Vairavan Murugappan

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Recent advances in social network analysis methodologies for large (millions of nodes and billions of edges) and dynamic (evolving at different rates) networks have focused on leveraging new high performance architectures, parallel/distributed tools and novel data structures. However, there has been less focus on designing scalable and efficient algorithms(More)
The flood of real time social data, generated by various social media applications and sensors, is enabling researchers to gain critical insights into important social modeling and analysis problems such as the evolution of social relationships and analysis of emergent social processes. However, current computational tools have to address the grand(More)
Insider threats can cause immense damage to organizations of different types, including government, corporate, and non-profit organizations. Being an insider, however, does not necessarily equate to being a threat. Effectively identifying valid threats, and assessing the type of threat an insider presents, remain difficult challenges. In this work, we(More)
Over the past decade, there has been a dramatic increase in the availability of large and dynamic social network datasets. Conducting social network analysis (SNA) on these networks is critical for understanding underlying social phenomena. However, continuously evolving graph structures require massive recomputations and conducting SNA is infeasible if the(More)
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