Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification

TL;DR

This paper introduces a novel multi-side-view guided subgraph selection method that enhances neurological disorder classification by integrating brain network data with additional clinical and cognitive measures.

Contribution

It proposes a new feature evaluation criterion and an efficient branch-and-bound algorithm to select discriminative subgraph patterns using multiple side views, improving disease diagnosis accuracy.

Findings

Enhanced classification performance on neurological disorder datasets

Selected subgraph patterns are relevant to disease diagnosis

Efficient search algorithm for multi-view subgraph selection

Abstract

Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification, in addition to the brain networks derived from neuroimaging data, hundreds of clinical, immunologic, serologic and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph features by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.