A Method for Characterizing Communities in Dynamic Attributed Complex Networks

TL;DR

This paper introduces a novel method for characterizing communities in dynamic attributed complex networks by using sequence-based representations and pattern detection to interpret community structures and identify outliers.

Contribution

It presents a new approach combining temporal, topological, and attribute data into sequences for community characterization and outlier detection in dynamic networks.

Findings

Effective characterization of communities in dynamic attributed networks.

Ability to detect unusual community behaviors and outliers.

Application demonstrated on a scientific collaboration network.

Abstract

Many methods have been proposed to detect communities, not only in plain, but also in attributed, directed or even dynamic complex networks. In its simplest form, a community structure takes the form of a partition of the node set. From the modeling point of view, to be of some utility, this partition must then be characterized relatively to the properties of the studied system. However, if most of the existing works focus on defining methods for the detection of communities, only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either in the type of data they handle, or by the nature of the results they output. In this work, we propose a method to efficiently support such a characterization task. We first define a sequence-based representation of networks, combining temporal information, topological measures, and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset, and use them to characterize the communities. We also show how to detect unusual behavior in a community, and highlight outliers. Finally, as an illustration, we apply our method to a network of scientific collaborations.