Attribute-Based Influence Maximization in Social Networks
As traditional advertising model exposes its weakness of ignoring consumer interests, the concept of narrow advertising draws increasingly more attention which considers the feature of each user. Under this specific environment, effective viral marketing has to select a set of initial users to maximize their influence on the targeted customers. This paper aims at the integration of viral marketing and narrow advertising, by proposing a novel problem called attribute-based influence maximization. Firstly, the problem definition is presented with the consideration of user features. Then the influence probability between two nodes is modeled and two heuristic algorithms, Sum of Probability Covered Algorithm (SoPCA) and Community-based Algorithm (CBA), are designed. Finally, experiments on six datasets are conducted to verify the effectiveness of proposed algorithms.
KeywordsInfluence maximization Influence probability Social networks User attribute
This work is supported by National Natural Science Foundation of China (61272531, 61202449, 61272054, 61370207, 61370208, 61300024, 61320106007 and 61472081), China high technology 863 program (2013AA013503), Jiangsu Technology Planning Program (SBY2014021039-10), Jiangsu Provincial Key Laboratory of Network and Information Security under Grant No. BM2003201 and Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grant No. 93k-9.
- 3.Cao, T., Wu, X., Wang, S., Hu, X.: Oasnet: an optimal allocation approach to influence maximization in modular social networks. In: 2010 ACM Symposium on Applied Computing, pp. 1088–1094. ACM (2010)Google Scholar
- 4.Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208. ACM (2009)Google Scholar
- 5.Christakis, N.A., Fowler, J.H.: Connected: The surprising power of our social networks and how they shape our lives. hachette digital (2009)Google Scholar
- 6.Domingos, P., Richardson, M.: Mining the network value of customers. In: Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66. ACM (2001)Google Scholar
- 10.Jung, K., Heo, W., Chen, W.: Irie: scalable and robust influence maximization in social networks. In: 2012 IEEE 12th International Conference on Data Mining, pp. 918–923. IEEE (2012)Google Scholar
- 11.Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)Google Scholar
- 13.Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429. ACM (2007)Google Scholar
- 14.Li, F.-H., Li, C.-T., Shan, M.-K.: Labeled influence maximization in social networks for target marketing. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Inernational Conference on Social Computing, pp. 560–563. IEEE (2011)Google Scholar
- 15.Liu, S., Chen, L., Ni, L.M., Fan, J.: Cim: categorical influence maximization. In: 5th International Conference on Ubiquitous Information Management and Communication, p. 124. ACM (2011)Google Scholar
- 17.Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 807–816. ACM (2009)Google Scholar
- 18.Wang, Y., Cong, G., Song, G., Xie, K.: Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1039–1048. ACM (2010)Google Scholar
- 20.Young, H.P.: The diffusion of innovations in social networks. The economy as an evolving complex system III: Current perspectives and future directions. 267 (2006)Google Scholar