Differentially Describing Groups of Graphs

Coupette, Corinna and Dalleiger, Sebastian and Vreeken, Jilles
(2022) Differentially Describing Groups of Graphs.
In: AAAI Conference on Artificial Intelligence.
Conference: AAAI National Conference of the American Association for Artificial Intelligence

[img] Text
gragra-coupette,dalleiger,vreeken-preprint.pdf - Draft Version

Download (604kB)

Abstract

How does neural connectivity in autistic children differ from neural connectivity in healthy children or autistic youths? What patterns in global trade networks are shared across classes of goods, and how do these patterns change over time? Answering questions like these requires us to differentially describe groups of graphs: Given a set of graphs and a parti- tion of these graphs into groups, discover what graphs in one group have in common, how they systematically differ from graphs in other groups, and how multiple groups of graphs are related. We refer to this task as graph group analysis, which seeks to describe similarities and differences between graph groups by means of statistically significant subgraphs. To per- form graph group analysis, we introduce GRAGRA, which uses maximum entropy modeling to identify a non-redundant set of subgraphs with statistically significant associations to one or more graph groups. Through an extensive set of ex- periments on a wide range of synthetic and real-world graph groups, we confirm that GRAGRA works well in practice.

Actions

Actions (login required)

View Item View Item