2013 IEEE 13th International Conference on Data Mining (2013)
Dallas, TX, USA USA
Dec. 7, 2013 to Dec. 10, 2013
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2013.145
Social influence and influence diffusion has been widely studied in online social networks. However, most existing works on influence diffusion focus on static networks. In this paper, we study the problem of maximizing influence diffusion in a dynamic social network. Specifically, the network changes over time and the changes can be only observed by periodically probing some nodes for the update of their connections. Our goal then is to probe a subset of nodes in a social network so that the actual influence diffusion process in the network can be best uncovered with the probing nodes. We propose a novel algorithm to approximate the optimal solution. The algorithm, through probing a small portion of the network, minimizes the possible error between the observed network and the real network. We evaluate the proposed algorithm on both synthetic and real large networks. Experimental results show that our proposed algorithm achieves a better performance than several alternative algorithms.
Heuristic algorithms, Probes, Algorithm design and analysis, Approximation algorithms, Twitter, Estimation
H. Zhuang, Y. Sun, J. Tang, J. Zhang and X. Sun, "Influence Maximization in Dynamic Social Networks," 2013 IEEE 13th International Conference on Data Mining(ICDM), Dallas, TX, USA USA, 2013, pp. 1313-1318.