., Wang , H., Chen , S.-C., Li , T. & Zhang , Y. (eds). Springer International Publishing, 399–414.

Shen , K., Song , L., Yang , X. & Zhang , W. 2010. A hierarchical diffusion algorithm for community detection in social networks. In 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 276–283. IEEE.

Sheshar , S., Srivastva , S. D., Verma , M. & Singh , J. 2021. Influence maximization frameworks, performance, challenges and directions on social network: A theoretical study. Journal of King Saud University-Computer and Information Sciences 34(9), 7570–7603.

Singh , N. et al. 2019. Improved link prediction using pca. International Journal of Analysis and Applications 17(4), 578–585.

Singh , S. S., Kumar , A., Singh , K. & Biswas , B. 2019a. C2im: Community based context-aware influence maximization in social networks. Physica A: Statistical Mechanics and its Applications 514, 796–818. http://www.sciencedirect.com/science/article/pii/S0378437118312822

Singh , S. S., Kumar , A., Singh , K. & Biswas , B. 2019b. Lapso-im: A learning-based influence maximization approach for social networks. Applied Soft Computing, 105554. http://www.sciencedirect.com/science/article/pii/S1568494619303345

Singh , S. S., Kumar , A., Singh , K. & Biswas , B. 2020. Im-sso: Maximizing influence in social networks using social spider optimization. Concurrency and Computation: Practice and Experience 32(2), e5421. e5421 cpe.5421. https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5421

Singh , S. S., Mishra , S., Kumar , A. & Biswas , B. 2020. Clp-id: Community-based link prediction using information diffusion. Information Sciences 514, 402–433. http://www.sciencedirect.com/science/article/pii/S0020025519310734

Singh , S. S., Muhuri , S., Mishra , S., Srivastava , D., Shakya , H. K. & Kumar , N. 2024. Social network analysis: A survey on process, tools, and application. ACM Computing Surveys 56(8). https://doi.org/10.1145/3648470

Singh , S. S., Singh , K., Kumar , A. & Biswas , B. 2019a. Aco-im: Maximizing influence in social networks using ant colony optimization. Soft Computing, 1–23.

Singh , S. S., Singh , K., Kumar , A. & Biswas , B. 2019b. Mim2: Multiple influence maximization across multiple social networks. Physica A: Statistical Mechanics and its Applications 526, 120902. http://www.sciencedirect.com/science/article/pii/S037843711930500X

Singh , S. S., Singh , K., Kumar , A., Shakya , H. K. & Biswas , B. 2019. A survey on information diffusion models in social networks. In Advanced Informatics for Computing Research, Luhach , A. K., Singh , D., Hsiung , P.-A., Hawari , K. B. G., Lingras , P. & Singh , P. K. (eds). Springer Singapore, 426–439.

Singh , S. S., Srivastava , V., Kumar , A., Tiwari , S., Singh , D. & Lee , H.-N. 2023. Social network analysis: A survey on measure, structure, language information analysis, privacy, and applications. ACM Transactions on Asian and Low-Resource Language Information Processing 22(5). https://doi.org/10.1145/3539732

Singh , S. S., Srivastva , D., Kumar , A. & Srivastava , V. 2022. Flp-id: Fuzzy-based link prediction in multiplex social networks using information diffusion perspective. Knowledge-Based Systems 248, 108821. https://www.sciencedirect.com/science/article/pii/S0950705122003859

Singh , S. S., Srivastva , D., Verma , M. & Singh , J. 2021. Influence maximization frameworks, performance, challenges and directions on social network: A theoretical study. Journal of King Saud University - Computer and Information Sciences 34(9), 7570–7603. https://www.sciencedirect.com/science/article/pii/S1319157821002123

Singh , S. S., Muhuri , S. & Srivastava , V. 2024. B+ tree-inspired community-based link prediction in dynamic social networks. Arabian Journal for Science and Engineering 49, 4039–4060.

Song , D. & Meyer , D. A. 2015. Recommending positive links in signed social networks by optimizing a generalized auc. In Twenty-Ninth AAAI Conference on Artificial Intelligence .

Stanley , N., Bonacci , T., Kwitt , R., Niethammer , M. & Mucha , P. J. 2019. Stochastic block models with multiple continuous attributes. Applied Network Science 4(1), 1–22.

Su , S., Li , X., Cheng , X. & Sun , C. 2018. Location-aware targeted influence maximization in social networks. Journal of the Association for Information Science and Technology 69(2), 229–241. http:https://doi.org/10.1002/asi.23931

Suganya , T., Thennammai , S. & Velusamy , R. 2017. Unique user identification across multiple social network. International Journal of Research in Marketing 8, 137–142.

