Content Patterns in Topic-Based Overlapping Communities
Understanding the underlying community structure is an important challenge in social network analysis. Most state-of-the-art algorithms only consider structural properties to detect disjoint subcommunities and do not include the fact that people can belong to more than one community and also ignore...
| Main Authors: | , |
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| Format: | Online |
| Language: | English |
| Published: |
Hindawi Publishing Corporation
2014
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4000663/ |
| Summary: | Understanding the underlying community structure is an important challenge in social network
analysis. Most state-of-the-art algorithms only consider structural properties to detect disjoint
subcommunities and do not include the fact that people can belong to more than one community
and also ignore the information contained in posts that users have made. To tackle this problem,
we developed a novel methodology to detect overlapping subcommunities in online social networks
and a method to analyze the content patterns for each subcommunities using topic models.
This paper presents our main contribution, a hybrid algorithm which combines two different overlapping
sub-community detection approaches: the first one considers the graph structure of the
network (topology-based subcommunities detection approach) and the second one takes the textual
information of the network nodes into consideration (topic-based subcommunities detection
approach). Additionally we provide a method to analyze and compare the content generated.
Tests on real-world virtual communities show that our algorithm outperforms other methods. |
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