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CFIN: A community-based algorithm for finding influential nodes in complex social networks

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Abstract

Influence maximization (IM) problem, a fundamental algorithmic problem, is the problem of selecting a set of k users (refer as seed set) from a social network to maximize the expected number of influenced users (also known as influence spread). Due to the numerous applications of IM in marketing, IM has been studied extensively in recent years. Nevertheless, many algorithms do not take into consideration the impact of communities to influence maximization and some algorithms are non-scalable and time-consuming in practice. In this paper, we proposed a fast and scalable algorithm called community finding influential node (CFIN) that selects k users based on community structure, which maximizes the influence spread in the networks. The CFIN consists of two main parts for influence maximization: (1) seed selection and (2) local community spreading. The first part of CFIN is the extraction of seed nodes from communities which obtained the running of the community detection algorithm. In this part, to decrease computational complexity effectively and scatter seed nodes into communities, the meaningful communities are selected. The second part consists of the influence spread inside communities that are independent of each other. In this part, the final seed nodes entered to distribute the local spreading by the use of a simple path inside communities. To study the performance of the CFIN, several experiments have been conducted on some real and synthetic networks. The experimental simulations on the CFIN, in comparison with other algorithms, confirm the superiority of the CFIN in terms of influence spread, coverage ratio, running time, and Dolan-Moré performance profile.

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References

  1. Alrashed S (2017) Reducing power consumption of non-preemptive real-time systems. J Supercomput 73:5402–5413. https://doi.org/10.1007/s11227-017-2092-9

    Article  Google Scholar 

  2. Min-Allah N, Qureshi MB, Alrashed S, Rana OF (2019) Cost efficient resource allocation for real-time tasks in embedded systems. Sustain Cities Soc 48:101523. https://doi.org/10.1016/j.scs.2019.101523

    Article  Google Scholar 

  3. 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

  4. 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, pp 1539–1554

  5. Zhang H, Mishra S, Thai MT et al (2014) Recent advances in information diffusion and influence maximization in complex social networks. Oppor Mob Soc Netw 37:37

    Google Scholar 

  6. Budak C, Agrawal D, El Abbadi A (2011) Limiting the spread of misinformation in social networks. In: Proceedings of the 20th International Conference on World Wide Web. ACM, pp 665–674

  7. Wu P, Pan L (2017) Scalable influence blocking maximization in social networks under competitive independent cascade models. Comput Netw 123:38–50

    Google Scholar 

  8. Feng Z, Xu X, Yuruk N, Schweiger TA (2007) A novel similarity-based modularity function for graph partitioning. In: International Conference on Data Warehousing and Knowledge Discovery. Springer, pp 385–396

  9. Teng YW TC, Yu PS, Chen MS (2018) Revenue maximization on the multi-grade product. In: Proceedings of the 2018 SIAM International Conference on Data Mining, pp 576–584

  10. 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

  11. Khomami MMD, Rezvanian A, Bagherpour N, Meybodi MR (2018) Minimum positive influence dominating set and its application in influence maximization: a learning automata approach. Appl Intell 48:570–593

    Google Scholar 

  12. Rezvanian A, Moradabadi B, Ghavipour M et al (2019) Social Influence Maximization. In: Rezvanian A, Moradabadi B, Ghavipour M et al (eds) Learning automata approach for social networks. Springer International Publishing, Cham, pp 315–329

    Google Scholar 

  13. 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, New York, NY, USA, pp 137–146

  14. Leskovec J, Krause A, Guestrin C et al (2007) 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

  15. Goyal A, Lu W, Lakshmanan LV (2011) 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

  16. Kundu S, Pal SK (2015) Deprecation based greedy strategy for target set selection in large scale social networks. Inf Sci 316:107–122

    MATH  Google Scholar 

  17. Zhou C, Zhang P, Zang W, Guo L (2015) On the upper bounds of spread for greedy algorithms in social network influence maximization. IEEE Trans Knowl Data Eng 27:2770–2783

    Google Scholar 

  18. Song G, Li Y, Chen X et al (2016) Influential node tracking on dynamic social network: an interchange greedy approach. IEEE Trans Knowl Data Eng 29:359–372

    Google Scholar 

  19. 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

  20. Guo J, Zhang P, Zhou C et al (2013) Item-based top-k influential user discovery in social networks. In: 2013 IEEE 13th International Conference on Data Mining Workshops. IEEE, pp 780–787

  21. Zhao X-Y, Huang B, Tang M et al (2015) Identifying effective multiple spreaders by coloring complex networks. EPL Europhys Lett 108:68005

