Graph Structure of Neural Networks

Presented By

Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang

Major Conclusions

(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.