Apurba Das
Papers
1
Total Citations
2
H-Index
1
About
Apurba Das is a researcher whose work lies at the intersection of graph analytics and high-performance computing, with a particular focus on triangle counting and its applications. His most-cited paper, "Agent-Based Triangle Counting: Unlocking Truss Decomposition, Triangle Centrality, and Local Clustering Coefficient" (2024), demonstrates his innovative approach to solving fundamental graph problems. In this work, Das introduces an agent-based framework that not only efficiently counts triangles in large graphs but also unlocks advanced analytics including truss decomposition—a method for identifying tightly-knit subgraphs—along with triangle centrality and local clustering coefficient calculations. This contribution is significant because triangle counting serves as a cornerstone for understanding graph structure, with applications spanning social network analysis, biology, and cybersecurity. Though his citation count is still growing, Das’s work represents a meaningful step toward scalable graph analytics, offering practical solutions for researchers and engineers working with complex networks. His agent-based methodology stands out for its potential to handle massive datasets, positioning him as an emerging voice in the field of graph algorithms and distributed computing.
Research Focus
Key Achievements
Top Papers
- 1