ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS

TL;DR

This paper introduces ABACUS, an algorithm for discovering communities in multidimensional networks by leveraging frequent pattern mining of monodimensional community memberships, enabling detection of meaningful groups beyond dense connectivity.

Contribution

The paper proposes a novel concept of multidimensional communities and an algorithm that applies apriori-based pattern mining to identify them in complex networks.

Findings

Communities can be effectively identified through shared memberships across dimensions.

ABACUS outperforms traditional methods relying solely on network density.

Experiments demonstrate the meaningfulness of multidimensional community detection.

Abstract

Community Discovery in complex networks is the problem of detecting, for each node of the network, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive, or, more in general, similar, according to a similarity function. So far, the problem has been widely studied in monodimensional networks, i.e. networks where only one connection between two entities can exist. However, real networks are often multidimensional, i.e., multiple connections between any two nodes can exist, either reflecting different kinds of relationships, or representing different values of the same type of tie. In this context, the problem of Community Discovery has to be redefined, taking into account multidimensional structure of the graph. We define a new concept of community that groups together nodes sharing memberships to the same monodimensional communities in the different single dimensions. As we show, such communities are meaningful and able to group highly correlated nodes, even if they might not be connected in any of the monodimensional networks. We devise ABACUS (Apriori-BAsed Community discoverer in mUltidimensional networkS), an algorithm that is able to extract multidimensional communities based on the apriori itemset miner applied to monodimensional community memberships. Experiments on two different real multidimensional networks confirm the meaningfulness of the introduced concepts, and open the way for a new class of algorithms for community discovery that do not rely on the dense connections among nodes.