Community Detection in Complex Networks using Randomisation

S, Archana J; Kaimal, M. R.

doi:10.1145/2660859.2660943

Cited by 6 publications

(1 citation statement)

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“…Each of the systems has multiple subsystems. As the data associated with the complex networks evolved to be more heterogeneous and complex, the demand for organizing the complex network into a multilayer network grew [1], [2], [3]. Research in Complex networks has transformed the realworld entities represented from single layer network to multilayer networks by combining the set of nodes exhibiting identical behavior [4].…”

Section: Introductionmentioning

confidence: 99%

Microscopic Structural Analysis of Complex Networks: An Empirical Study Using Motifs

2022

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Complex Networks can depict a clear image of real-world systems. A real-world scenario can be represented a graph with interconnected layers -called a multi-layer network. Finding motifs can give an idea of the topology of complex systems and helps to understand the dynamics in the graphs. Looking at motifs as atoms of the network is helpful to analyze the relationship between nodes, and between layers. In this work, we suggest a sub-graph enumeration approach to find and count the motifs in multi-layer network. The proposed work has many application in graph mining, particularly to structure and dynamics of complex networks.INDEX TERMS complex networks , isomorphism , graph enumeration , motifs.

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