Abstract
Influence maximization with application to viral marketing is a well-studied problem of finding a small number of influential users in a social network to maximize the spread of influence under certain influence cascade models. However, almost all previous studies have focused on node-level mining, where they consider identifying nodes as the initial seeders to achieve the desired outcomes. In this article, instead of targeting nodes, we investigate a new boosted influence maximization problem from the edge-level perspective, which asks for finding an edge set that is added to the network to maximize the increased influence spread of a given seed set. We show that the problem is NP-hard and the influence spread function no longer exhibits the property of submodularity, which impose more challenging on the problem. Therefore, we devise a restricted form that is submodular and propose a greedy algorithm with approximate guarantee to solve the problem. However, because of its poor computational efficiency, we further propose an improved greedy algorithm that integrates several effective optimization strategies to significantly speed up the edge selection without sacrificing its accuracy. Extensive experiments over real-world available social networks of different sizes demonstrate the effectiveness and efficiency of the proposed methods.
- Stefanos Antaris, Dimitrios Rafailidis, and Alexandros Nanopoulos. 2014. Link injection for boosting information spread in social networks. Soc. Netw. Anal. Min. 4, 1 (2014), 1--16.Google Scholar
- Ilija Bogunovic, Junyao Zhao, and Volkan Cevher. 2018. Robust maximization of nonsubmodular objectives. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS’18).Google Scholar
- Christian Borgs, Michael Brautbar, Jennifer Chayes, and Brendan Lucier. 2014. Maximizing social influence in nearly optimal time. In Proceedings of the 25th Annual ACM-SIAM Symposium on Discrete Algorithms, 946--957.Google Scholar
- Erik Cambria, Marco Grassi, Amir Hussain, and Catherine Havasi. 2012. Sentic computing for social media marketing. Multimedia Tools Appl. 59, 2 (2012), 557--577.Google Scholar
Digital Library
- Vineet Chaoji, Sayan Ranu, Rajeev Rastogi, and Rushi Bhatt. 2012. Recommendations to boost content spread in social networks. In Proceedings of the 21st ACM International Conference on World Wide Web, 529--539.Google Scholar
- Wei Chen, Chi Wang, and Yajun Wang. 2010. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1029--1038.Google Scholar
- Wei Chen, Yifei Yuan, and Li Zhang. 2011. Scalable influence maximization in social networks under the linear threshold model. In Proceedings of the 10th IEEE International Conference on Data Mining, 88--97.Google Scholar
- Yi-Cheng Chen, Wen-Yuan Zhu, Wen-Chih Peng, Wang-Chien Lee, and Suh-Yin Lee. 2014. CIM: Community-based influence maximization in social networks. ACM Trans. Intell. Syst. Technol. 5, 2 (2014), 1--31.Google Scholar
Digital Library
- Judith A. Chevalier and Dina Mayzlin. 2006. The effect of word-of-mouth on sales: Online book reviews. J. Market. Res. 43, 3 (2006), 345--354.Google Scholar
Cross Ref
- Pierluigi Crescenzi, Gianlorenzo D’angelo, Lorenzo Severini, and Yllka Velaj. 2016. Greedily improving our own closeness centrality in a network. ACM Trans. Knowl. Discov. Data 11, 1, Article 9 (2016), 32 pages.Google Scholar
Digital Library
- Gianlorenzo D’Angelo, Lorenzo Severini, and Yllka Velaj. 2019. Recommending links through influence maximization. Theoretical Computer Science 764 (2019), 30--41.Google Scholar
Digital Library
- Abhimanyu Das and David Kempe. 2011. Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection. In Proceedings of International Conference on Machine Learning, 1057--1064.Google Scholar
- Erik D. Demaine and Morteza Zadimoghaddam. 2010. Minimizing the diameter of a network using shortcut edges. In Proceedings of theScandinavian Conference on Algorithm Theory, 420--431.Google Scholar
- Pedro Domingos and Matthew Richardson. 2001. Mining the network value of customers. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 57--66.Google Scholar
- Fabrizio Frati, Serge Gaspers, Joachim Gudmundsson, and Luke Mathieson. 2015. Augmenting graphs to minimize the diameter. Algorithmica 72, 4 (2015), 995--1010.Google Scholar
Digital Library
- Valiant Leslie G. 1979. The complexity of enumeration and reliability problems. SIAM J. Comput. 8, 3 (1979), 410--421.Google Scholar
- Chao Gao, Jiming Liu, and Ning Zhong. 2011. Network immunization and virus propagation in email networks: experimental evaluation and analysis. Knowl. Inf. Syst. 27, 2 (2011), 253--279.Google Scholar
Digital Library
- Arpita Ghosh and Stephen Boyd. 2006. Growing well-connected graphs. In Proceedings of the IEEE Conference on Decision and Control, 6605--6611.Google Scholar
