Difference between revisions of "Graph Structure of Neural Networks"

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(Major Conclusions (Section 5 in the paper))
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= Experimental Setup (Section 4 in the paper) =
= Experimental Setup (Section 4 in the paper) =
= Major Conclusions (Section 5 in the paper) =
= Discussions and Conclusions =

Revision as of 20:05, 10 November 2020

Presented By

Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang


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

Experimental Setup (Section 4 in the paper)

Discussions and Conclusions

1. Neural networks performance depends on its structure

2. Sweet spot where performance is significantly improved

3. Relationship between neural network’s performance and parameters

4. Consistency among many different tasks and datasets

5. top architectures can be identified efficiently

6. well-performing neural networks have graph structure surprisingly similar to those of real biological neural networks