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Vertical Memristive Crossbar Array for Multilayer Graph Embedding and Analysis.

Janguk Han1, Yoon Ho Jang1, Ji Won Moon1

  • 1Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a novel 3D vertical metal-at-diagonal crossbar array (vm-CBA) for multilayer graph embedding. This approach enhances accuracy and reduces computational load compared to traditional methods.

Keywords:
graph data structuregraph embeddinglink predictionmultilayer graphself‐rectifying memritstor

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Area of Science:

  • Computer Science
  • Materials Science
  • Network Science

Background:

  • Graph data structures model complex relationships, with metal-at-diagonal crossbar arrays (m-CBA) previously used for planar graphs.
  • Conventional methods for multilayer graph embedding in high-dimensional spaces face mathematical complexity, computational burden, and information loss.

Purpose of the Study:

  • To propose a novel graph embedding method for multilayer graphs using a fabricated vertical m-CBA (vm-CBA).
  • To validate the functionality of the vm-CBA with a custom-built measurement system.
  • To demonstrate the enhanced performance of vm-CBA for multilayer graph representation.

Main Methods:

  • Fabrication of a 3D vm-CBA structure capable of directly mapping multilayer graphs.
  • Development and utilization of a custom measurement system for vm-CBA validation.
  • Evaluation of the vm-CBA method using link prediction and information scores on real-world datasets.

Main Results:

  • The vm-CBA successfully and directly mapped multilayer graphs, representing both inter-layer and intra-layer connections.
  • Practical link prediction and information scores showed enhanced accuracy for vm-CBA compared to conventional embeddings.
  • The vm-CBA method achieved these results with a significantly decreased number of operations.

Conclusions:

  • The proposed vm-CBA offers a more efficient and accurate method for embedding multilayer graphs.
  • This 3D vm-CBA approach overcomes the limitations of conventional high-dimensional embedding techniques.
  • The findings suggest vm-CBA as a promising hardware-based solution for complex network analysis.