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DGCNN: A convolutional neural network over large-scale labeled graphs.

Anh Viet Phan1, Minh Le Nguyen2, Yen Lam Hoang Nguyen2

  • 1Japan Advanced Institute of Science and Technology (JAIST), Nomi city, 923-1211, Japan; Research Group in Computational Intelligence, Le Quy Don Technical University, 236 Hoang Quoc Viet St., Ha Noi, Viet Nam.

Neural Networks : the Official Journal of the International Neural Network Society
|November 22, 2018
PubMed
Summary
This summary is machine-generated.

We developed a novel deep learning method, the directed graph convolutional neural network (DGCNN), to efficiently analyze large, complex graphs. DGCNN excels at tasks like malware analysis and defect prediction, outperforming existing deep learning approaches.

Keywords:
Control flow graphs (CFGs)Convolutional neural networks (CNNs)Labeled directed graphsabstract syntax trees (ASTs)

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Graph-structured data is prevalent in natural language processing, programming analysis, and malware detection.
  • Existing methods struggle with the scale and dynamic nature of large graphs.
  • Learning representations from large-scale, irregularly shaped graphs remains a significant challenge.

Purpose of the Study:

  • To introduce a novel deep learning framework, the directed graph convolutional neural network (DGCNN), capable of processing large-scale, dynamic graphs.
  • To design convolutional filters that adapt to the varying local structures within graphs.
  • To enable efficient analysis of labeled directed graphs without vertex alignment.

Main Methods:

  • Proposed a multi-view, multi-layer convolutional neural network specifically for labeled directed graphs (DGCNN).
  • Developed flexible convolutional filters that dynamically adapt to local graph structures.
  • Demonstrated the ability to process large-scale dynamic graphs with hundreds of thousands of nodes.

Main Results:

  • DGCNN effectively handles large-scale dynamic graphs, overcoming limitations of existing methods.
  • Achieved superior performance in malware analysis tasks compared to baseline methods.
  • Outperformed several deep neural network approaches in software defect prediction.

Conclusions:

  • DGCNN offers a powerful and scalable solution for learning from graph-structured data.
  • The method's adaptability to dynamic graph structures makes it suitable for complex real-world applications.
  • DGCNN represents a significant advancement in graph representation learning for tasks like security and software engineering.