论文类型：论文集
第一作者：张逸飞
通讯作者：Pingpeng Yuan
合写作者：林隆龙,Hai Jin
发表刊物：The 27th International Conference on Database Systems for Advanced Applications（DASFAA 2022）
收录刊物：EI
所属单位：计算机科学与技术学院
学科门类：工学
一级学科：计算机科学与技术
项目来源：自然科学基金
文献类型：C
卷号：13245
关键字：Temporal networks；Community search；k-core
发表时间：2022-04-08
摘要：Community search, retrieving the cohesive subgraph which contains the user-specified query vertex, has been widely touched over the past decades. The existing studies on community search mainly focus on static networks. However, real-world networks, such as scientific cooperation networks and communication networks, usually are temporal networks whose each edge is associated with timestamps. Therefore, the previous methods do not work when handling temporal networks. Inspired by this, we study the problem of identifying the significant engagement community to which the user-specified query belongs. Specifically, given an integer k and a query vertex u, then we search for the subgraph H which satisfies (i) u∈H; (ii) the de-temporal graph of H is a connected k-core; (iii) In H that u has the maximum engagement level. To address our problem, we first develop a top-down greedy peeling algorithm named TDGP, which iteratively removes the vertices with the maximum temporal degree. To further boost the efficiency, we then design a bottom-up local search algorithm named BULS with several powerful pruning strategies. Lastly, we empirically show the superiority of our proposed solutions on six real-world temporal graphs.
发布期刊链接：https://link.springer.com/chapter/10.1007/978-3-031-00123-9_20