Abstract
Given a social network with diffusion probabilities as edge weights and a positive integer k, which k nodes should be chosen for initial injection of information to maximize the influence in the network? This problem is popularly known as the Social Influence Maximization Problem (SIM Problem). This is an active area of research in computational social network analysis domain, since one and half decades or so. Due to its practical importance in various domains, such as viral marketing, target advertisement and personalized recommendation, the problem has been studied in different variants, and different solution methodologies have been proposed over the years. This paper presents a survey on the progress in and around SIM Problem. At last, it discusses current research trends and future research directions as well.
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References
Ackerman E, Ben-Zwi O, Wolfovitz G (2010) Combinatorial model and bounds for target set selection. Theor Comput Sci 411(44–46):4017–4022
Angell R, Schoenebeck G (2017) Dont be greedy: leveraging community structure to find high quality seed sets for influence maximization. In: International conference on web and internet economics. Springer, pp 16–29
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. ACM, pp 651–666
Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, pp 65–74
Balogh J, Bollobás B, Morris R (2010) Bootstrap percolation in high dimensions. Comb Probab Comput 19(5–6):643–692
Banerjee P, Chen W, Lakshmanan LV (2019) Maximizing welfare in social networks under a utility driven influence diffusion model. In: Proceedings of the 2019 international conference on management of data. ACM, pp 1078–1095
Banerjee S, Mathew R (2018) An inapproximability result for the target set selection problem on bipartite graphs. arXiv preprint arXiv:1812.01482
Bazgan C, Chopin M, Nichterlein A, Sikora F (2014) Parameterized approximability of maximizing the spread of influence in networks. J Discrete Algorithms 27:54–65
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. SIAM, pp 946–957
Bozorgi A, Haghighi H, Zahedi MS, Rezvani M (2016) Incim: A community-based algorithm for influence maximization problem under the linear threshold model. Inf Process Manag 52(6):1188–1199
Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1–7):107–117
Bucur D, Iacca G (2016) Influence maximization in social networks with genetic algorithms. In: European conference on the applications of evolutionary computation. Springer, pp 379–392
Campbell WM, Dagli CK, Weinstein CJ (2013) Social network analysis with content and graphs. Linc Lab J 20(1):61–81
Centola D (2010) The spread of behavior in an online social network experiment. Science 329(5996):1194–1197
Chakraborty T, Dalmia A, Mukherjee A, Ganguly N (2017) Metrics for community analysis: a survey. ACM Comput Surv (CSUR) 50(4):54
Charikar M, Naamad Y, Wirth A (2016) On approximating target set selection. In: LIPIcs-Leibniz international proceedings in informatics, vol 60. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik
Chen N (2009) On the approximability of influence in social networks. SIAM J Discrete Math 23(3):1400–1415
Chen S, Fan J, Li G, Feng J, Kl Tan, Tang J (2015) Online topic-aware influence maximization. Proc VLDB Endow 8(6):666–677
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. ACM, pp 199–208
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. ACM, pp 1029–1038
Chen W, Yuan Y, Zhang L (2010) Scalable influence maximization in social networks under the linear threshold model. In: 2010 IEEE 10th international conference on data mining (ICDM). IEEE, pp 88–97
Chen W, Collins A, Cummings R, Ke T, Liu Z, Rincon D, Sun X, Wang Y, Wei W, 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. SIAM, pp 379–390
Chen Y, Chang S, Chou C, Peng W, Lee S (2012) Exploring community structures for influence maximization in social networks. In: Proceedings of the 6th SNA-KDD workshop on social network mining and analysis held in conjunction with KDD12 (SNA-KDD12), pp 1–6
Chen YC, Zhu WY, Peng WC, Lee WC, Lee SY (2014) Cim: community-based influence maximization in social networks. ACM Trans Intell Syst Technol (TIST) 5(2):25
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. ACM, pp 509–518
Chopin M, Nichterlein A, Niedermeier R, Weller M (2012) Constant thresholds can make target set selection tractable. Springer, Berlin, pp 120–133
Chopin M, Nichterlein A, Niedermeier R, Weller M (2014) Constant thresholds can make target set selection tractable. Theory Comput Syst 55(1):61–83
