  Understanding the behavior of Artificial Neural Networks is one of the main
topics in the field recently, as black-box approaches have become usual since
the widespread of deep learning. Such high-dimensional models may manifest
instabilities and weird properties that resemble complex systems. Therefore, we
propose Complex Network (CN) techniques to analyze the structure and
performance of fully connected neural networks. For that, we build a dataset
with 4 thousand models and their respective CN properties. They are employed in
a supervised classification setup considering four vision benchmarks. Each
neural network is approached as a weighted and undirected graph of neurons and
synapses, and centrality measures are computed after training. Results show
that these measures are highly related to the network classification
performance. We also propose the concept of Bag-Of-Neurons (BoN), a CN-based
approach for finding topological signatures linking similar neurons. Results
suggest that six neuronal types emerge in such networks, independently of the
target domain, and are distributed differently according to classification
accuracy. We also tackle specific CN properties related to performance, such as
higher subgraph centrality on lower-performing models. Our findings suggest
that CN properties play a critical role in the performance of fully connected
neural networks, with topological patterns emerging independently on a wide
range of models.
