Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification

by   Bokai Cao, et al.

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.


Discriminative Feature Selection for Uncertain Graph Classification

Mining discriminative features for graph data has attracted much attenti...

Broad Learning for Healthcare

A broad spectrum of data from different modalities are generated in the ...

Jointly learning relevant subgraph patterns and nonlinear models of their indicators

Classification and regression in which the inputs are graphs of arbitrar...

Automatic View Selection in Graph Databases

Recently, several works have studied the problem of view selection in gr...

TED: Towards Discovering Top-k Edge-Diversified Patterns in a Graph Database

With an exponentially growing number of graphs from disparate repositori...

Graph embedding using multi-layer adjacent point merging model

For graph classification tasks, many traditional kernel methods focus on...

DSL: Discriminative Subgraph Learning via Sparse Self-Representation

The goal in network state prediction (NSP) is to classify the global sta...

Please sign up or login with your details

Forgot password? Click here to reset