Cicalese F, Cordasco G, Gargano L, Milanič M, Vaccaro U (2014) Latency-bounded target set selection in social networks. Theor Comput Sci 535:1–15
Clauset A, Newman ME, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):066111
Cohen E, Delling D, Pajor T, Werneck RF (2014) 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, pp 629–638
Cordasco G, Gargano L, Mecchia M, Rescigno AA, Vaccaro U (2015a) A fast and effective heuristic for discovering small target sets in social networks. In: Combinatorial optimization and applications. Springer, pp 193–208
Cordasco G, Gargano L, Rescigno AA (2015b) Influence propagation over large scale social networks. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015. ACM, pp 1531–1538
Cordasco G, Gargano L, Rescigno AA (2016) Active spreading in networks. In: ICTCS, pp 149–162
Cowan R, Jonard N (2004) Network structure and the diffusion of knowledge. J Econ Dyn Control 28(8):1557–1575
Dhamal S, Prabuchandran K, Narahari Y (2016) Information diffusion in social networks in two phases. IEEE Trans Netw Sci Eng 3(4):197–210
Diestel R (2005) Graph theory. 2005. Grad Texts in Math 101
Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 57–66
Downey RG, Fellows MR (2012) Parameterized complexity. Springer, Berlin
Downey RG, Fellows MR (2013) Fundamentals of parameterized complexity, vol 4. Springer, Berlin
Downey RG, Fellows MR, Regan KW (1998) Parameterized circuit complexity and the W hierarchy. Theor Comput Sci 191(1–2):97–115
Dreyer PA, Roberts FS (2009) Irreversible k-threshold processes: graph-theoretical threshold models of the spread of disease and of opinion. Discrete Appl Math 157(7):1615–1627
Epasto A, Mahmoody A, Upfal E (2017) Real-time targeted-influence queries over large graphs. In: Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017. ACM, pp 224–231
Feige U, Goemans M (1995) Approximating the value of two power proof systems, with applications to max 2sat and max dicut
Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174
Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239
Galhotra S, Arora A, Virinchi S, Roy S (2015) Asim: a scalable algorithm for influence maximization under the independent cascade model. In: Proceedings of the 24th international conference on world wide web. ACM, pp 35–36
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. ACM, pp 743–758
Garey MR, Johnson DS (2002) Computers and intractability, vol 29. W. H. Freeman, New York
Gionis A, Terzi E, Tsaparas P (2013) Opinion maximization in social networks. In: Proceedings of the 2013 SIAM international conference on data mining. SIAM, pp 387–395
Goel S, Watts DJ, Goldstein DG (2012) The structure of online diffusion networks. In: Proceedings of the 13th ACM conference on electronic commerce. ACM, pp 623–638
Gong M, Yan J, Shen B, Ma L, Cai Q (2016) Influence maximization in social networks based on discrete particle swarm optimization. Inf Sci 367:600–614
Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. In: Proceedings of the third ACM international conference on Web search and data mining. ACM, pp 241–250
Goyal A, Bonchi F, Lakshmanan LV (2011a) A data-based approach to social influence maximization. Proc. VLDB Endow. 5(1):73–84
Goyal A, Lu W, Lakshmanan LV (2011b) Celf++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th international conference companion on world wide web. ACM, pp 47–48
Goyal A, Lu W, Lakshmanan LV (2011c) Simpath: an efficient algorithm for influence maximization under the linear threshold model. In: 2011 IEEE 11th international conference on data mining (ICDM). IEEE, pp 211–220
Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. In: Proceedings of the 13th international conference on world wide web. ACM, pp 491–501
Han K, Huang K, Xiao X, Tang J, Sun A, Tang X (2018) Efficient algorithms for adaptive influence maximization. In: Proceedings of the VLDB endowment, vol 11, no 9
Harant J, Pruchnewski A, Voigt M (1999) On dominating sets and independent sets of graphs. Comb Probab Comput 8(6):547–553
Heidari N (2016) Modeling information diffusion in social networks. arXiv preprint arXiv:1603.02178
Ienco D, Bonchi F, Castillo C (2010) The meme ranking problem: maximizing microblogging virality. In: 2010 IEEE international conference on data mining workshops (ICDMW). IEEE, pp 328–335
Jiang Q, Song G, Cong G, Wang Y, Si W, Xie K (2011) Simulated annealing based influence maximization in social networks. In: AAAI, vol 11, pp 127–132
Jung K, Heo W, Chen W (2012) Irie: scalable and robust influence maximization in social networks. In: 2012 IEEE 12th international conference on data mining (ICDM). IEEE, pp 918–923
