Uncovering Attribute-Driven Active Intimate Communities

Md Musfique Anwar, Chengfei Liu, Jianxin Li

Research output: Chapter in Book/Conference paperConference paperpeer-review

8 Citations (Scopus)


Most existing studies in community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. However, the bulk of literature ignores the interaction strength among the users in the retrieved communities. As a result, many members of the detected communities do not interact frequently to each other. This inactivity will create problem for online advertisers as they require the community to be highly interactive to efficiently diffuse marketing information. In this paper, we study the problem of detecting attribute-driven active intimate community, that is, for a given input query consisting a set of attributes, we want to find densely-connected communities in which community members actively participate as well as have strong interaction (intimacy) with respect to the given query attributes. We design a novel attribute relevance intimacy score function for the detected communities and establish its desirable properties. To this end, we use an indexed based solution to efficiently discover active intimate communities. Extensive experiments on real data sets show the effectiveness and performance of our proposed method. © Springer International Publishing AG, part of Springer Nature 2018.
Original languageEnglish
Title of host publicationDatabases Theory and Applications
Subtitle of host publication29th Australasian Database Conference, ADC 2018
EditorsJunhu Wang, Gao Cong, Jinjun Chen, Jianzhong Qi
Place of PublicationCham
Number of pages14
ISBN (Electronic)9783319920139
ISBN (Print)9783319920122
Publication statusPublished - 24 May 2018
Event29th Australasian Database Conference - Gold Coast, Australia
Duration: 24 May 201827 May 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10837 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference29th Australasian Database Conference
CityGold Coast


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