Sun , H., Gao , X., Chen , G., Gu , J. & Wang , Y. 2016. Multiple influence maximization in social networks. In Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication, IMCOM’16, 44:1–44:8. ACM.

Sun , Z., Sheng , J., Wang , B., Ullah , A. & Khawaja , F. 2020. Identifying communities in dynamic networks using information dynamics. Entropy 22(4), 425.

Sun , Z., Wang , B., Sheng , J., Yu , Z. & Shao , J. 2018. Overlapping community detection based on information dynamics. IEEE Access 6, 70919–70934.

Susarla , A., Oh , J.-H. & Tan , Y. 2012. Social networks and the diffusion of user-generated content: Evidence from youtube. Information Systems Research 23(1), 23–41.

Sviridenko , M. 2004. A note on maximizing a submodular set function subject to a knapsack constraint. Operations Research Letters 32(1), 41–43.

Tang , J., Tang , X. & Yuan , J. 2017. Towards profit maximization for online social network providers. CoRR abs/1712.08963.

Tang , J., Tang , X. & Yuan , J. 2018. Profit maximization for viral marketing in online social networks: Algorithms and analysis. IEEE Transactions on Knowledge and Data Engineering 30(6), 1095–1108.

Tang , J., Wu , S. & Sun , J. 2013. Confluence: Conformity influence in large social networks.

Tang , Y., Shi , Y. & Xiao , X. 2015. Influence maximization in near-linear time: A martingale approach. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD’15, ACM, 1539–1554. http://doi.acm.org/10.1145/2723372.2723734

Tang , Y., Xiao , X. & Shi , Y. 2014. Influence maximization: Near-optimal time complexity meets practical efficiency. In SIGMOD Conference.

Tong , G., Wu , W., Tang , S. & Du , D. 2017. Adaptive influence maximization in dynamic social networks. IEEE/ACM Transactions on Networking 25(1), 112–125.

Van Lierde , H., Chen , G. & Chow , T. W. 2019. Scalable spectral clustering for overlapping community detection in large-scale networks. IEEE Transactions on Knowledge and Data Engineering 32, 754–767.

Varshney , D., Kumar , S. & Gupta , V. 2014. Modeling information diffusion in social networks using latent topic information. In Intelligent Computing Theory, Huang , D.-S., Bevilacqua , V. & Premaratne , P. (eds), 137–148. Springer International Publishing.

Varshney , D., Kumar , S. & Gupta , V. 2017. Predicting information diffusion probabilities in social networks: A Bayesian networks based approach. Knowledge-Based Systems 133, 66–76.

Wang , C., Guan , X., Qin , T. & Zhou , Y. 2015. Modelling on opinion leader’s influence in microblog message propagation and its application. Journal of Software 26, 1473–1485.

Wang , C., Huang , P., Yang , D. & Chen , W. 2016. Cross-layer design of influence maximization in mobile social networks. CoRR abs/1604.02796. http://arxiv.org/abs/1604.02796

Wang , P., Xu , B., Wu , Y. & Zhou , X. 2015. Link prediction in social networks: The state-of-the-art. Science China Information Sciences 58(1), 1–38.

Wang , Q., Gong , M., Song , C. & Wang , S. 2017. Discrete particle swarm optimization based influence maximization in complex networks. In 2017 IEEE Congress on Evolutionary Computation (CEC), 488–494.

Wang , X., Zhang , Y., Zhang , W., Lin , X. & Chen , C. 2017. Bring order into the samples: A novel scalable method for influence maximization. IEEE Transactions on Knowledge and Data Engineering 29(2), 243–256.

Wang , Y., Cong , G., Song , G. & Xie , K. 2010. Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In KDD.

Wang , Y. & Feng , X. 2009. A potential-based node selection strategy for influence maximization in a social network. In Advanced Data Mining and Applications, Huang , R., Yang , Q., Pei , J., Gama , J., Meng , X. & Li , X. (eds), 350–361. Springer Berlin Heidelberg.

Wang , Y., Zhu , J. & Ming , Q. 2017. Incremental influence maximization for dynamic social networks. In Data Science, Zou , B., Han , Q., Sun , G., Jing , W., Peng , X. & Lu , Z. (eds). Springer Singapore, 13–27.

Wang , Z., Lei , Y. & Li , W. 2020. Neighborhood attention networks with adversarial learning for link prediction. IEEE Transactions on Neural Networks and Learning Systems 32(8), 3653–3663.

Wang , Z., Sun , C., Xi , J. & Li , X. 2021. Influence maximization in social graphs based on community structure and node coverage gain. Future Generation Computer Systems 118, 327–338.

