Neural Clustering and Ranking Approach for Gas-Theft Suspect Detection

Abstract

Some boiler room users steal natural gas by refitting equipment without permission in winter, resulting in gas safety hazards and social problems. Instead of random manual on-site inspection, it is crucial to discover gas-theft suspects timely and automatically by analyzing the gas consumption data. Unfortunately, gas-theft behaviors are complex and various, while the caught gas thefts by gas companies are limited. In this paper, we propose a neural clustering and ranking approach to detect gas theft suspects under the positive-unlabeled learning framework. Our approach contains two modules: joint clustering for normal user identification and triplet ranking for suspicious user detection. The former module considers the regular behaviors to distinguish between normal and unstable users by integrating representation learning and clustering. Then, considering the identified normal samples and the labeled gas thefts, the later module excavates the behavior correlations to discover suspects among unstable users through triplet relation ranking. Thus, normal user identification and suspicious user detection are seamlessly connected to overcome the label scarcity problem. We conduct extensive experiments on three real-world datasets, and the results demonstrate the advantages of our approach over various baselines.