Sai Harsh Tondomker received his MS in Computer Science and Engineering (CSE). His research work was supervised by Dr. Kishore Kothapalli. Here’s a summary of Sai Harsh Tondomker’s  MS  thesis, Identifying Who is important in my world as explained by him: 

Centrality plays an essential role in identifying the important nodes in social networks. In this thesis, we propose a new flavor of centrality, where we focus on farness and closeness centrality, which identify closer nodes in a network. Since closeness-centrality is a fundamental problem in graph theory, several algorithms exist for this problem for both static and dynamic environments. Recent algorithms for computing centrality have relied heavily on breadth-first search (BFS) traversals of the input graph. Usually, a significant portion of the algorithm’s overall run-time is spent on BFS operations. We have come up with a framework (called BRICS) based on structural properties of the graph, which not only reduces the time taken for single BFS traversal but also reduces the overall number of traversals. Since finding the exact values of centrality requires considerable computational time, we have devised an estimation algorithm with a reasonable trade-off between accuracy and speedup. Additionally, we have created a GPU algorithm to update closeness-centrality in a dynamic setting when a batch of edges are added or deleted, by extending the algorithm of Sarıy¨uce et al. (CLUSTER 2013), an incremental algorithm (single edge update) to the batch update. We have implemented our algorithm in Nvidia Tesla V100 GPU parallel architecture with 80 SMXs. We experimented on five different types of real-world graphs and varying the optimization techniques to achieve the max speed up. Our experimental results show that we were able to achieve a maximum of 12.54x speedup on computing fairness centrality and 53x on updating closeness-centrality in the dynamic setting.