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Social community detection and message propagation scheme based on personal willingness in social network

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Abstract

Personal willingness is one of the most important factors influencing the construction of social community and the message propagation in social network. Personal willingness is used to describe the subjective initiative of node (user) to communicate information with outside world. The personal willingness is greater, the corresponding user is more willing to make communication with outside world, then the user is more likely to join the corresponding community. So, personal willingness may reduce the probability of generating large-scale communities so as to improve the accuracy and reliability of community detection and increase the stability of community structure. This paper proposes a social community detection and message propagation scheme based on personal willingness in social network. In the proposed scheme, the social community detection algorithm extracts node attributes and then uses modularity degree, interest degree and personal willingness to sophisticatedly detect social communities; also, the message propagation method is based on the exponential model, which constructs feature vector by edge feature and node feature, willingness vector by personal willingness and community willingness, and related basic relationship by propagation probability and propagation delay. Based on the Weibo, YouTube and Digg data, the experiments show that our proposed scheme can ensure the stability and reliability of social community detection and add the initiative and effectiveness of message propagation among users.

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Notes

  1. 1.

    In China, Weibo is a kind of social software provided by the SINA corporation, which is widely used by young people.

  2. 2.

    To extract some features whose values are not the interval [0, 1], we may use the min-max standardization method to process the features. Also, other features may be directly got according to the related definitions or formulas.

  3. 3.

    The URL links to a network page that allows more detailed or comprehensive interpretation of the message content.

  4. 4.

    The content of the label information can attract more readers’ attention or generate interest similarity.

  5. 5.

    In this paper, we use lexical items to construct document vector.

  6. 6.

    We may prove the function \(F(C|\alpha ,\beta )\) has the continuous first-order partial derivatives on \(\alpha \) and \(\beta \).

  7. 7.

    The Sina company is a big network company, which provides many functions of Web site portal.

References

  1. Ahn YY, Bagrow JP, Lehmann S (2010) Link communities reveal multiscale complexity in networks. Nature 466(7307):761–4

  2. Amelio A, Pizzuti C (2016) Evolutionary clustering for mining and tracking dynamic multilayer networks. Comput Intell 33(2):181–209

  3. Blondel VD, Guillaume JL, Lambiotte R et al (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10-008

  4. Buccafurri F, Fotia L, Saraswat V et al (2016) Analysis-preserving protection of user privacy against information leakage of social-network Likes. Inf Sci Int J 328(C):340–358

  5. Centola D (2010) The spread of behavior in an online social network experiment. Science 329(5996):1194–1197

  6. Dang T, Viennet E (2012) Community detection based on structural and attribute similarities. In: The sixth international conference on digital society (ICDS), pp 7–12. ISBN:978-1-61208-176-2

  7. Deitrick W, Hu W (2013) Mutually enhancing community detection and sentiment analysis on twitter networks. J Data Anal Inf Process 1:19

  8. Devi JC, Poovammal E (2016) An analysis of overlapping community detection algorithms in social networks. Proc Comput Sci 89:349–358

  9. Fang M, Shi P, Shang W et al (2018) Locating the source of asynchronous diffusion process in online social networks. IEEE Access PP(99):1-1

  10. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Nat Acad Sci 99(12):7821–7826

  11. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Market Lett 12(3):211–223

  12. Goyal A, Bonchi F, Lakshmanan LVS (2010 February) Learning influence probabilities in social networks. In: International conference on web search and web data mining, WSDM 2010, New York, NY, USA, pp 241–250

  13. Guo L, Ding Z, Wang H (2016) Behavior-based Twitter overlapping community detection [M] database systems for advanced applications. Springer, Berlin

  14. Guo C, Li B, Tian X (2016) Flickr group recommendation using rich social media information. Neurocomputing 204:8–16

  15. Hutair MB, Aghbari ZA, Kamel I (2017) Social community detection based on node distance and interest. In: IEEE/ACM international conference on big data computing applications and technologies. IEEE, pp 274–289

  16. Itakura KY, Sonehara N (2013) Using Twitter’s mentions for efficient emergency message propagation. In: International conference on availability, reliability and security. IEEE Computer Society, pp. 530–537

  17. Jaho E, Karaliopoulos M, Stavrakakis I (2011) ISCoDe: a framework for interest similarity-based community detection in social networks. In: Computer communications workshops. IEEE Xplore, pp 912–917

  18. Johnson R, Zhang T (2013) Accelerating stochastic gradient descent using predictive variance reduction. In: International conference on neural information processing systems. Curran Associates Inc., pp 315–323

  19. Kempe D, Kleinberg J, Tardos (2003) Maximizing the spread of influence through a social network. In: ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 137–146

  20. Kewalramani MN (2011) Community detection in Twitter. University of Maryland, Baltimore, pp 231–300

  21. Kim J, Yoo J (2012) Role of sentiment in message propagation: reply vs. retweet behavior in political communication. In: International conference on social informatics. IEEE computer society, pp 131–136

  22. Lagnier C, Denoyer L, Gaussier E et al (2013) Predicting information diffusion in social networks using content and users profiles. In: European conference on information retrieval. Springer, Berlin, pp 74–85

  23. Lancichinetti A, Fortunato S, Kertsz J (2009) detecting the overlapping and hierarchical community structure in complex networks. N J Phys 11(3):033015

  24. Li K, Lin Z, Wang X (2015) An empirical analysis of users’ privacy disclosure behaviors on social network sites. Elsevier Science Publishers B.V, Amsterdam

