Outlier Detection from Network Data with Subnetwork Interpretation

TL;DR

This paper introduces a novel algorithm for outlier detection in network data that identifies discriminative subnetworks to explain anomalies, outperforming existing methods in accuracy and interpretability.

Contribution

The paper presents a new network regression-based algorithm that detects outliers and interpretable subnetworks, with proven convergence to a global optimum.

Findings

Outperforms baseline methods in real-world datasets

Discovers highly relevant and interpretable subnetworks

Effective in high-dimensional network data

Abstract

Detecting a small number of outliers from a set of data observations is always challenging. This problem is more difficult in the setting of multiple network samples, where computing the anomalous degree of a network sample is generally not sufficient. In fact, explaining why the network is exceptional, expressed in the form of subnetwork, is also equally important. In this paper, we develop a novel algorithm to address these two key problems. We treat each network sample as a potential outlier and identify subnetworks that mostly discriminate it from nearby regular samples. The algorithm is developed in the framework of network regression combined with the constraints on both network topology and L1-norm shrinkage to perform subnetwork discovery. Our method thus goes beyond subspace/subgraph discovery and we show that it converges to a global optimum. Evaluation on various real-world network datasets demonstrates that our algorithm not only outperforms baselines in both network and high dimensional setting, but also discovers highly relevant and interpretable local subnetworks, further enhancing our understanding of anomalous networks.