    Google Scholar 

  22. 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). IEEE, pp 266–277

  23. Kim J, Lee W, Yu H (2014) CT-IC: continuously activated and time-restricted independent cascade model for viral marketing. Knowl Based Syst 62:57–68

    Google Scholar 

  24. Li D, Xu Z-M, Chakraborty N et al (2014) Polarity related influence maximization in signed social networks. PLoS ONE 9:e102199

    Google Scholar 

  25. Luo Z-L, Cai W-D, Li Y-J, Peng D (2012) A pagerank-based heuristic algorithm for influence maximization in the social network. In: Recent Progress in Data Engineering and Internet Technology. Springer, pp 485–490

  26. Kimura M, Saito K, Nakano R, Motoda H (2010) Extracting influential nodes on a social network for information diffusion. Data Min Knowl Discov 20:70

    MathSciNet  Google Scholar 

  27. Ohsaka N, Akiba T, Yoshida Y, Kawarabayashi K (2014) Fast and accurate influence maximization on large networks with pruned monte-carlo simulations. In: Twenty-Eighth AAAI Conference on Artificial Intelligence

  28. Goyal A, Lu W, Lakshmanan LV (2011) 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

  29. 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. IEEE, pp 88–97

  30. Lu Z, Fan L, Wu W et al (2014) Efficient influence spread estimation for influence maximization under the linear threshold model. Comput Soc Netw 1:2

    Google Scholar 

  31. Heidari M, Asadpour M, Faili H (2015) SMG: fast scalable greedy algorithm for influence maximization in social networks. Phys Stat Mech Appl 420:124–133

    Google Scholar 

  32. Narayanam R, Narahari Y (2011) A shapley value-based approach to discover influential nodes in social networks. IEEE Trans Autom Sci Eng 8:130–147

    Google Scholar 

  33. Cantwell GT, Newman MEJ (2019) Mixing patterns and individual differences in networks. Phys Rev E 99:042306

    Google Scholar 

  34. Riolo MA, Cantwell GT, Reinert G, Newman ME (2017) Efficient method for estimating the number of communities in a network. Phys Rev E 96:032310

    Google Scholar 

  35. Liu W, Pellegrini M, Wang X (2014) Detecting communities based on network topology. Sci Rep 4:5739

    Google Scholar 

  36. Li H, Bhowmick SS, Sun A, Cui J (2015) Conformity-aware influence maximization in online social networks. VLDB J 24:117–141

    Google Scholar 

  37. Guo L, Zhang D, Cong G et al (2016) Influence maximization in trajectory databases. IEEE Trans Knowl Data Eng 29:627–641

    Google Scholar 

  38. Li Y, Zhang D, Tan K-L (2015) Real-time targeted influence maximization for online advertisements

  39. Stein S, Eshghi S, Maghsudi S et al (2017) Heuristic algorithms for influence maximization in partially observable social networks. In: SocInf@ IJCAI, pp 20–32

  40. Wilder B, Immorlica N, Rice E, Tambe M (2018) Maximizing influence in an unknown social network. In: Thirty-Second AAAI Conference on Artificial Intelligence

  41. Rezvanian A, Moradabadi B, Ghavipour M et al (2019) Social Community Detection. Learning automata approach for social networks. Springer International Publishing, Cham, pp 151–168

    Google Scholar 

  42. de Guzzi Bagnato G, Ronqui JRF, Travieso G (2018) Community detection in networks using self-avoiding random walks. Phys Stat Mech Appl 505:1046–1055

    Google Scholar 

  43. Fortunato S (2010) Community detection in graphs. Phys Rep 486:75–174

    MathSciNet  Google Scholar 

  44. Malliaros FD, Vazirgiannis M (2013) Clustering and community detection in directed networks: a survey. Phys Rep 533:95–142. https://doi.org/10.1016/j.physrep.2013.08.002

    Article  MathSciNet  MATH  Google Scholar 

  45. Kumpula JM, Kivelä M, Kaski K, Saramäki J (2008) Sequential algorithm for fast clique percolation. Phys Rev E 78(2):026109

    Google Scholar 

  46. Luo Z-G, Ding F, Jiang X-Z, Shi J-L (2011) New progress on community detection in complex networks. J Nat Univ Defense Technol 33(1):47–52

    Google Scholar 

  47. Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818

    Google Scholar 

  48. Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76:036106

    Google Scholar 

  49. Gregory S (2010) Finding overlapping communities in networks by label propagation. New J Phys 12:103018

    MATH  Google Scholar 

  50. Xie J, Szymanski BK (2012) Towards linear time overlapping community detection in social networks. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, pp 25–36

  51. Ugander J, Backstrom L (2013) Balanced label propagation for partitioning massive graphs. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp 507–516

