Exploring and mining attributed sequences of interactions
This work addresses the analysis of dynamic, attributed interaction data for domains like social networks or citation analysis, but it is incremental as it builds on existing frameworks for stream graphs and formal concept analysis.
The paper tackles the problem of mining patterns in sequences of interactions over time, such as meetings or citations, by modeling them as stream graphs and using extended formal concept analysis to enumerate and select relevant closed patterns, demonstrating feasibility and relevance on real-world datasets of student interactions and author citations.
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.