Discovering Functional Communities in Social Media

被引：0

作者：

Thompson, Brian ^{[1
]}

Ness, Linda ^{[2
]}

Shallcross, David ^{[2
]}

Bassu, Devasis ^{[3
]}

机构：

[1] Rutgers State Univ, Dept Comp Sci, Piscataway, NJ 08854 USA

[2] Appl Commun Sci, Basking Ridge, NJ 07920 USA

[3] AIG Inc, New York, NY 10005 USA

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW) | 2015年

关键词：

ALGORITHMS;

D O I：

10.1109/ICDMW.2015.92

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose an approach for discovering functional communities in social media by identifying groups of users who interact with similar content, represented as dense biclusters in a user-content matrix. We present a heuristic algorithm to efficiently search the space of possible co-clusterings for one which maximizes the value of a given metric, along with a new class of co-clustering metrics that are more suitable for this task than existing metrics. We evaluate our approach using synthetic and real-world datasets.

引用

页码：917 / 924

页数：8

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