  52. Stokes ME, Barmada MM, Kamboh MI, Visweswaran S (2014) The application of network label propagation to rank biomarkers in genome-wide Alzheimer’s data. BMC Genom 15:282

    Google Scholar 

  53. Hosseini R, Rezvanian A (2020) AntLP: ant-based label propagation algorithm for community detection in social networks. CAAI Trans Intell Technol 5:34–41

    Google Scholar 

  54. Kuzmin K, Shah SY, Szymanski BK (2013) Parallel overlapping community detection with SLPA. In: 2013 International Conference on Social Computing. IEEE, pp 204–212

  55. Ahn Y-Y, Bagrow JP, Lehmann S (2010) Link communities reveal multiscale complexity in networks. Nature 466:761–764

    Google Scholar 

  56. Ye Q, Wu B, Zhao Z, Wang B (2011) Detecting link communities in massive networks. In: 2011 International Conference on Advances in Social Networks Analysis and Mining. IEEE, pp 71–78

  57. Lee C, Reid F, McDaid A, Hurley N (2010) Detecting highly overlapping community structure by greedy clique expansion. ArXiv Prepr ArXiv10021827

  58. Zhang X, Wang C, Su Y et al (2017) A fast overlapping community detection algorithm based on weak cliques for large-scale networks. IEEE Trans Comput Soc Syst 4:218–230

    Google Scholar 

  59. Badie R, Aleahmad A, Asadpour M, Rahgozar M (2013) An efficient agent-based algorithm for overlapping community detection using nodes’ closeness. Phys Stat Mech Appl 392:5231–5247

    Google Scholar 

  60. Khomami MMD, Rezvanian A, Meybodi MR (2016) Distributed learning automata-based algorithm for community detection in complex networks. Int J Mod Phys B 30:1650042

    MathSciNet  Google Scholar 

  61. Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99:7821–7826

    MathSciNet  MATH  Google Scholar 

  62. Lusseau D (2003) The emergent properties of a dolphin social network. Proc R Soc Lond B Biol Sci 270:S186–S188

    Google Scholar 

  63. Park J, Newman ME (2005) A network-based ranking system for US college football. J Stat Mech: Theory Exp 2005:P10014

    MATH  Google Scholar 

  64. Adamic LA, Glance N (2005) The political blogosphere and the 2004 US election: divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery. ACM, pp 36–43

  65. Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data TKDD 1:1–41

    Google Scholar 

  66. Richardson M, Agrawal R, Domingos P (2003) Trust management for the semantic web. In: International Semantic Web Conference. Springer, pp 351–368

  67. Erdos P, Rényi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5:17–61

    MathSciNet  MATH  Google Scholar 

  68. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442

    MATH  Google Scholar 

  69. Lancichinetti A, Radicchi F, Ramasco JJ, Fortunato S (2011) Finding statistically significant communities in networks. PLoS ONE 6:e18961

    Google Scholar 

  70. Goyal A, Bonchi F, Lakshmanan LVS (2011) A data-based approach to social influence maximization. Proc VLDB Endow 5:73–84

    Google Scholar 

  71. Chen Y-C, Zhu W-Y, Peng W-C et al (2014) CIM: community-based influence maximization in social networks. ACM Trans Intell Syst Technol TIST 5:25

    Google Scholar 

  72. 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 42:1353–1361

    Google Scholar 

  73. Ok J, Jin Y, Shin J, Yi Y (2014) On maximizing diffusion speed in social networks: impact of random seeding and clustering. In: The 2014 ACM International Conference on Measurement and Modeling of Computer Systems, pp 301–313

  74. He J-L, Fu Y, Chen D-B (2015) A novel top-k strategy for influence maximization in complex networks with community structure. PLOS ONE 10(12):e0145283. https://doi.org/10.1371/journal.pone.0145283

    Article  Google Scholar 

  75. Dolan ED, Moré JJ (2002) Benchmarking optimization software with performance profiles. Math Program 91:201–213

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This research was in part supported by a Grant from IPM. (No. CS1398-4-222).

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Authors and Affiliations

  1. Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnics), Tehran, Iran

    Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi & Alireza Bagheri

  2. Department of Computer Engineering, University of Science and Culture, Tehran, Iran

    Alireza Rezvanian

  3. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

    Alireza Rezvanian

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  1. Mohammad Mehdi Daliri Khomami
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  2. Alireza Rezvanian
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Correspondence to Mohammad Mehdi Daliri Khomami.

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Khomami, M.M.D., Rezvanian, A., Meybodi, M.R. et al. CFIN: A community-based algorithm for finding influential nodes in complex social networks. J Supercomput 77, 2207–2236 (2021). https://doi.org/10.1007/s11227-020-03355-2

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