mlsanityData to 7 Oct 2026

A Large-Scale Database for Graph Representation Learning

arXiv 2011.07682 ↗First posted 16 Nov 2020cs.LG cs.AI cs.CRcited by 54

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Abstract (arXiv)

With the rapid emergence of graph representation learning, the construction of new large-scale datasets is necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique. By carefully analyzing existing graph databases, we identify 3 critical components important for advancing the field of graph representation learning: (1) large graphs, (2) many graphs, and (3) class diversity. To date, no single graph database offers all these desired properties. We introduce MalNet, the largest public graph database ever constructed, representing a large-scale ontology of malicious software function call graphs. MalNet contains over 1.2 million graphs, averaging over 15k nodes and 35k edges per graph, across a hierarchy of 47 types and 696 families. Compared to the popular REDDIT-12K database, MalNet offers 105x more graphs, 39x larger graphs on average, and 63x more classes. We provide a detailed analysis of MalNet, discussing its properties and provenance, along with the evaluation of state-of-the-art machine learning and graph neural network techniques. The unprecedented scale and diversity of MalNet offers exciting opportunities to advance the frontiers of graph representation learning--enabling new discoveries and research into imbalanced classification, explainability and the impact of class hardness. The database is publicly available at www.mal-net.org.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Nov 2020.

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Citing papers per year: 2021: 2; 2022: 11; 2023: 7; 2024: 13; 2025: 15; 2026: 6.

54 papers.

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  1. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 702
  2. 2022Long Range Graph Benchmarkcited by 258
  3. 2023Exphormer: Sparse Transformers for Graphscited by 166
  4. 2024Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spacescited by 132
  5. 2022A Generalization of ViT/MLP-Mixer to Graphscited by 93
  6. 2022Evaluating Explainability for Graph Neural Networkscited by 85
  7. 2022BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphscited by 80
  8. 2022AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutionscited by 77
  9. 2025Position: Graph Learning Will Lose Relevance Due To Poor Benchmarkscited by 52
  10. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  11. 2024Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learningcited by 28
  12. 2025Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellencecited by 24
38 more
  1. 2022Transformers over Directed Acyclic Graphscited by 23
  2. 2024When Graph meets Multimodal: Benchmarking and Meditating on Multimodal Attributed Graphs Learningcited by 20
  3. 2024PowerGraph: A power grid benchmark dataset for graph neural networkscited by 19
  4. 2023TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphscited by 16
  5. 2024Understanding Virtual Nodes: Oversquashing and Node Heterogeneitycited by 14
  6. 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysiscited by 13
  7. 2024What Can We Learn from State Space Models for Machine Learning on Graphs?cited by 13
  8. 2024Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedycited by 13
  9. 2021MalNet: A Large-Scale Image Database of Malicious Softwarecited by 11
  10. 2022Taxonomy of Benchmarks in Graph Representation Learningcited by 11
  11. 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
  12. 2022Graph Neural Network-based Android Malware Classification with Jumping Knowledgecited by 9
  13. 2023Learning Large Graph Property Prediction via Graph Segment Trainingcited by 9
  14. 2024View-based Explanations for Graph Neural Networkscited by 8
  15. 2024Use of Graph Neural Networks in Aiding Defensive Cyber Operationscited by 7
  16. 2024AI-Driven Guided Response for Security Operation Centers with Microsoft Copilot for Securitycited by 7
  17. 2023State of the Art and Potentialities of Graph-level Learningcited by 6
  18. 2025Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centralitycited by 5
  19. 2021Computing Graph Descriptors on Edge Streamscited by 4
  20. 2024A Scalable and Effective Alternative to Graph Transformerscited by 4
  21. 2024TorchGT: A Holistic System for Large-scale Graph Transformer Trainingcited by 4
  22. 2023A Comparison of Graph Neural Networks for Malware Classificationcited by 3
  23. 2026Graph Tokenization for Bridging Graphs and Transformerscited by 3
  24. 2025Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter Trendscited by 1
  25. 2025HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysiscited by 1
  26. 2025Signal-Based Malware Classification Using 1D CNNscited by 1
  27. 2025Rotary Position Encodings for Graphscited by 1
  28. 2026Benchmarking Sheaf Neural Networks for Inductive Taskscited by 1
  29. 2022Graph-level Neural Networks: Current Progress and Future Directionscited by 0
  30. 2024Web Scale Graph Mining for Cyber Threat Intelligencecited by 0
  31. 2025GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecturecited by 0
  32. 2025HSG-12M: A Large-Scale Benchmark of Spatial Multigraphs from the Energy Spectra of Non-Hermitian Crystalscited by 0
  33. 2025TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flowscited by 0
  34. 2025Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmarkcited by 0
  35. 2025State Space Models over Directed Graphscited by 0
  36. 2025Learning Joint Embeddings of Function and Process Call Graphs for Malware Detectioncited by 0
  37. 2025Message Passing on the Edge: Towards Scalable and Expressive GNNscited by 0
  38. 2025Better Call Graphs: A New Dataset of Function Call Graphs for Malware Classificationcited by 0

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