A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation

• URL: http://arxiv.org/abs/2511.20801v1
• Date: Tue, 25 Nov 2025 19:40:39 GMT
• Title: A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation
• Authors: Hossein Shokouhinejad, Griffin Higgins, Roozbeh Razavi-Far, Ali A. Ghorbani,
• Abstract summary: Graph Neural Networks (GNNs) have become an effective tool for malware detection by capturing program execution through graph-structured representations.<n>This paper brings together six related studies that collectively address these issues.<n>The portfolio begins with a survey of graph-based malware detection and explainability, then advances to new graph reduction methods, integrated reduction-learning approaches, and investigations into the consistency of explanations.
• Score: 4.437835658886064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings together six related studies that collectively address these issues. The portfolio begins with a survey of graph-based malware detection and explainability, then advances to new graph reduction methods, integrated reduction-learning approaches, and investigations into the consistency of explanations. It also introduces dual explanation techniques based on subgraph matching and develops ensemble-based models with attention-guided stacked GNNs to improve interpretability. In parallel, curated datasets of control flow graphs are released to support reproducibility and enable future research. Together, these contributions form a coherent line of research that strengthens GNN-based malware detection by enhancing efficiency, increasing transparency, and providing solid experimental foundations.

Related papers

• InteractiveGNNExplainer: A Visual Analytics Framework for Multi-Faceted Understanding and Probing of Graph Neural Network Predictions [0.0]
  Graph Neural Networks (GNNs) excel in graph-based learning tasks, but their complex, non-linear operations often render them as opaque "black boxes"<n>This paper introduces InteractiveGNNExplainer, a visual analytics framework to enhance GNN explainability.
  arXiv  Detail & Related papers  (2025-11-17T09:08:31Z)
• Toward Robust Signed Graph Learning through Joint Input-Target Denoising [20.15917072156998]
  Signed Graph Neural Networks (SGNNs) are widely adopted to analyze complex patterns in signed graphs with both positive and negative links.<n>We propose RIDGE, a novel framework for Robust graph learning through joint Denoising of Graph inputs and supervision targEts.<n>We extensively validate our method on four prevalent signed graph datasets, and the results show that RIDGE clearly improves the robustness of popular SGNN models under various levels of noise.
  arXiv  Detail & Related papers  (2025-10-26T03:34:40Z)
• Recent Advances in Malware Detection: Graph Learning and Explainability [2.5824213547618067]
  This survey focuses on the interplay between graph learning and explainability.<n>By integrating these components, this survey demonstrates how graph learning and explainability contribute to building robust, interpretable, and scalable malware detection systems.
  arXiv  Detail & Related papers  (2025-02-14T21:10:03Z)
• Explainable Malware Detection through Integrated Graph Reduction and Learning Techniques [2.464148828287322]
  Control Flow Graphs and Function Call Graphs have become pivotal in providing a detailed understanding of program execution.<n>These graph-based representations, when combined with Graph Neural Networks (GNN), have shown promise in developing high-performance malware detectors.<n>This paper addresses these issues by developing several graph reduction techniques to reduce graph size and applying the state-of-the-art GNNExplainer to enhance the interpretability of GNN outputs.
  arXiv  Detail & Related papers  (2024-12-04T18:59:45Z)
• Conditional Distribution Learning on Graphs [15.730933577970687]
  We propose a conditional distribution learning (CDL) method that learns graph representations from graph-structured data for semisupervised graph classification.<n>Specifically, we present an end-to-end graph representation learning model to align the conditional distributions of weakly and strongly augmented features over the original features.
  arXiv  Detail & Related papers  (2024-11-20T07:26:36Z)
• Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks [16.12856816023414]
  This paper explores the applications and challenges of graph neural networks (GNNs) in processing complex graph data brought about by the rapid development of the Internet. By introducing a self-supervisory mechanism, it is expected to improve the adaptability of existing models to the diversity and complexity of graph data.
  arXiv  Detail & Related papers  (2024-10-23T07:14:37Z)
• DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations [62.04558318166396]
  Graph neural networks (GNNs) demonstrate a robust capability for representation learning on graphs with complex structures. A novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced to obtain a more comprehensive embedding representation of nodes. Experimental results conducted on several graph benchmark datasets verify DGNN's superiority in node classification task.
  arXiv  Detail & Related papers  (2024-01-28T06:43:13Z)
• Learning Strong Graph Neural Networks with Weak Information [64.64996100343602]
  We develop a principled approach to the problem of graph learning with weak information (GLWI) We propose D$2$PT, a dual-channel GNN framework that performs long-range information propagation on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.
  arXiv  Detail & Related papers  (2023-05-29T04:51:09Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• An Empirical Study of Retrieval-enhanced Graph Neural Networks [48.99347386689936]
  Graph Neural Networks (GNNs) are effective tools for graph representation learning. We propose a retrieval-enhanced scheme called GRAPHRETRIEVAL, which is agnostic to the choice of graph neural network models. We conduct comprehensive experiments over 13 datasets, and we observe that GRAPHRETRIEVAL is able to reach substantial improvements over existing GNNs.
  arXiv  Detail & Related papers  (2022-06-01T09:59:09Z)
• Amortized Probabilistic Detection of Communities in Graphs [39.56798207634738]
  We propose a simple framework for amortized community detection. We combine the expressive power of GNNs with recent methods for amortized clustering. We evaluate several models from our framework on synthetic and real datasets.
  arXiv  Detail & Related papers  (2020-10-29T16:18:48Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.