Radar: Residual Analysis for Anomaly Detection in Attributed Networks

By Jundong Li, Harsh Dani, Xia Hu, Huan Liu. International Joint Conference on Artificial Intelligence, 2017

295 citations recorded by the source corpus.

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 types of anomalies in real-world attributed networks coexist, the assumption that priori knowledge regarding anomalies is available does not hold. In this paper, we investigate the problem of anomaly detection in attributed networks generally from a residual analysis perspective, which has been shown to be effective in traditional anomaly detection problems. However, it is a non-trivial task in attributed networks as interactions among instances complicate the residual modeling process. Methodologically, we propose a learning framework to characterize the residuals of attribute information and its coherence with network information for anomaly detection. By learning and analyzing the residuals, we detect anomalies whose behaviors are singularly different from the majority. Experiments on real datasets show the effectiveness and generality of the proposed framework.

Subfields: Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a5160db2d31d47545fb68a4a17580969e1e02f80

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jiliang Tang
2. Huan Liu
3. Jundong Li
4. Chuan Zhou
5. Xia Hu
6. Fred Morstatter
7. Shirui Pan
8. Yixin Liu

In Computer Networks and Communications

1. Huan Liu
2. Jundong Li
3. Hui Xiong
4. Yi Chang
5. Jia Wu
6. Quan Z. Sheng
7. Jiliang Tang
8. Chuan Zhou

In Statistical and Nonlinear Physics

1. Huan Liu
2. Jundong Li
3. Chuan Zhou
4. Fred Morstatter
5. Xia Hu
6. Jiliang Tang
7. Shirui Pan
8. Yixin Liu

Citations in the corpus

• Deep Anomaly Detection on Attributed Networks (2019, methodology): “• Radar [17] is the state-of-the-art unsupervised anomaly detection framework for attributed networks.”
• A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, methodology): “Many machine learning techniques, such as matrix factorization [41], [42] and SVM [43], have also been applied to detect graph anomalies.”
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (2021, methodology): “ROC-AUC is widely used in previous works for the evaluation of anomaly detection performance [13], [17], [18].”
• Attributed Network Embedding for Learning in a Dynamic Environment (2017, background): “Also, many real-world applications, such as node classi€cation, community detection, topic modeling and anomaly detection [18, 22, 24, 28, 51], have shown signi€cant improvements by modeling such correlations.”
• Feature Selection (2016, methodology): “…Nguyen et al. 2014; Shishkin et al. 2016; Gao et al. 2016), feature ability to preserve data manifold structure (He et al. 2005; Zhao and Liu 2007; Gu et al. 2011b; Jiang and Ren 2011), and feature ability to reconstruct the original data (Masaeli et al. 2010; Farahat et al. 2011; Li et al. 2017a).”