Mixed heuristic and greedy strategies based algorithm for influence maximization in social networks

被引：1

作者：

Cao J. ^{[1
,2
]}

Min H. ^{[1
,2
]}

Xu S. ^{[1
,2
]}

Liu B. ^{[1
,2
]}

机构：

[1] School of Computer Science and Engineering, Southeast University, Nanjing

[2] College of Software Engineering, Southeast University, Suzhou

来源：

Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2016年 / 46卷 / 05期

关键词：

Dissemination model; Greedy algorithm; Heuristic algorithm; Influence maximization; Social networks;

D O I：

10.3969/j.issn.1001-0505.2016.05.009

中图分类号：

学科分类号：

摘要：

To solve the imbalance problem between the influence scope and the running time of the classic influence maximization algorithms, a mixed heuristic and greedy strategies based algorithm (MHG algorithm) for influence maximization in social networks is proposed. The algorithm considers the advantages on the greedy and heuristic strategies, and the selection of seed nodes is divided into two steps. First, the candidate node set is selected by the heuristic algorithm, and then the final seed node set is deduced from this set by the greedy algorithm. The results show that the MHG algorithm is superior in influence scope compared with current heuristic algorithms. It exhibits the approximate effect of the greedy algorithm, but the running time is obviously lower. Therefore, the MHG algorithm achieves a good balance between the influence scope and the running time. Moreover, the MHG algorithm presents a stable influence scope when running on real data sets and different dissemination models, showing its scalability in large scale social networks. © 2016, Editorial Department of Journal of Southeast University. All right reserved.

引用

页码：950 / 956

页数：6

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