Finding Influential Nodes by a Fast Marginal Ranking Method
The problem of Influence Maximization (IM) aims to find a small set of k nodes (seed nodes) in a social network G that could maximize the expected number of nodes. It has been proven to be #P-hard, and many approximation algorithms and heuristic algorithms have been proposed to solve this problem in polynomial time. Those algorithms, however, either trade effectiveness for practical efficiency or vice versa. In order to make a good balance between effectiveness and efficiency, this paper introduces a novel ranking method to identify the influential nodes without computing their exact influence. In particular, our method consists of two phases, the influence ranking and the node selection. At the first phase, we rank the node’s influence based on the centrality of the network. At the second phase, we greedily pick the nodes of high ranks as seeds by considering their marginal influence to the current seed set. Experiments on real-world datasets show that the effectiveness of our method outperforms the state-of-the-art heuristic methods by 3% to 25%; and its speed is faster than the approximate method by at least three orders of magnitude (e.g., the approximate method could not complete in 12 h even for a social network of |V| = 196,591 and |E| = 950,327, while our method completes in 100 s).
This work is partially supported by the ARC (DP170102726, DP180102050), NSF of China (61728204, 91646204), and China National Key Research and Development Program (2016YFB1000700).
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