Granular Model for Social Networks, Target Set Selection and Fuzzy-Rough Community Detection


  • At: PhD defence viva
  • Place:Department of Computer Science and Engineering, Jadavpur University
  • City: Kolkata, India
  • Presented on June 30, 2017
  • Thesis Copy:Click Here


The present thesis provides some new results of investigations, both theoretical and experimental, in the field of social network analysis within the purview of information diffusion and community structure. Two major problems for network analysis, namely, target set selection and community detection are addressed. The theory of probability is used to integrate the property of information diffusion with the centrality measures and then these are used to identify the influential nodes (target set) in large scale social networks. The upper bound of the influence is determined. List of nodes thus identified, sometimes may contain unwanted ones instead of higher influencing individuals. A new greedy strategy based on set theory and mathematical logic is developed to identify and deprecate such nodes; thereby improving the quality of the target set.

Fuzzy granulation theory is used to model uncertainties in social networks. This provides a new knowledge representation scheme of relational data by taking care of the indiscernibility among the actors as well as the fuzziness in their relations. Various measures of network are defined on this new model. Within the context of this knowledge framework of social network, algorithms for target set selection and community detection are developed. Here the target sets are determined using the new measure granular degree, whereas it is granular embeddedness, together with granular degree, which is used for detecting various overlapping communities. The resulting community structures have a fuzzy-rough set theoretic description which allows a node to be a member of multiple communities with different memberships of association only if it falls in the (rough upper − rough lower) approximate region. A new index, called normalized fuzzy mutual information is introduced which can be used to quantify the similarity between two fuzzy partition matrices, and hence the quality of the communities detected.

Complexity analysis is provided for all the algorithms. The issue of scalability of the granular model is addressed. Superiority of the algorithms over some state of the art algorithms is demonstrated with extensive experimental results.


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