Complex social network partition for balanced subnetworks

Hao Lan Zhang, Jiming LIU, Chunyu Feng, Chaoyi Pang, Tongliang Li, Jing He*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Complex social network analysis methods have been applied extensively in various domains including online social media, biological complex networks, etc. Complex social networks are facing the challenge of information overload. The demands for efficient complex network analysis methods have been rising in recent years, particularly the extensive use of online social applications, such as Flickr, Facebook and LinkedIn. This paper aims to simplify the network complexity through partitioning a large complex network into a set of less complex networks. Existing social network analysis methods are mainly based on complex network theory and data mining techniques. These methods are facing the challenges while dealing with extreme large social network data sets. Particularly, the difficulties of maintaining the statistical characteristics of partitioned sub-networks have been increasing dramatically. The proposed Normal Distribution (ND) based method can balance the distribution of the partitioned sub-networks according to the original complex network. Therefore, each subnetwork can have its degree distribution similar to that of the original network. This can be very beneficial for analyzing sub-divided networks and potentially reducing the complexity in dynamic online social environment.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4177-4182
Number of pages6
ISBN (Electronic)9781509006199
DOIs
Publication statusPublished - 31 Oct 2016
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2016-October

Conference

Conference2016 International Joint Conference on Neural Networks, IJCNN 2016
Country/TerritoryCanada
CityVancouver
Period24/07/1629/07/16

Scopus Subject Areas

  • Software
  • Artificial Intelligence

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