Graph Structure of Neural Networks
From statwiki
Contents
Presented By
Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang
Introduction
Relational Graph
Parameter Definition
(1) Clustering Coefficient
(2) Average Path Length
Experimental Setup (Section 4 in the paper)
Major Conclusions (Section 5 in the paper)
(1) graph structure of neural networks matters;
(2) a “sweet spot” of relational graphs lead to neural networks with significantly improved predictive performance;
(3) neural network’s performance is approximately a smooth function of the clustering coefficient and average path length of its relational graph;
(4) our findings are consistent across 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.
