Exploring and mining attributed sequences of interactions

TL;DR

This paper introduces a novel method for mining closed patterns in sequences of attributed interactions modeled as stream graphs, extending formal concept analysis to analyze evolving interaction patterns over time.

Contribution

It extends formal concept analysis to stream graphs and proposes algorithms for enumerating and selecting relevant closed patterns in labeled interaction sequences.

Findings

Algorithms successfully identify meaningful interaction patterns.

Method is feasible on real-world datasets.

Patterns are relevant for understanding social and citation networks.

Abstract

We are faced with data comprised of entities interacting over time: this can be individuals meeting, customers buying products, machines exchanging packets on the IP network, among others. Capturing the dynamics as well as the structure of these interactions is of crucial importance for analysis. These interactions can almost always be labeled with content: group belonging, reviews of products, abstracts, etc. We model these stream of interactions as stream graphs, a recent framework to model interactions over time. Formal Concept Analysis provides a framework for analyzing concepts evolving within a context. Considering graphs as the context, it has recently been applied to perform closed pattern mining on social graphs. In this paper, we are interested in pattern mining in sequences of interactions. After recalling and extending notions from formal concept analysis on graphs to stream graphs, we introduce algorithms to enumerate closed patterns on a labeled stream graph, and introduce a way to select relevant closed patterns. We run experiments on two real-world datasets of interactions among students and citations between authors, and show both the feasibility and the relevance of our method.