- Jacob Goldenberg, Barak Libai, and Eitan Muller. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Market. Lett. 12, 3 (2001), 211--223.Google Scholar
Cross Ref
- Amit Goyal, Wei Lu, and Laks V. S. Lakshmanan. 2011. Celf++: Optimizing the greedy algorithm for influence maximization in social networks. In Proceedings of the ACM International Conference Companion on World Wide Web, 47--48.Google Scholar
- Kyomin Jung, Wooram Heo, and Wei Chen. 2013. IRIE: Scalable and robust influence maximization in social networks. In Proceedings of the IEEE International Conference on Data Mining, 918--923.Google Scholar
- Richard Karp. 2010. Reducibility among combinatorial problems. J. Symbol. Logic 40, 4 (2010), 618--619.Google Scholar
- David Kempe, Jon Kleinberg, and Éva Tardos. 2003. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 137--146.Google Scholar
- Masahiro Kimura, Kazumi Saito, and Hiroshi Motoda. 2008. Minimizing the spread of contamination by blocking links in a network. In Proceedings of the 23rd National Conference on Artificial Intelligence, 1175--1180.Google Scholar
- Masahiro Kimura, Kazumi Saito, and Hiroshi Motoda. 2008. Solving the contamination minimization problem on networks for the linear threshold model. Malay. J. Med. Sci. 12, 2 (2008), 50--55.Google Scholar
- Masahiro Kimura, Kazumi Saito, and Hiroshi Motoda. 2009. Blocking links to minimize contamination spread in a social network. ACM Trans. Knowl. Discov. Data 3, 2 Article 9 (2009), 23.Google Scholar
Digital Library
- Chris J. Kuhlman, Gaurav Tuli, Samarth Swarup, Madhav V. Marathe, and S. S. Ravi. 2013. Blocking simple and complex contagion by edge removal. In Proceedings of the IEEE International Conference on Data Mining, 399--408.Google Scholar
- Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Natalie Glance, and Natalie Glance. 2007. Cost-effective outbreak detection in networks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 420--429.Google Scholar
Digital Library
- Yanhua Li, Wei Chen, Yajun Wang, and Zhi-Li Zhang. 2013. Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships. Proceedings of the ACM International Conference on Web Search and Data Mining, 657--666.Google Scholar
- Elchanan Mossel and Sebastien Roch. 2007. On the submodularity of influence in social networks. In Proceedings of the 39th Annual ACM Symposium on Theory of Computing. 128--134.Google Scholar
- G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher. 1978. An analysis of the approximations for maximizing submodular set functions. Math. Program. 14, 1 (1978), 265--294.Google Scholar
Digital Library
- M. E. J. Newman. 2003. The structure and function of complex networks. SIAM Rev. 45, 2 (2003), 167--256.Google Scholar
Digital Library
- Hung T. Nguyen, My T. Thai, and Thang N. Dinh. 2016. Stop-and-stare: Optimal sampling algorithms for viral marketing in billion-scale networks. In Proceedings of the ACM International Conference on Management of Data, 695--710.Google Scholar
- Manos Papagelis. 2015. Refining social graph connectivity via shortcut edge addition. ACM Trans. Knowl. Discov. Data 10, 2, Article 12 (2015), 35 pages.Google Scholar
Digital Library
- Guido Proietti. 2012. Improved approximability and non-approximability results for graph diameter decreasing problems. Theor. Comput. Sci. 417, 1 (2012), 12--22.Google Scholar
- Khadije Rahimkhani, Abolfazl Aleahmad, Maseud Rahgozar, and Ali Moeini. 2015. A fast algorithm for finding most influential people based on the linear threshold model. Exp. Syst. Appl. 42, 3 (2015), 1353--1361.Google Scholar
Digital Library
- Matthew Richardson and Pedro Domingos. 2002. Mining knowledge-sharing sites for viral marketing. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 61--70.Google Scholar
- SNAP Datasets. 2014. Retrieved from http://snap.stanford.edu/data/.Google Scholar
- Youze Tang, Yanchen Shi, and Xiaokui Xiao. 2015. Influence maximization in near-linear time: A martingale approach. In Proceedings of ACM SIGMOD International Conference on Management of Data, 1539--1554.Google Scholar
- Youze Tang, Xiaokui Xiao, and Yanchen Shi. 2014. Influence maximization: Near-optimal time complexity meets practical efficiency. In Proceedings of ACM SIGMOD International Conference on Management of Data, 75--86.Google Scholar
- Yu Wang, Gao Cong, Guojie Song, and Kunqing Xie. 2010. Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1039--1048.Google Scholar
- Chuan Zhou, Peng Zhang, Wenyu Zang, and Li Guo. 2014. Maximizing the long-term integral influence in social networks under the voter model. In Proceedings of the ACM International Conference on World Wide Web, 423--424.Google Scholar
Index Terms
Maximizing Boosted Influence Spread with Edge Addition in Online Social Networks
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