mlsanityData to 7 Oct 2026

Graph Neural Network-based Android Malware Classification with Jumping Knowledge

arXiv 2201.07537 ↗First posted 19 Jan 2022cs.CR cs.LGcited by 9

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Abstract (arXiv)

This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set of program functions and their inter-procedural calls. Thus, this paper proposes a GNN-based method for Android malware detection by capturing meaningful intra-procedural call path patterns. In addition, a Jumping-Knowledge technique is applied to minimize the effect of the over-smoothing problem, which is common in GNNs. The proposed method has been extensively evaluated using two benchmark datasets. The results demonstrate the superiority of our approach compared to state-of-the-art approaches in terms of key classification metrics, which demonstrates the potential of GNNs in Android malware detection and classification.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 19 of its 48 references that are in the map. First posted on arXiv 19 Jan 2022.

Builds on

19 of 48 references.

References · 19

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  11. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
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7 more
  1. 2020DeeperGCN: All You Need to Train Deeper GCNscited by 249
  2. 2021E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTcited by 55
  3. 2020A Large-Scale Database for Graph Representation Learningcited by 54
  4. 2019Edge Contraction Pooling for Graph Neural Networkscited by 53
  5. 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
  6. 2021Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithmscited by 11
  7. 2016A New Android Malware Detection Approach Using Bayesian Classificationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 4; 2026: 2.

9 papers.

  1. 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
  2. 2024Use of Graph Neural Networks in Aiding Defensive Cyber Operationscited by 7
  3. 2023A Comparison of Graph Neural Networks for Malware Classificationcited by 3
  4. 2025VOLTRON: Detecting Unknown Malware Using Graph-Based Zero-Shot Learningcited by 1
  5. 2025HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysiscited by 1
  6. 2025Fractal Graph Contrastive Learningcited by 0
  7. 2025Better Call Graphs: A New Dataset of Function Call Graphs for Malware Classificationcited by 0
  8. 2026LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attributioncited by 0
  9. 2026HYDRA: Proactive Android Malware Drift Adaptation via Hierarchical Graph Contrastive Learningcited by 0