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Outlier Detection from Network Data with Subnetwork Interpretation
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
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