CoFIM: A community-based framework for influence maximization on large-scale networks

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• 作者：Jiaxing Shang ; ^a ; ^b ; ^{shangjx@cqu.edu.cn} ; Shangbo Zhou^a ; ^b ; Xin Li^c ; Lianchen Liu^d ; Hongchun Wu^a ; ^b
• 关键词：Influence maximization ; Community structure ; Large-scale networks ; Diffusion model ; Computational complexity
• 刊名：Knowledge-Based Systems
• 出版年：2017
• 出版时间：1 February 2017
• 年：2017
• 卷：117
• 期：Complete
• 页码：88-100
• 全文大小：1849 K
• 卷排序：117

文摘

Influence maximization is a classic optimization problem studied in the area of social network analysis and viral marketing. Given a network, it is defined as the problem of finding k seed nodes so that the influence spread of the network can be optimized. Kempe et al. have proved that this problem is NP hard and the objective function is submodular, based on which a greedy algorithm was proposed to give a near-optimal solution. However, this simple greedy algorithm is time consuming, which limits its application on large-scale networks. Heuristic algorithms generally cannot provide any performance guarantee. To solve this problem, in this paper we propose CoFIM, a community-based framework for influence maximization on large-scale networks. In our framework the influence propagation process is divided into two phases: (i) seeds expansion; and (ii) intra-community propagation. The first phase is the expansion of seed nodes among different communities at the beginning of diffusion. The second phase is the influence propagation within communities which are independent of each other. Based on the framework, we derive a simple evaluation form of the total influence spread which is submodular and can be efficiently computed. Then we further propose a fast algorithm to select the seed nodes.Experimental results on synthetic and nine real-world large datasets including networks with millions of nodes and hundreds of millions of edges show that our algorithm achieves competitive results in influence spread as compared with state-of-the-art algorithms and it is much more efficient in terms of both time and memory usage.