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Found 6 papers, 4 papers with code
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators
We use these metrics to compare GraphRNN and GRAN, two well-known generative models for graphs, and unveil the influence of node orderings.
deepstruct -- linking deep learning and graph theory
deepstruct connects deep learning models and graph theory such that different graph structures can be imposed on neural networks or graph structures can be extracted from trained neural network models.
Experiments on Properties of Hidden Structures of Sparse Neural Networks
Sparsity in the structure of Neural Networks can lead to less energy consumption, less memory usage, faster computation times on convenient hardware, and automated machine learning.
DeepGG: a Deep Graph Generator
Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis, and much more.
Ranked #1 on
Graph Embedding
on Barabasi-Albert
Structural Analysis of Sparse Neural Networks
Sparse Neural Networks regained attention due to their potential for mathematical and computational advantages.
Ranked #1 on
Neural Architecture Search
on MNIST
Analysing Neural Network Topologies: a Game Theoretic Approach
In previous work, heuristics based on using the weight distribution of a neuron as contribution measure have shown some success, but do not provide a proper theoretical understanding.
Ranked #1 on
Network Pruning
on MNIST