Kang C, Kraus S, Molinaro C, Spezzano F, Subrahmanian V (2016) Diffusion centrality: a paradigm to maximize spread in social networks. Artif Intell 239:70–96
Karp RM (1972) Reducibility among combinatorial problems. In: Complexity of computer computations. Springer, pp 85–103
Kasprzak R (2012) Diffusion in networks. J Telecommun Inf Technol 99–106
Ke X, Khan A, Cong G (2018) Finding seeds and relevant tags jointly: for targeted influence maximization in social networks. In: Proceedings of the 2018 international conference on management of data. ACM, pp 1097–1111
Kempe D, Kleinberg J, Tardos É (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. ACM, pp 137–146
Kempe D, Kleinberg JM, Tardos É (2005) Influential nodes in a diffusion model for social networks. In: ICALP, vol 5. Springer, pp 1127–1138
Kempe D, Kleinberg JM, Tardos É (2015) Maximizing the spread of influence through a social network. Theory Comput 11(4):105–147
Khuller S, Moss A, Naor JS (1999) The budgeted maximum coverage problem. Inf Process Lett 70(1):39–45
Kim J, Kim SK, 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). IEEE, pp 266–277
Kimura M, Saito K (2006) Tractable models for information diffusion in social networks. In: Knowledge discovery in databases: PKDD 2006, pp 259–271
Kimura M, Saito K, Nakano R, Motoda H (2009) Finding influential nodes in a social network from information diffusion data. In: Social computing and behavioral modeling, pp 1–8
Klasing R, Laforest C (2004) Hardness results and approximation algorithms of k-tuple domination in graphs. Inf Process Lett 89(2):75–83
Kortsarz G (2001) On the hardness of approximating spanners. Algorithmica 30(3):432–450
Landherr A, Friedl B, Heidemann J (2010) A critical review of centrality measures in social networks. Bus Inf Syst Eng 2(6):371–385
Lee JR, Chung CW (2015) A query approach for influence maximization on specific users in social networks. IEEE Trans Knowl Data Eng 27(2):340–353
Leskovec J, Kleinberg J, Faloutsos C (2005) Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM, pp 177–187
Leskovec J, Adamic LA, Huberman BA (2007a) The dynamics of viral marketing. ACM Trans Web (TWEB) 1(1):5
Leskovec J, Krause A, Guestrin C, Faloutsos C, VanBriesen J, Glance N (2007b) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 420–429
Li X, Cheng X, Su S, Sun C (2018a) Community-based seeds selection algorithm for location aware influence maximization. Neurocomputing 275:1601–1613
Li Y, Chen W, Wang Y, Zhang ZL (2013) Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships. In: Proceedings of the sixth ACM international conference on web search and data mining. ACM, pp 657–666
Li Y, Zhang D, Tan KL (2015) Real-time targeted influence maximization for online advertisements. Proc VLDB Endow 8(10):1070–1081
Li Y, Fan J, Wang Y, Tan KL (2018b) Influence maximization on social graphs: a survey. IEEE Trans Knowl Data Eng 30:1852–1872
Liu B (2011) Social network analysis. In: Web data mining. Springer, Berlin, pp 269–309
Liu SJ, Chen CY, Tsai CW (2017) An effective simulated annealing for influence maximization problem of online social networks. Proc Comput Sci 113:478–483
Ma H, Yang H, Lyu MR, King I (2008) Mining social networks using heat diffusion processes for marketing candidates selection. In: Proceedings of the 17th ACM conference on information and knowledge management. ACM, pp 233–242
Maehara T, Suzuki H, Ishihata M (2017) Exact computation of influence spread by binary decision diagrams. In: Proceedings of the 26th international conference on world wide web, international world wide web conferences steering committee, pp 947–956
Narayanam R, Narahari Y (2011) A shapley value-based approach to discover influential nodes in social networks. IEEE Trans Autom Sci Eng 8(1):130–147
Nekovee M, Moreno Y, Bianconi G, Marsili M (2007) Theory of rumour spreading in complex social networks. Phys A 374(1):457–470
Nguyen H, Zheng R (2012) On budgeted influence maximization in social networks. arXiv preprint arXiv:1204.4491
Nguyen H, Zheng R (2013) On budgeted influence maximization in social networks. IEEE J Sel Areas Commun 31(6):1084–1094
Nguyen HT, Dinh TN, Thai MT (2016a) Cost-aware targeted viral marketing in billion-scale networks. In: IEEE INFOCOM 2016-the 35th annual IEEE international conference on computer communications. IEEE, pp 1–9
Nguyen HT, Thai MT, Dinh TN (2016b) Stop-and-stare: optimal sampling algorithms for viral marketing in billion-scale networks. In: Proceedings of the 2016 international conference on management of data. ACM, pp 695–710
Nguyen HT, Ghosh P, Mayo ML, Dinh TN (2017) Social influence spectrum at scale: near-optimal solutions for multiple budgets at once. ACM Trans Inf Syst (TOIS) 36(2):14
