Efficient community detection in multilayer networks using boolean compositions.

Networks (or graphs) are used to model the dyadic relations between entities in complex systems. Analyzing the properties of the networks reveal important characteristics of the underlying system. However, in many disciplines, including social sciences, bioinformatics, and technological systems, multiple relations exist between entities.

Constant community identification in million-scale networks.

The inherently stochastic nature of community detection in real-world complex networks poses an important challenge in assessing the accuracy of the results. In order to eliminate the algorithmic and implementation artifacts, it is necessary to identify the groups of vertices that are always clustered together, independent of the community detection algorithm used. Such groups of vertices are called Current approaches for finding constant communities are In this paper, we use binary edge classification to find constant communities.

A New Augmentation Based Algorithm for Extracting Maximal Chordal Subgraphs.

A graph is chordal if every cycle of length greater than three contains an edge between non-adjacent vertices. Chordal graphs are of interest both theoretically, since they admit polynomial time solutions to a range of NP-hard graph problems, and practically, since they arise in many applications including sparse linear algebra, computer vision, and computational biology. A maximal chordal subgraph is a chordal subgraph that is not a proper subgraph of any other chordal subgraph.

Constant communities in complex networks.

Identifying community structure is a fundamental problem in network analysis. Most community detection algorithms are based on optimizing a combinatorial parameter, for example modularity. This optimization is generally NP-hard, thus merely changing the vertex order can alter their assignments to the community.