Anomaly detection in heterogeneous bibliographic information networks using co-evolution pattern mining

Abstract

Detecting evolution-based anomalies have emerged as an effective research topic in many domains, such as social and information networks, bioinformatics, and diverse security applications. However, the majority of research has focused on detecting anomalies using evolutionary behavior among objects in a network. The real-world networks are omnipresent, and heterogeneous in nature, while, in these networks, multiple types of objects co-evolve together with their attributes. To understand the anomalous co-evolution of multi-typed objects in a heterogeneous information network (HIN), we need an effective technique that can capture abnormal co-evolution of multi-typed objects. For example, detecting co-evolution-based anomalies in the heterogeneous bibliographic information network (HBIN) can depict better the object-oriented semantics than just scrutinizing the co-author or citation network alone. In this paper, we introduce the novel notion of a co-evolutionary anomaly in the HBIN, detect anomalies using co-evolution pattern mining (CPM), and study how multi-typed objects influence each other in their anomalous declaration by following a special type of HIN called star networks. The influence of three pre-defined attributes namely paper-count, co-author, and venue over target objects is measured to detect co-evolutionary anomalies in HBIN. The anomaly scores are calculated for each 510 target objects and individual influence of attributes is measured for two top target objects in case-studies. It is observed that venue has the most influence on the target objects discussed as case studies, however, about the rest of anomalies in the list, the most anomalous influential attribute could be rather different than the venue. Indeed, the CABIN algorithm constructs the way to find out the most influential attributes in co-evolutionary anomaly detection. Experiments on bibliographic dataset validate the effectiveness of the model and dominance of the algorithm. The proposed technique can be applied on various HINs such as Facebook, Twitter, Delicious to detect co-evolutionary anomalies.