Guojing Cong, Shruti Kulkarni, Seung–Hwan Lim, Prasanna Date, Shay Snyder, Maryam Parsa, D. Kennedy and C. D. Schuman

December, 2024

International Conference on Machine Learning and Applications (ICMLA)

https://ieeexplore.ieee.org/abstract/document/10459955

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Abstract

Graph convolutional networks leverage both graph structures and features on nodes and edges for improved learning performance in comparison with classical machine learning approaches. Spiking neuromorphic computers natively implement network-like computation and have been shown to be successful at implementing graph learning without features. Incorporating graph features brings the challenge of efficient feature representation and balancing the contribution of topology and features in learning. In this work, we present our design of a simulated network of spiking neurons to perform semi-supervised learning on graph data using both the graph structure and the node features. We explore various design choices, present preliminary results, and discuss the opportunities for using neuromorphic computers for this task in the future.

Citation Information

Text

author     G. Cong and S. Kulkarni and S. H. Lim and P. Date and S. Snyder and M. Parsa and D. Kennedy and C. D. Schuman
title      Hyperparameter Optimization and Feature Inclusion in Graph Neural Networks for Spiking Implementation
booktitle  International Conference on Machine Learning and Applications (ICMLA)
address    Jacksonville, FL
month      December
year       2023
doi        10.1109/ICMLA58977.2023.00232
url        https://ieeexplore.ieee.org/abstract/document/10459955

Bibtex

@INPROCEEDINGS{ckl:23:hof,
    author = "G. Cong and S. Kulkarni and S. H. Lim and P. Date and S. Snyder and M. Parsa and D. Kennedy and C. D. Schuman",
    title = "Hyperparameter Optimization and Feature Inclusion in Graph Neural Networks for Spiking Implementation",
    booktitle = "International Conference on Machine Learning and Applications (ICMLA)",
    address = "Jacksonville, FL",
    month = "December",
    year = "2023",
    doi = "10.1109/ICMLA58977.2023.00232",
    url = "https://ieeexplore.ieee.org/abstract/document/10459955"
}