Skip to Main content Skip to Navigation
Conference papers

Crawling and Detecting Community Structure in Online Social Networks Using Local Information

Abstract : As Online Social Networks (OSNs) become an intensive subject of research for example in computer science, networking, social sciences etc., a growing need for valid and useful datasets is present. The time taken to crawl the network is however introducing a bias which should be minimized. Usual ways of addressing this problem are sampling based on the nodes (users) ids in the network or crawling the network until one “feels” a sufficient amount of data has been obtained.In this paper we introduce a new way of directing the crawling procedure to selectively obtain communities of the network. Thus, a researcher is able to obtain those users belonging to the same community and rapidly begin with the evaluation. As all users involved in the same community are crawled first, the bias introduced by the time taken to crawl the network and the evolution of the network itself is less.Our presented technique is also detecting communities during runtime. We compare our method called Mutual Friend Crawling (MFC) to the standard methods Breadth First Search (BFS) and Depth First Search (DFS) and different community detection algorithms. The presented results are very promising as our method takes only linear runtime but is detecting equal structures as modularity based community detection algorithms.
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download

https://hal.inria.fr/hal-01531140
Contributor : Hal Ifip <>
Submitted on : Thursday, June 1, 2017 - 11:35:12 AM
Last modification on : Saturday, November 18, 2017 - 6:16:02 PM
Long-term archiving on: : Wednesday, September 6, 2017 - 6:24:19 PM

File

978-3-642-30045-5_5_Chapter.pd...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Norbert Blenn, Christian Doerr, Bas Kester, Piet Mieghem. Crawling and Detecting Community Structure in Online Social Networks Using Local Information. 11th International Networking Conference (NETWORKING), May 2012, Prague, Czech Republic. pp.56-67, ⟨10.1007/978-3-642-30045-5_5⟩. ⟨hal-01531140⟩

Share

Metrics

Record views

90

Files downloads

265