Community mining in co-authorship network

Community is formed by individuals such that those within a group interact with each other more frequently than with those outside the group. Community mining or detection involves discovering groups in a network where an individual's group memberships are not explicitly given. Much of the research on detecting communities in co-authorship networks has been done using publicly available datasets. Discovering communities by using a publication list available from the researchers homepage is a challenging task As the data available from homepages will be in an unstructured form, an interpreter is required to extract the structured co-authorship network to detect communities. The focus of this work is to detect communities in co-authorship network for which the co-authorship network will be constructed using the publication list of researchers homepage. Network centric community detection algorithms: Multi-dimensional scaling, Block model, Spectral clustering and Modularity maximization was applied on co-authorship network to mine communities. The results of evaluation of these algorithms using the measures Internal density, Expansion and Conductance will be discussed in this work.