Nishith Pathak

TL;DR: A Bayesian generative model for community extraction which naturally incorporates both the link and content information present in the social network and is able to extract well-connected and topically meaningful communities.

TL;DR: The proposed model is shown to be a rapidly mixing Markov chain and the corresponding steady state distribution is used to estimate highly likely states of the cascades' spread in the network.

TL;DR: In this article , the authors bring together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing to identify questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research.

TL;DR: This paper exploits the underlying principle of first order markov model on which PageRank is based, to incrementally compute PageRank for the evolving Web graph, and shows significant speed up in computational cost.

TL;DR: An effective and scalable approach for modeling organizational networks by tapping into an organization's email communication is described and it is shown that these techniques provide sociologists with a new tool to understand organizational networks.