Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data

TL;DR

This paper introduces a spectral learning algorithm to identify discriminative subnetworks in global-state network data, enabling effective classification by optimizing a low-dimensional subspace that captures shared topology and discriminative features.

Contribution

It proposes a novel spectral graph learning method with a matrix eigen-decomposition approach to discover discriminative subnetworks in global-state networks, addressing the exponential subnetwork search challenge.

Findings

Efficient algorithm for subnetwork discrimination via eigen-decomposition.

Effective classification of global network states using learned low-dimensional subspace.

Addresses the exponential complexity of subnetwork search with a spectral approach.

Abstract

Data mining practitioners are facing challenges from data with network structure. In this paper, we address a specific class of global-state networks which comprises of a set of network instances sharing a similar structure yet having different values at local nodes. Each instance is associated with a global state which indicates the occurrence of an event. The objective is to uncover a small set of discriminative subnetworks that can optimally classify global network values. Unlike most existing studies which explore an exponential subnetwork space, we address this difficult problem by adopting a space transformation approach. Specifically, we present an algorithm that optimizes a constrained dual-objective function to learn a low-dimensional subspace that is capable of discriminating networks labelled by different global states, while reconciling with common network topology sharing across instances. Our algorithm takes an appealing approach from spectral graph learning and we show that the globally optimum solution can be achieved via matrix eigen-decomposition.