Interpretable Community Detection for Multilayer Networks
Abstract
Networks arise in many fields and provide a powerful and compact representation of the internal structure of complex systems consisting of agents that interact with each other. Traditional network models employ simple graphs where the nodes are connected to each other by a single, static edge. However, in many contemporary applications, this relatively simple structure cannot capture the diverse nature of the networks. Multilayer networks (MLNs) allow one to represent the interactions between a pair of nodes through multiple types of links, where each type of link reflects a distinct type of interaction and can be separated into its own layer, thereby connecting the same set of nodes in multiple ways. A core task in the complexity reduction of these high-dimensional networks is community detection. Communities can reveal meaningful structure and provide a better understanding of the overall functioning of networks. Current approaches to multilayer community detection are either limited to community detection over the aggregated network or are extensions of single layer community detection methods with simplifying assumptions such as a common community structure across layers. Some of the challenges associated with community detection in MLNs include- i) definition of within-layer and across-layer communities; ii) heterogeneity of node attributes and community structure across layers; iii) robustness and scalability of community detection for large, noisy networks; and iv) interpretability of the detected community structure. The proposed research aims to address these key challenges by developing a unified framework for community detection in multiplex and multilayer networks addressing the issues of heterogeneity in node attributes and community structure, scalability of the algorithms and interpretability of the resulting communities.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 29, 2024
- Source ID
- FA95502310224
Entities
People
- Sara Aviyente
Organizations
- Air Force Office of Scientific Research
- Michigan State University
- United States Air Force