Game theory based algorithms for community detection

The problem of community detection is important as it helps in understanding the spread of information in a social network. All real complex networks have an inbuilt structure which captures and characterizes the network dynamics between its nodes. Linkages are more likely to form between similar nodes, leading to the formation of some community structure which characterizes the network dynamic. The more friends they have in common, the more the influence that each person can exercise on the other. People use their attributes to assess the similarity of the other people with them, similarly, the attributes are also influenced with the people they interact and the network structure. Hence, we assume that communities capture homophily as people of the same community share a lot of similar features. The contributions of my thesis are as follows: • We propose a disjoint community detection algorithm, NashDisjoint that detects disjoint communities in any given network. • We evaluate the algorithm NashDisjoint on the standard LFR benchmarks, and we find that our algorithm works at least as good as that of the state of the art algorithms for the mixing factors less than 0.55 in all the cases. • On Real Social Networks, we observe that the modularity values for the community structure detected by our algorithm NashDisjoint is in comparison to that of one of the best modularity optimization algorithms so far. • We propose an overlapping community detection algorithm NashOverlap to detect the overlapping communities in any given network. • We evaluate the algorithm NashOverlap on the standard LFR benchmarks and we find that our algorithm works far better than the state of the art algorithms in around 152 different scenarios, generated by varying the number of nodes, mixing factor and overlapping membership. • The algorithm NashDisjoint and NashOverlap are modeled as a sequence of weighted potential games. • We run our algorithm NashOverlap on DBLP dataset to detect the top collaboration groups. We have identified a giant component as a top collaboration group and the second largest collaboration group is much smaller than the giant component. This leads to a reasoning which says that the scientific collaboration network is highly connected and there is a possibility of more interdisciplinary work in the future which is a good sign for the development of science. The diameter of the largest collaboration group for both the datasets is between 9 and 11 and has the average path length between 4 and 5, which strengthens the above argument. Also, the average number of intermediate researchers that connect any pair of researchers in the giant component is quite less. • These results of our algorithm on DBLP collaboration network are compared with the results of the COPRA algorithm.

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