4 research outputs found
Explainable Classification of Brain Networks via Contrast Subgraphs
Mining human-brain networks to discover patterns that can be used to
discriminate between healthy individuals and patients affected by some
neurological disorder, is a fundamental task in neuroscience. Learning simple
and interpretable models is as important as mere classification accuracy. In
this paper we introduce a novel approach for classifying brain networks based
on extracting contrast subgraphs, i.e., a set of vertices whose induced
subgraphs are dense in one class of graphs and sparse in the other. We formally
define the problem and present an algorithmic solution for extracting contrast
subgraphs. We then apply our method to a brain-network dataset consisting of
children affected by Autism Spectrum Disorder and children Typically Developed.
Our analysis confirms the interestingness of the discovered patterns, which
match background knowledge in the neuroscience literature. Further analysis on
other classification tasks confirm the simplicity, soundness, and high
explainability of our proposal, which also exhibits superior classification
accuracy, to more complex state-of-the-art methods.Comment: To be published at KDD 202
Mining Density Contrast Subgraphs
Dense subgraph discovery is a key primitive in many graph mining
applications, such as detecting communities in social networks and mining gene
correlation from biological data. Most studies on dense subgraph mining only
deal with one graph. However, in many applications, we have more than one graph
describing relations among a same group of entities. In this paper, given two
graphs sharing the same set of vertices, we investigate the problem of
detecting subgraphs that contrast the most with respect to density. We call
such subgraphs Density Contrast Subgraphs, or DCS in short. Two widely used
graph density measures, average degree and graph affinity, are considered. For
both density measures, mining DCS is equivalent to mining the densest subgraph
from a "difference" graph, which may have both positive and negative edge
weights. Due to the existence of negative edge weights, existing dense subgraph
detection algorithms cannot identify the subgraph we need. We prove the
computational hardness of mining DCS under the two graph density measures and
develop efficient algorithms to find DCS. We also conduct extensive experiments
on several real-world datasets to evaluate our algorithms. The experimental
results show that our algorithms are both effective and efficient.Comment: Full version of an ICDE'18 pape