  25. Lin YR, Sun J, Sundaram H et al (2011) Community discovery via metagraph factorization. ACM Trans Knowl Discov Data 5(3):1–44

  26. Liu G, Li Y (2016) Social-aware data dissemination service in mobile social network with controlled overhead. Pervasive Mobile Comput 33:127–139

  27. Liu L, Chen B, Qu B et al (2017) Data driven modeling of continuous time information diffusion in social networks. In: IEEE second international conference on data science in cyberspace. IEEE, pp 655–660

  28. Newman MEJ, Clauset A (2015) Structure and inference in annotated networks. arXiv preprint arXiv:1507.04001

  29. Newman MEJ (2004) Analysis of weighted networks. Phys Rev E Stat Nonlinear Soft Matter Phys 70(5 Pt 2):056131

  30. Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E Stat Nonlinear Soft Matter Phys 69(2 Pt 2):026113–026113

  31. Ouadrhiri AE, Kamili ME, Rahmouni I (2017) Messages propagation control in delay tolerant networks under epidemic routing protocol. In: International wireless communications and mobile computing conference, pp 1552–1557

  32. Pons P, Latapy M (2006) Computing communities in large networks using random walks. J Graph Algorithms Appl 10(2):191–218

  33. Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76(3):036106

  34. Rao CS, Raju SV (2016) Similarity analysis between chromosomes of Homo sapiens and monkeys with correlation coefficient, rank correlation coefficient and cosine similarity measures. Genom Data 7(C):202–209

  35. Roux NL, Schmidt M, Bach F (2013) A stochastic gradient method with an exponential convergence rate for finite training sets. Adv Neural Inf Process Syst 4:2663–2671

  36. Saito K, Ohara K, Ohara K et al (2009) Learning continuous-time information diffusion model for social behavioral data analysis. In: Asian conference on machine learning: advances in machine learning. Springer, pp 322–337

  37. Saito K, Ohara K, Yamagishi Y et al (2011) Learning diffusion probability based on node attributes in social networks. In: International symposium on methodologies for intelligent systems. Springer, Berlin, pp 153–162

  38. Shang R, Luo S, Zhang W et al (2016) A multiobjective evolutionary algorithm to find community structures based on affinity propagation. Phys Stat Mech Appl 453:203–227

  39. Shen H, Cheng X, Cai K et al (2009) Detect overlapping and hierarchical community structure in networks. Phys Stat Mech Appl 388(8):1706–1712

  40. Shi C, Cai Y, Fu D et al (2013) A link clustering based overlapping community detection algorithm. Data Knowl Eng 87:394–404

  41. Spiro E, Irvine C, DuBois C et al (2012) Waiting for a retweet: modeling waiting times in information propagation. In: 2012 NIPS workshop of social networks and social media conference. http://snap.stanford.edu/social2012/papers/spiro-dubois-butts.pdf. Accessed 12

  42. Steinhaeuser K, Chawla NV (2010) Identifying and evaluating community structure in complex networks. Pattern Recognit Lett 31(5):413–421

  43. Sun X, Lin H (2013) Topical community detection from mining user tagging behavior and interest. J Assoc Inf Sci Technol 64(2):321–333

  44. Tagarelli A, Amelio A, Gullo F (2017) Ensemble-based community detection in multilayer networks. Data Min Knowl Discov 3:1–38

  45. Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416

  46. Vorontsov MA (1998) Stochastic parallel-gradient-descent technique for high-resolution wave-front phase-distortion correction. J Opt Soc Am A 15(10):2745–2758

  47. Whang JJ, Gleich DF, Dhillon IS (2013) Overlapping community detection using seed set expansion. In: Proceedings of the 22nd ACM international conference on conference on information and knowledge management, ACM, pp 2099–2108

  48. Xu K, Zou K, Huang Y et al (2016) Mining community and inferring friendship in mobile social networks. Neurocomputing 174(PB):605–616

  49. Yang J, Counts S (2010) Predicting the speed, scale, and range of information diffusion in Twitter. ICWSM 10:355–358

  50. Yang L, Zhang Y, Xing C et al (2011) A node interest similarity based P2P trust model. In: IEEE international conference on communication technology. IEEE, pp 572–575

  51. Yao L, Xiaohui K, Hong G et al (2015) A community detecting method based on the node intimacy and degree in social network. J Comput Res Dev 52(10):2363–2372 (in Chinese)

  52. Young HP (2000) The diffusion of innovations in social networks. Gen Inf 413(1):2329–2334

  53. Zhao Y, Li S, Jin F (2016) Identification of influential nodes in social networks with community structure based on label propagation. Neurocomputing 210:34–44

  54. Zhou D, Han W, Wang Y (2015) A fine-grained information diffusion model based on node attributes and content features. J Comput Res Dev 52(1):156–166 (in Chinese)

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Acknowledgements

Funding was provided by National Natural Science Foundations of China (No. 61402055, No. 61504013), Hunan Provincial Natural Science Foundation of China (No. 2018JJ2445, No. 2016JJ3012) and Scientific Research Project of Hunan Provincial Education Department (No. 15C0041, No. 12C0010).

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Correspondence to Ke Gu.

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Gu, K., Wang, L. & Yin, B. Social community detection and message propagation scheme based on personal willingness in social network. Soft Comput 23, 6267–6285 (2019). https://doi.org/10.1007/s00500-018-3283-x

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Keywords

  • Social network
  • Personal willingness
  • Community detection
  • Message propagation