Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs (2005.07427v2)

Abstract: Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media. Previous network embedding based methods have been mostly focusing on learning good node representations, whereas largely ignoring the subgraph structural changes related to the target nodes in dynamic graphs. In this paper, we propose StrGNN, an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs. In particular, we first extract the $h$-hop enclosing subgraph centered on the target edge and propose the node labeling function to identify the role of each node in the subgraph. Then, we leverage graph convolution operation and Sortpooling layer to extract the fixed-size feature from each snapshot/timestamp. Based on the extracted features, we utilize Gated recurrent units (GRUs) to capture the temporal information for anomaly detection. Extensive experiments on six benchmark datasets and a real enterprise security system demonstrate the effectiveness of StrGNN.

• The paper introduces StrGNN, a novel framework for anomaly detection in dynamic graphs by focusing on structural temporal patterns within localized subgraphs around edges, rather than solely relying on node embeddings.
• StrGNN employs a three-stage methodology including enclosing subgraph generation, graph structural feature extraction using convolutions and pooling, and a temporal detection network with GRUs to model dependencies.
• Experimental results show StrGNN outperforms existing methods on six benchmark datasets, achieving higher AUC and reducing false positives by up to 50% in a real-world security system deployment.

Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs: An Analysis

The paper entitled "Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs" introduces StrGNN, a novel framework for the identification of anomalous edges in dynamic graph environments. This research specifically targets dynamic systems where nodes and edges are subject to continuous change, such as networks observed in cybersecurity, finance, and social media contexts. Dynamic graphs differ significantly from static graphs in that they require methods capable of handling both temporal dependencies and structural transitions over time.

In traditionally crafted methods for network embedding on dynamic graphs, emphasis has been on acquiring effective node representation alone. However, this approach can be insufficient, as it often overlooks critical structural temporal dynamics near target nodes or edges. StrGNN overcomes these limitations by integrating structural analysis directly targeted at subgraphs defined around candidate edges, rather than relying exclusively on static embeddings.

Core Contributions and Methodology

The StrGNN framework brings forward several innovative aspects:

1. Enclosing Subgraph Generation (ESG): StrGNN first isolates the $h$-hop enclosing subgraph around the target edge. This process efficiently captures local dynamic structures, reducing unnecessary complexity associated with evaluating entire graph snapshots.
2. Graph Structural Feature Extraction (GSFE): Using graph convolution layers and Sortpooling, GSFE extracts fixed-size representations of the otherwise variably sized subgraphs at each time snapshot. This ensures uniformity in input size for subsequent temporal modeling stages.
3. Temporal Detection Network (TDN): To capture the temporal dependencies crucial for anomaly detection, TDN employs Gated Recurrent Units (GRUs), which are well-suited for modeling sequences and therefore adept at learning patterns from historical graph data.

The paper provides rigorous validation of StrGNN's efficacy on six benchmark datasets, showcasing superior performance over competing methods like Node2Vec, DeepWalk, and NetWalk. Experimental results highlight StrGNN's ability to achieve higher AUC values, indicating enhanced accuracy in anomaly detection tasks. Notably, StrGNN is shown to reduce false positives by up to 50% in a real-world enterprise security system deployment, further proving its practical applicability.

Implications and Future Directions

The findings reported in this paper have substantial implications for the fields of anomaly detection and dynamic graph analysis. StrGNN demonstrates that embracing structural temporal patterns specific to dynamic graphs, as opposed to merely learning node embeddings, can dramatically improve anomaly detection accuracy. This aspect is particularly critical in scenarios involving cybersecurity or fraud detection, where anomalies may indicate significant threats.

From a future work perspective, enhancements in computational efficiency could expand StrGNN’s applicability to even larger-scale networks. Additionally, extending the framework to incorporate other RNN variants or integration with attention mechanisms could further bolster detection precision and capture latent temporal dependencies. Collaborative strategies that combine domain-specific knowledge with graph neural network advancements promise to enhance the robustness and interpretability of such systems in the face of complex dynamic behaviors.