Graph Structure of Neural Networks: Difference between revisions
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== 1. Neural Networks Performance Depends on its Structure == | == 1. Neural Networks Performance Depends on its Structure == | ||
== 2. | == 2. Sweet spot where performance is significantly improved == | ||
== 3. neural network’s performance is approximately a smooth function of the clustering coefficient and average path length of its relational graph == | == 3. neural network’s performance is approximately a smooth function of the clustering coefficient and average path length of its relational graph == | ||
Revision as of 18:22, 10 November 2020
Presented By
Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang
Introduction
We develop a new way of representing a neural network as a graph, which we call relational graph. Our key insight is to focus on message exchange, rather than just on directed data flow. As a simple example, for a fixedwidth fully-connected layer, we can represent one input channel and one output channel together as a single node, and an edge in the relational graph represents the message exchange between the two nodes (Figure 1(a)).
Relational Graph
Parameter Definition
(1) Clustering Coefficient
(2) Average Path Length