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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chowdhary, G. Bandyopadhyay, S. |
| Copyright Year | 2015 |
| Description | Author affiliation: Machine Intell. Unit, Indian Stat. Inst., Kolkata, India (Chowdhary, G.; Bandyopadhyay, S.) |
| Abstract | On-line social networks mostly allow individuals to extend friend requests to all forms of possible connections including those related to official purposes, interests, family relations, friendships, and acquaintances. One requires to mine relevant connections in order to make reliable and meaningful interpretations following network analysis. Most networks lack weight assignments that mark the strength of a connection. Thus there is a requirement of methods that can effectively identify essential edges from only the topological information available. The method discussed in this article identifies unique and high number of mutual connections through weighted self-information. The extracted skeleton network has highly reduced number of edges, still conserving the centrality distributions as far as possible. The method used is applied locally to each node, to extract connections relevant to every node. Results are demonstrated on five datasets which show that the proposed method is able to eliminate a large number of irrelevant edges. The method is also found to scale well to large datasets. |
| Starting Page | 2398 |
| Ending Page | 2403 |
| File Size | 311911 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479999262 |
| DOI | 10.1109/BigData.2015.7364033 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-10-29 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Uncertainty Network topology Relevant connections Self-information Social network Big data Network skeleton Topology Reliability YouTube |
| Content Type | Text |
| Resource Type | Article |
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