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Differentially Describing Groups of Graphs


Coupette,  Corinna       
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Coupette, C., Dalleiger, S., & Vreeken, J. (2022). Differentially Describing Groups of Graphs. Retrieved from https://arxiv.org/abs/2201.04064.

Cite as: https://hdl.handle.net/21.11116/0000-000C-165D-4
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 partition 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 perform 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 experiments on a wide
range of synthetic and real-world graph groups, we confirm that Gragra works
well in practice.