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Graph Mining Approaches to Study Volunteer Relationships in Sourceforge.net
Abstract
The contribution of volunteers in the development of Free and Open Source Software in Sourceforge.net is studied in this paper. Using Social Network analysis, the small set of developers who can maximize the information flow in the network are discovered. The propagation of top developers across past three years are also studied. The four algorithms used to find top influential developers gives almost similar results. The movement of top developers over past years was also consistent. Influential nodes in a network are very important to diffuse influence on the rest of the network. They are most often highly connected within the network. The existing algorithms are efficient to identify them. However, the challenge is in selecting a seed set that can spread the influence instantaneously with least effort. In this paper, a method is defined to spread influence on the entire network by selecting the least number of non-overlapping influential nodes faster than a well known existing algorithm. Further to this, the number of clusters in the network is also determined simultaneously from the seed set of the networks.
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