Graph Structure of Neural Networks

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)).

Parameter Definition

(1) Clustering Coefficient

(2) Average Path Length