二元图形卷积网络与容量探索
IEEE transactions on pattern analysis and machine intelligence
|December 13, 2023
概括
本研究介绍了二进制图形卷积网络 (Bi-GCNs),通过二元化参数和属性来压缩图形神经网络 (GNNs). 这大大减少了内存,并加快了对大型图形数据的推断速度.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 图形神经网络 (GNN) 需要大量的内存来处理大型的属性图形.
- 由于完全精确的处理,现有的GNN面临内存限制的局限性.
研究的目的:
- 开发一个内存高效和加速的GNN模型.
- 用有限的资源来解决处理大型归因图的挑战.
主要方法:
- 拟议的二进制图卷积网络 (Bi-GCN) 二元化网络参数和节点属性.
- 使用二进制运算,而不是浮点矩阵乘法.
- 引入了一种新的渐变近似,用于Bi-GCNs的反向传播.
- 开发了一个透覆盖假设,以解决二元化GNN的潜在容量问题.
主要成果:
- 对参数和数据的内存消耗平均减少了~31倍.
- 在引用网络 (Cora,PubMed,CiteSeer) 上,推断速度平均加速了~51倍.
- 将扩展二进制化扩展到其他GNN变体,实现类似的效率增长.
- 在七个节点分类数据集上证实了与完全精确基线相对应的性能.
- 验证了透覆盖假设用于压缩GNN中的容量管理.
结论:
- 双GCN为GNN提供了显著的压缩和加速.
- 提出的方法在内存限制下有效处理大型图形数据.
- 覆盖假设为二元化GNN的容量限制提供了一个可行的解决方案.
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