摘要
【目的】针对影响力最大化问题中贪心算法时间效率低的局限,提出基于重叠社区的影响力最大化算法。【方法】基于重叠社区,综合传播度最大的节点和重叠节点选出候选种子集,并采用CELF算法确定最优种子集,从而提高影响范围。【结果】实验数据表明,在亚马逊数据集上IM-BOC算法运行时间最大幅度能够提高约89%。【局限】仅凭社区节点的数量分配候选种子节点的数量,可能存在一定误差。【结论】基于重叠社区的IM-BOC算法在保证影响范围的前提下,适用于大型社交网络。
[Objective] This paper proposes a new algorithm for influence maximization based on overlapping community, called IM-BOC algorithm, aiming to the low efficiency of greedy algorithm.[Methods] This method selects candidate seed set by combing propagation degree and k-core firstly, then it utilizes CELF algorithm to ensure the optimal seed set, which can improve both efficiency and accuracy.[Results] The experimental results show that running time of our algorithm can improve about 89% when facing Amazon dataset.[Limitations] Our IM-BOC algorithm allocates the number of candidate seeds only according to the number of community nodes, which has insufficient theoretical evidence.[Conclusions] IM-BOC algorithm is applicable to large scale networks under the premise of ensuring the influence spread.
作者
仇丽青
贾玮
范鑫
Qiu Liqing;Jia Wei;Fan Xin(College of Computer Science and Engineering, Shandong University of Science and Technology,Qingdao 266590, China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2019年第7期94-102,共9页
Data Analysis and Knowledge Discovery
基金
2018年度青岛市社会科学规划项目“社会网络视角下的情感图谱研究:以突发公共卫生事件为例”(项目编号:QDSKL1801103)
国家自然科学基金青年基金项目“时间演化尺度下大规模社会网络特征分析与社区结构挖掘”(项目编号:622814971)
山东科技大学优秀教学团队建设计划资助项目“嵌入式计算机技术系列课程群教学团队,程序设计技术系列课程群教学团队”(项目编号:JXTD20170503,JXTD20180503)的研究成果之一
关键词
社交网络
重叠社区
影响力最大化
Social Network
Overlapping Community
Influence Maximization
