Anomaly detection in online social networks (1608.00301v1)

Abstract: 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.

• The paper provides a comprehensive survey of anomaly detection techniques in online social networks, highlighting both static and dynamic methods.
• It details how methods such as ego-net analysis, spectral signal processing, and belief propagation effectively identify fraudulent behaviors.
• The paper outlines future directions, emphasizing the integration of rich network features and the use of synthetic datasets for robust model validation.

Anomaly Detection in Online Social Networks

The detection of anomalies within online social networks represents a critical intersection of computational techniques and social network analysis (SNA). The paper “Anomaly Detection in Online Social Networks” provides a comprehensive survey of the methods and challenges associated with identifying irregular or undesirable behaviors in such networks. These anomalies often signal malicious behaviors, such as spam, predation, and fraud, making robust detection methods an imperative in maintaining the integrity and security of digital environments.

Overview of Anomaly Characterization

Anomalies in social networks can be broadly characterized based on their time dynamics as either static or dynamic, and their accompanying data as labelled or unlabelled. Static anomalies exist at a single time point and can be detected through structural analysis of the network at that point. Dynamic anomalies are identified by observing changes over time. Labelled anomalies leverage additional edge or vertex label information, adding context to anomaly detection, whereas unlabelled anomalies depend solely on network structure.

Techniques for Anomaly Detection

The paper delineates how identifying anomalies generally involves two subprocesses: selecting and calculating network features, and anomaly classification within this feature space. Various techniques are surveyed, including traditional statistical methods, machine learning models, and signal processing frameworks.

Static Unlabelled Techniques:

Examples of these methods include ego-net analysis, where aberrations in expected local cluster formations can signal anomalies. The deployment of signal processing methods, such as spectral analysis, allows for the detection of subgraphs that deviate from an expected network model by treating them as signals within a noisy backdrop.

Static Labelled Techniques:

Incorporating labels allows for richer anomaly contexts. For instance, belief propagation techniques, illustrated by methods like FraudEagle, exploit link and node features to update and infer hidden states iteratively, such as identifying fake user reviews on e-commerce platforms.

Dynamic Unlabelled Techniques:

Traditional time-series analysis techniques are adapted to network metrics to detect deviations from normal evolution patterns. Methods like scan statistics, Bayesian inference, and ARMA models find application in tracking node degree changes or ego-net size fluctuations over time.

Dynamic Labelled Techniques:

This area remains relatively under-explored compared to other categories. Some methods, such as those utilizing linear combinations of node attributes over time, have begun to explore this space, indicating potential for further development.

Implications and Future Directions

While considerable progress has been made, the paper identifies several challenges and avenues for future research. First, the need for more robust frameworks that can seamlessly integrate a broader spectrum of network features is evident. This integration demands heuristics for reliably mapping behaviors to graph properties, enabling scalable real-world applications. Second, the evaluation of anomaly detection methods requires richer datasets with known ground truths, suggesting the potential role of synthetic data generated through advanced modeling techniques. Agent-based models, for instance, offer a promising avenue for simulating realistic networking scenarios through the emergent properties of individual agent behaviors.

In summary, anomaly detection in online social networks encompasses significant methodological diversity and complexity. Continued advancements in this field are essential to responding to the growing challenges posed by the scale and intricacy of modern digital social systems. The paper emphasizes the importance of both conceptual frameworks and practical implementations to enhance the robustness and efficacy of anomaly detection strategies in dynamic, often adversarial, online environments.