Personalized community detection in scholarly network

Gao, Zheng; Liu, Xiaozhong

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https://hdl.handle.net/2142/98863 Copy

Description

Title

Personalized community detection in scholarly network

Author(s)

• Gao, Zheng
• Liu, Xiaozhong

Issue Date

2017

Keyword(s)

• Personalized community detection
• Graph mining
• Network analysis

Abstract

Most graph clustering methods partition a network into communities based solely on the topology and structure of the network. Due to this, the means via which communities are detected on a network are insensitive to the preferences of a user who is searching the network with a specific, personalized information need. Such partition algorithms may be of diminished value for scholars exploring networks of research if these scholars possess prior preferences on what information they consider relevant. To better address this type of information seeking behavior, we introduce a personalized community detection algorithm that provides higher-resolution partitioning of areas of the network that are more relevant to a provided seed query. This algorithm utilizes the divisive Girvan-Newman approach but incorporates a user's personal preferences as a prior. We show that this personalized algorithm can produce a more fine-tuned partition of a scholarly network when compared to existing prior-insensitive approaches.

Publisher

iSchools

Series/Report Name or Number

iConference 2017 Proceedings Vol. 2

Type of Resource

text

Language

en

Permalink

http://hdl.handle.net/2142/98863

Copyright and License Information

Copyright 2017 is held by Zheng Gao and Xiaozhong Liu.

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