Anomaly detection in online social networks

By David Savage, Xiuzhen Zhang, Xinghuo Yu, Pauline Lienhua Chou, Qingmai Wang. Soc. Networks, 2014

296 citations recorded by the source corpus.

Anomalies in online social networks can signify irregular, and often illegal behaviour. Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, and as being labelled or unlabelled, and survey methods for detecting these different types of anomalies. We suggest that the detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this feature space. In addition, this paper provides an overview of the types of problems that anomaly detection can address and identifies key areas of future research.

Subfields: Computer Networks and Communications, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/761e52cd4463d3018e1cd59e2829c0018912e864

How researchers see this paper

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

In Computer Networks and Communications

1. Raghavendra Chalapathy
2. Sanjay Chawla
3. Hui Xiong
4. Jun Zhu
5. Jia Wu
6. Quan Z. Sheng
7. Chuan Zhou
8. Shan Xue

In Information Systems

1. Hui Xiong
2. Jun Zhu
3. Jia Wu
4. Quan Z. Sheng
5. Chuan Zhou
6. Juzheng Li
7. Xia Hu
8. Raghavendra Chalapathy

In Statistical and Nonlinear Physics

1. Chuan Zhou
2. Hui Xiong
3. Jun Zhu
4. Jia Wu
5. Quan Z. Sheng
6. Shan Xue
7. Xiaoxiao Ma
8. Jian Yang

Citations in the corpus

• Discriminative Deep Random Walk for Network Classification (2016, background): “Recent efforts have been made in developing statistical models for various network analysis tasks, such as network classification (Neville and Jensen, 2000), content recommendation (Fouss et al., 2007), link prediction (Adamic and Adar, 2003), and anomaly detection (Savage et al., 2014).”
• Deep Representation Learning for Social Network Analysis (2019, background): “While numerous algorithms have been proposed for spotting anomalies and outliers in networks (Savage et al., 2014; Akoglu et al., 2015; Liu N. et al., 2017), anomaly detection methods, based on network embedding techniques, have recently received increased attention (Hu et al.”
• A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, background): “Other surveys are dedicated to particular applications of graph anomaly detection, such as computer network intrusion detection and anomaly detection in online social networks, e.g., [3], [6], [19], and [50], [51], [52], [53], [54].”
• Deep Learning for Anomaly Detection: A Survey (2019, background): “A survey of traditional anomaly detection techniques and its challenges to detect anomalies in social networks is a well studied topic in literature (Liu and Chawla [2017], Savage et al. [2014], Anand et al. [2017], Yu et al. [2016], Cao et al. [2018b], Yu et al. [2016]).”