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Empirical Analysis of Seed Selection Criterion in Influence Mining for Different Classes of Networks

Recent years have seen social networks gain lot of popularity to share information, connecting millions of people from all over the world. Studying the spread of information, or Information Diffusion in these networks has shaped into a well known field of study with numerous applications in areas such as marketing, politics, and personality evaluation. Researchers have studied information diffusion under various models and opted centrality-based algorithms that offer better results over many other approaches. These algorithms try to select initial seed nodes effectively so as to maximize influence in a network in minimum time. However, since different networks follow different structural properties, motivating the need to study different diffusion strategies for networks with different structural properties. In this paper, we aim to empirically analyze four different measures of centrality to select seed vertices for influence mining on four classes of networks: small-World networks, scale-free networks, small world-scale free networks and random networks. These networks are generated equivalent in size to four semantically different real world social networks. We use two most frequently used diffusion models: Independent Cascade model and Linear Threshold model for analysis. Our results show interesting behavior of various centrality measures for the above said classes of networks.