Chandni Saxena — Page 2

arXiv2018

Stability of Local Information based Centrality Measurements under Degree Preserving Randomizations

Node centrality is one of the integral measures in network analysis with wide range of applications from socio-economic to personalized recommendation. We argue that an effective centrality measure should undertake stability even under information loss or noise introduced in the network. With six local information based centrality metric, we investigate the effect of varying assortativity while keeping degree distribution unchanged, using networks with scale free and exponential degree distribution. This model provides a novel scope to analyze stability of centrality metric which can further finds many applications in social science, biology, information science, community detection and so on.

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arXiv2018

Group based Centrality for Immunization of Complex Networks

Network immunization is an extensively recognized issue in several domains like virtual network security, public health and social media, to deal with the problem of node inoculation so as to minimize the transmission through the links existed in these networks. We aim to identify top ranked nodes to immunize networks, leading to control the outbreak of epidemics or misinformation. We consider group based centrality and define a heuristic objective criteria to establish the target of key nodes finding in network which if immunized result in essential network vulnerability. We propose a group based game theoretic payoff division approach, by employing Shapley value to assign the surplus acquired by participating nodes in different groups through the positional power and functional influence over other nodes. We tag these key nodes as Shapley Value based Information Delimiters (SVID). Experiments on empirical data sets and model networks establish the efficacy of our proposed approach and acknowledge performance of node inoculation to delimit contagion outbreak.

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