期刊
KNOWLEDGE-BASED SYSTEMS
卷 160, 期 -, 页码 88-103出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2018.06.013
关键词
Social network; Influence maximization; Metaheuristic; Discrete bat algorithm; Collective intelligence
资金
- National Natural Science Foundation of China [21503101, 61702240]
- SRF for ROCS, SEM [SEM[2015]311]
- Fundamental Research Funds for the Central Universities [lzujbky-2017-191]
Influence maximization aims to select a small set of k influential nodes to maximize the spread of influence. It is still an open research topic to develop effective and efficient algorithms for the optimization problem. Greedy-based algorithms utilize the property of submodularity to provide performance guarantee, but the computational cost is unbearable especially in large-scale networks. Meanwhile, conventional topology-based centrality methods always fail to provide satisfying identification of influential nodes. To identify the k influential nodes effectively, we propose a metaheuristic discrete bat algorithm (DBA) based on the collective intelligence of bat population in this paper. According to the evolutionary rules of the original bat algorithm (BA), a probabilistic greedy-based local search strategy based on network topology is presented and a CandidatesPool is generated according to the contribution of each node to the network topology to enhance the exploitation operation of DBA. The experimental results and statistic tests on five real-world social networks and a synthetic network under independent cascade model demonstrate that DBA outperforms other two metaheuristics and the Stop-and-Stair algorithm, and achieves competitive influence spread to CELF (Cost-Effective Lazy Forward) but has less time computation than CELF.
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