Nguyen HT, Thai MT, Dinh TN (2017) A billion-scale approximation algorithm for maximizing benefit in viral marketing. IEEE/ACM Trans Netw 25:2419–2429
Nichterlein A, Niedermeier R, Uhlmann J, Weller M (2010) On tractable cases of target set selection. In: Algorithms and computation, pp 378–389
Nichterlein A, Niedermeier R, Uhlmann J, Weller M (2013) On tractable cases of target set selection. Soc Netw Anal Min 3(2):233–256
Peleg D (2002) Local majorities, coalitions and monopolies in graphs: a review. Theor Comput Sci 282(2):231–257
Peng S, Zhou Y, Cao L, Yu S, Niu J, Jia W (2018) Influence analysis in social networks: a survey. J Netw Comput Appl 106:17–32
Raghavan S, Zhang R (2015) Weighted target set selection on social networks. Technical report, Working paper, University of Maryland
Rahimkhani K, Aleahmad A, Rahgozar M, Moeini A (2015) A fast algorithm for finding most influential people based on the linear threshold model. Expert Syst Appl 42(3):1353–1361
Raman V, Saurabh S, Srihari S (2008) Parameterized algorithms for generalized domination. Lect Notes Comput Sci 5165:116–126
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. ACM, pp 61–70
Saito K, Nakano R, Kimura M (2008) Prediction of information diffusion probabilities for independent cascade model. In: Knowledge-based intelligent information and engineering systems. Springer, pp 67–75
Saito K, Kimura M, Ohara K, Motoda H (2010) Selecting information diffusion models over social networks for behavioral analysis. In: Machine learning and knowledge discovery in databases, pp 180–195
Saito K, Ohara K, Yamagishi Y, Kimura M, Motoda H (2011) Learning diffusion probability based on node attributes in social networks. In: International symposium on methodologies for intelligent systems. Springer, pp 153–162
Salathé M, Kazandjieva M, Lee JW, Levis P, Feldman MW, Jones JH (2010) A high-resolution human contact network for infectious disease transmission. Proc Nat Acad Sci 107(51):22020–22025
Sankar CP, Asharaf S, Kumar KS (2016) Learning from bees: an approach for influence maximization on viral campaigns. PLoS ONE 11(12):e0168125
Shakarian P, Bhatnagar A, Aleali A, Shaabani E, Guo R (2015) The independent cascade and linear threshold models. In: Diffusion in social networks. Springer, pp 35–48
Shang J, Zhou S, Li X, Liu L, Wu H (2017) Cofim: a community-based framework for influence maximization on large-scale networks. Knowl Based Syst 117:88–100
Song X, Tseng BL, Lin CY, Sun MT (2006) Personalized recommendation driven by information flow. In: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 509–516
Sun J, Tang J (2011) A survey of models and algorithms for social influence analysis. In: Social network data analytics. Springer, Berlin, pp 177–214
Sun L, Huang W, Yu PS, Chen W (2018) Multi-round influence maximization. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. ACM, pp 2249–2258
Tabak BM, Takami M, Rocha JM, Cajueiro DO, Souza SR (2014) Directed clustering coefficient as a measure of systemic risk in complex banking networks. Phys A 394:211–216
Tang J, Tang X, Yuan J (2017) Influence maximization meets efficiency and effectiveness: a hop-based approach. In: Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017. ACM, pp 64–71
Tang J, Tang X, Yuan J (2018) An efficient and effective hop-based approach for influence maximization in social networks. Soc Netw Anal Min 8(1):10
Tang Y, Xiao X, Shi Y (2014) Influence maximization: near-optimal time complexity meets practical efficiency. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data. ACM, pp 75–86
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. ACM, pp 1539–1554
Tong G, Wu W, Tang S, Du DZ (2017) Adaptive influence maximization in dynamic social networks. IEEE/ACM Trans Netw (TON) 25(1):112–125
Tovey CA (1984) A simplified np-complete satisfiability problem. Discrete Appl Math 8(1):85–89
Tsai CW, Yang YC, Chiang MC (2015) A genetic newgreedy algorithm for influence maximization in social network. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 2549–2554
Valente TW (1995) Network models of the diffusion of innovations
Valente TW (1996) Social network thresholds in the diffusion of innovations. Soc Netw 18(1):69–89
Varshney D, Kumar S, Gupta V (2017) Predicting information diffusion probabilities in social networks: a Bayesian networks based approach. Knowl Based Syst 133:66–76
Wang C, Chen W, Wang Y (2012) Scalable influence maximization for independent cascade model in large-scale social networks. Data Min Knowl Disc 25(3):545
Wang F, Jiang W, Li X, Wang G (2017a) Maximizing positive influence spread in online social networks via fluid dynamics. Future Gener Comput Syst 86:1491–1502
