An Explainer for Temporal Graph Neural Networks

He, Wenchong; Vu, Minh N.; Jiang, Zhe; Thai, My T.

doi:10.1109/globecom48099.2022.10001619

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…And the method presented in this paper cannot predict unseen malware, which is also a common drawback on machine learning approach. [23] Additionally, the dynamic nature of malware evolution requires continuous updates to datasets and models to maintain effectiveness.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Li,

Zhu,

Liu

et al. 2024

IJCSIT

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This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-2019 dataset. The aim is to advance cybersecurity capabilities by identifying and mitigating threats more effectively. Both ensemble and non-ensemble machine learning methods, such as Random Forest, XGBoost, K Nearest Neighbor (KNN), and Neural Networks, are explored. Special emphasis is placed on the importance of data pre-processing techniques, particularly TF-IDF representation and Principal Component Analysis, in improving model performance. Results indicate that ensemble methods, particularly Random Forest and XGBoost, exhibit superior accuracy, precision, and recall compared to others, highlighting their effectiveness in malware detection. The paper also discusses limitations and potential future directions, emphasizing the need for continuous adaptation to address the evolving nature of malware. This research contributes to ongoing discussions in cybersecurity and provides practical insights for developing more robust malware detection systems in the digital era.

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Section: Discussionmentioning

confidence: 99%

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Li,

Zhu,

Liu

et al. 2024

IJCSIT

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“…Kosan, Mert, et al [37] have done anomaly detection using macro-feature analysis, followed by a self-attention mechanism to detect anomalies in a dynamic graph accurately. T-GNNs [38] enabled the modeling of dynamic systems in which the system's evolution can be represented as a succession of graphs. T-GNNs have been used for several applications, including traffic prediction, social network analysis, and activity detection, where the temporal aspect is crucial.…”

Section: Subgraph Level Anomaly Detectionmentioning

confidence: 99%

Statistically Inspired Discrepancy Detection for Anomalous Spatio-Temporal Graphs

Tiwari,

Chatterjee,

Padmakumar

et al. 2024

Preprint

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Anomaly detection in dynamic graphs is a critical topic with applications in many fields, such as fraud detection and network security. This paper tackles the difficulties in locating abnormalities in time-varying graphs by presenting a novel divide-and-conquer method. We combine Graph Convolutional Networks (GCN) and Recurrent Neural Networks (RNN) to predict future node values on temporal graphs, followed by a macro and micro-level analysis. At the macro level, we present a novel algorithm to extract correlation-based subgraphs. The values obtained for each subgraph allow us to concentrate on subgraphs that show significant anomalies, effectively minimizing the challenges in node-level anomaly detection. This is followed by a micro-level analysis of the node contributions and temporal properties within the identified anomalous subgraphs. By combining macro and micro-level analysis with a machine learning-based approach, our method provides an efficient approach for zoning in on anomalies, significantly speeding up computation.

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“…DGExplainer [17] calculates the contributions of subgraphs based on the LRP algorithm. Wenchong et al proposed to extend PGMExplainer to dynamic graphs by applying it to each snapshot and finding out dominant Bayesian networks [18]. There works need to access the hidden states of a trained model, while our proposed StGraphLIME is a fully black-box explanation method that can be applied to dynamic graphs.…”

Section: Related Workmentioning

confidence: 99%

Structural Explanations for Graph Neural Networks using HSIC

Toyokuni¹,

Yamada²

2023

Preprint

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Graph neural networks (GNNs) are a type of neural model that tackle graphical tasks in an end-to-end manner. Recently, GNNs have been receiving increased attention in machine learning and data mining communities because of the higher performance they achieve in various tasks, including graph classification, link prediction, and recommendation. However, the complicated dynamics of GNNs make it difficult to understand which parts of the graph features contribute more strongly to the predictions. To handle the interpretability issues, recently, various GNN explanation methods have been proposed. In this study, a flexible model agnostic explanation method is proposed to detect significant structures in graphs using the Hilbert-Schmidt independence criterion (HSIC), which captures the nonlinear dependency between two variables through kernels. More specifically, we extend the GraphLIME method for node explanation with a group lasso and a fused lasso-based node explanation method. The group and fused regularization with GraphLIME enables the interpretation of GNNs in substructure units. Then, we show that the proposed approach can be used for the explanation of sequential graph classification tasks. Through experiments, it is demonstrated that our method can identify crucial structures in a target graph in various settings.

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An Explainer for Temporal Graph Neural Networks

Cited by 4 publications

References 10 publications

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Statistically Inspired Discrepancy Detection for Anomalous Spatio-Temporal Graphs

Structural Explanations for Graph Neural Networks using HSIC

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