1 citations · 1 across the 8 of their papers we have counts for
4 papers · 1 filter
On Rich Clubs of Path-Based Centralities in Networks
Soumya Sarkar, Animesh Mukherjee, Sanjukta Bhowmick
Many scale-free networks exhibit a rich club structure, where high degree vertices form tightly interconnected subgraphs. In this paper, we explore the emergence of rich clubs in t…
Core2Vec: A core-preserving feature learning framework for networks
Soumya Sarkar, Aditya Bhagwat, Animesh Mukherjee
Recent advances in the field of network representation learning are mostly attributed to the application of the skip-gram model in the context of graphs. State-of-the-art analogues…
Using Core-Periphery Structure to Predict High Centrality Nodes in Time-Varying Networks
Soumya Sarkar, Sandipan Sikdar, Animesh Mukherjee +1
Vertices with high betweenness and closeness centrality represent influential entities in a network. An important problem for time varying networks is to know a-priori, using minim…
ComPAS: Community Preserving Sampling for Streaming Graphs
Sandipan Sikdar, Tanmoy Chakraborty, Soumya Sarkar +2
In the era of big data, graph sampling is indispensable in many settings. Existing sampling methods are mostly designed for static graphs, and aim to preserve basic structural prop…