Wang Q, Gong M, Song C, Wang S (2017b) Discrete particle swarm optimization based influence maximization in complex networks. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 488–494
Wang T, Chen Y, Zhang Z, Xu T, Jin L, Hui P, Deng B, Li X (2011) Understanding graph sampling algorithms for social network analysis. In: 2011 31st international conference on distributed computing systems workshops (ICDCSW). IEEE, pp 123–128
Wang Y, Cong G, Song G, Xie K (2010) 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, pp 1039–1048
Weng J, Lim EP, Jiang J, He Q (2010) Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the third ACM international conference on Web search and data mining. ACM, pp 261–270
Wilder B, Immorlica N, Rice E, Tambe M (2017) Influence maximization with an unknown network by exploiting community structure. In: SocInf@ IJCAI, pp 2–7
Williamson DP, Shmoys DB (2011) The design of approximation algorithms. Cambridge University Press, Cambridge
Wilson C, Boe B, Sala A, Puttaswamy KP, Zhao BY (2009) User interactions in social networks and their implications. In: Proceedings of the 4th ACM European conference on computer systems. ACM, pp 205–218
Wu H, Yue K, Fu X, Wang Y, Liu W (2016) Parallel seed selection for influence maximization based on k-shell decomposition. In: International conference on collaborative computing: networking, applications and worksharing. Springer, pp 27–36
Wu HH, Küçükyavuz S (2017) A two-stage stochastic programming approach for influence maximization in social networks. Comput Optim Appl 69:1–33
Xie J, Szymanski BK, Liu X (2011) Slpa: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: 2011 IEEE 11th international conference on data mining workshops (ICDMW). IEEE, pp 344–349
Xu B, Liu L (2010) Information diffusion through online social networks. In: 2010 IEEE international conference on emergency management and management sciences (ICEMMS). IEEE, pp 53–56
Yang J, Leskovec J (2010) Modeling information diffusion in implicit networks. In: 2010 IEEE 10th international conference on data mining (ICDM). IEEE, pp 599–608
Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Oxford
Yang XS, Chien SF, Ting TO (2014) Computational intelligence and metaheuristic algorithms with applications. Sci World J 2014:425853
Yi H, Duan Q, Liao TW (2013) Three improved hybrid metaheuristic algorithms for engineering design optimization. Appl Soft Comput 13(5):2433–2444
Zhang H, Dinh TN, Thai MT (2013) Maximizing the spread of positive influence in online social networks. In: 2013 IEEE 33rd international conference on distributed computing systems (ICDCS). IEEE, pp 317–326
Zhang H, Mishra S, Thai MT, Wu J, Wang Y (2014) Recent advances in information diffusion and influence maximization in complex social networks. Oppor Mobile Soc Netw 37(1.1):37
Zhang K, Du H, Feldman MW (2017) Maximizing influence in a social network: improved results using a genetic algorithm. Phys A 478:20–30
Zhu T, Wang B, Wu B, Zhu C (2014) Maximizing the spread of influence ranking in social networks. Inf Sci 278:535–544
Zhu Y, Wu W, Bi Y, Wu L, Jiang Y, Xu W (2015) Better approximation algorithms for influence maximization in online social networks. J Comb Optim 30(1):97–108
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 (ICDM). IEEE, pp 1313–1318
Zong Z, Li B, Hu C (2014) dirier: distributed influence maximization in social network. In: 2014 20th IEEE international conference on parallel and distributed systems (ICPADS). IEEE, pp 119–125
Zou CC, Towsley D, Gong W (2007) Modeling and simulation study of the propagation and defense of internet e-mail worms. IEEE Trans Dependable Secure Comput 4(2):105–118
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The authors want to thank Ministry of Human Resource and Development (MHRD), Government of India, for sponsoring the project: E-business Center of Excellence under the scheme of Center for Training and Research in Frontier Areas of Science and Technology (FAST), Grant No. F.No.5-5/2014-TS.VII.
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Major part of this was done when the first author was a Ph.D. student at IIT Kharagpur. This work is financially supported by the project E-Business Center of Excellence (F.No.5-5/2014-TS.VII).
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Banerjee, S., Jenamani, M. & Pratihar, D.K. A survey on influence maximization in a social network. Knowl Inf Syst 62, 3417–3455 (2020). https://doi.org/10.1007/s10115-020-01461-4
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DOI: https://doi.org/10.1007/s10115-020-01461-4