PROJECT TITLE :

A Game Theory Inspired Approach to Stable Core Decomposition on Weighted Networks

ABSTRACT:

Meso-scale structural analysis, like core decomposition has uncovered groups of nodes that play important roles in the underlying complex systems. The existing core decomposition approaches generally specialise in node properties like degree and strength. The node centric approaches will solely capture a restricted information regarding the local neighborhood topology. In the present work, we propose a cluster density primarily based core analysis approach that overcome the drawbacks of the node centric approaches. The proposed algorithmic approach focuses on weight density, cohesiveness, and stability of a substructure. The strategy also assigns an unique score to every node that rank the nodes primarily based on their degree of core-ness. To see the correctness of the proposed method, we tend to propose a synthetic benchmark with planted core structure. A performance check on the null model is administered using a weighted lattice while not core structures. We have a tendency to any test the soundness of the approach against random noise. The experimental results prove the superiority of our algorithm over the state-of-the-arts. We have a tendency to finally analyze the core structures of several standard weighted network models and real life weighted networks. The experimental results reveal vital node ranking and hierarchical organization of the complex networks, that provide us higher insight about the underlying systems.