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Found 4 papers, 3 papers with code
Towards a Taxonomy of Graph Learning Datasets
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data.
Accurately Modeling Biased Random Walks on Weighted Graphs Using $\textit{Node2vec+}$
$\textit{Node2vec}$ is a widely used method for node embedding that works by exploring the local neighborhoods via biased random walks on the graph.
PecanPy: A parallelized, efficient, and accelerated node2vec in Python
Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks.
Supervised learning is an accurate method for network-based gene classification
Results: In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes.
