Multi-Scale Anomaly Detection on Attributed Networks

Accelerated Local Anomaly Detection via Resolving Attributed Networks

Attributed networks, in which network connectivity and node attributes are available, have been increasingly used to model real-world information systems, such as social media and e-commerce platforms. While outlier detection has been extensively studied to identify anomalies that deviate from certain chosen background, existing algorithms cannot be directly applied on attributed networks due t...

Radar: Residual Analysis for Anomaly Detection in Attributed Networks

Attributed networks are pervasive in different domains, ranging from social networks, gene regulatory networks to financial transaction networks. This kind of rich network representation presents challenges for anomaly detection due to the heterogeneity of two data representations. A vast majority of existing algorithms assume certain properties of anomalies are given a prior. Since various typ...

Efficient Anomaly Detection in Dynamic, Attributed Graphs

When working with large-scale network data, the interconnected entities often have additional descriptive information. This additional metadata may provide insight that can be exploited for detection of anomalous events. In this paper, we use a generalized linear model for random attributed graphs to model connection probabilities using vertex metadata. For a class of such models, we show that ...

Experimenting with Anomaly Detection by Mining Large-scale Information Networks

Social networks have formed the basis of many studies into large networks analysis. Whilst much is already known regarding efficient algorithms for large networks analysis, data mining, knowledge diffusion, anomaly detection, viral marketing, to mention. More recent research is focussing on new classes of efficient approximate algorithms that can scale to billion nodes and edges. To this end, t...

Community Detection and Analysis on Attributed Social Networks