Weersink , A. & Fulton , M. 2020. Limits to profit maximization as a guide to behavior change. Applied Economic Perspectives and Policy 42(1), 67–79. https://onlinelibrary.wiley.com/doi/abs/10.1002/aepp.13004

Williamson , S. A. 2016. Nonparametric network models for link prediction. The Journal of Machine Learning Research 17(1), 7102–7121.

Wu , J., Shen , J., Zhou , B., Zhang , X. & Huang , B. 2019. General link prediction with influential node identification. Physica A: Statistical Mechanics and its Applications 523, 996–1007.

Wu , X., Zhang , H., Zhao , X., Li , B. & Yang , C. 2015. Mining algorithm of microblogging opinion leaders based on user-behavior network. Applied Computing and Informatics 32, 2678–2683.

Xie , J., Kelley , S. & Szymanski , B. K. 2013. Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Computing Surveys (CSUR) 45(4), 43.

Xu , T., Zhu , H., Chen , E., Huai , B., Xiong , H. & Tian , J. 2014. Learning to annotate via social interaction analytics. Knowledge and Information Systems 41(2), 251–276.

Xu , T., Zhu , H., Zhao , X., Liu , Q., Zhong , H., Chen , E. & Xiong , H. 2016. 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, 1285–1294.

Yang , J., McAuley , J. & Leskovec , J. 2013. Community detection in networks with node attributes. In 2013 IEEE 13th International Conference on Data Mining, 1151–1156. IEEE.

Yang , W., Brenner , L. & Giua , A. 2018. Influence maximization by link activation in social networks. In 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), 1, 1248–1251.

Yang , Y., Lichtenwalter , R. N. & Chawla , N. V. 2015. Evaluating link prediction methods. Knowledge and Information Systems 45(3), 751–782.

Yao , L., Wang , L., Pan , L. & Yao , K. 2016. Link prediction based on common-neighbors for dynamic social network. Procedia Computer Science 83, 82–89.

Yao , Y., Zhang , R., Yang , F., Tang , J., Yuan , Y. & Hu , R. 2018. Link prediction in complex networks based on the interactions among paths. Physica A: Statistical Mechanics and its Applications 510, 52–67.

Yin , D., Hong , L. & Davison , B. D. 2011. Structural link analysis and prediction in microblogs. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, 1163–1168.

Zhan , Q., Zhang , J., Wang , S., Yu , P. S. & Xie , J. 2015. Influence maximization across partially aligned heterogenous social networks. In Advances in Knowledge Discovery and Data Mining, Cao , T., Lim , E.-P., Zhou , Z.-H., Ho , T.-B., Cheung , D. & Motoda , H. (eds). Springer International Publishing, 58–69.

Zhang , H., Dinh , T. N. & Thai , M. T. 2013. Maximizing the spread of positive influence in online social networks. In 2013 IEEE 33rd International Conference on Distributed Computing Systems, 317–326.

Zhang , H., Nguyen , D. T., Zhang , H. & Thai , M. T. 2016. Least cost influence maximization across multiple social networks. IEEE/ACM Transactions on Networking 24(2), 929–939.

Zhang , H., Wu , G. & Ling , Q. 2019. Distributed stochastic gradient descent for link prediction in signed social networks. EURASIP Journal on Advances in Signal Processing 2019(1), 1–11.

Zhang , J., Tang , J., Zhuang , H., Wing-ki Leung , C. & Li , J. 2014. Role-aware conformity influence modeling and analysis in social networks.

Zhang , Y. 2015. Influence maximization on multi-phased multi-layered network.

Zhang , Y., Lyu , T. & Zhang , Y. 2018. Cosine: Community-preserving social network embedding from information diffusion cascades. In Proceedings of the AAAI Conference on Artificial Intelligence, 32.

Zhao , X., Xu , T., Liu , Q. & Guo , H. 2016. Exploring the choice under conflict for social event participation. In International Conference on Database Systems for Advanced Applications, 396–411. Springer.

Zhou , C., Zhang , P., Zang , W. & Guo , L. 2015. On the upper bounds of spread for greedy algorithms in social network influence maximization. IEEE Transactions on Knowledge and Data Engineering 27(10), 2770–2783.

Zhou , T., Cao , J., Liu , B., Xu , S., Zhu , Z. & Luo , J. 2015. Location-based influence maximization in social networks. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM’15, ACM, New York, NY, USA, pp. 1211–1220. http://doi.acm.org/10.1145/2806416.2806462

Zhuang , H., Sun , Y., Tang , J., Zhang , J. & Sun , X. 2013. Influence maximization in dynamic social networks. In 2013 IEEE 13th International Conference on Data Mining , 1313–1318.