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DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks
A curated list of network embedding techniques.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)
StellarGraph - Machine Learning on Graphs
The main features of stellargraph/stellargraph are: General Machine Learning, Graph Machine Learning, Graph Representation Learning, Frameworks and Libraries.
Projects with overlapping indexed features include: dmlc/dgl — DGL is a Python library for building and training graph neural networks. It functions as a graph message passing… tflearn/tflearn — tflearn is a deep learning framework and high-level API wrapper for TensorFlow. It provides a toolkit for designing… benedekrozemberczki/karateclub — Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020). benedekrozemberczki/littleballoffur — Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020). chihming/awesome-network-embedding — A curated list of network embedding techniques. dsgiitr/graph_nets — Graph Nets is a graph neural network library and educational toolkit implemented in PyTorch, providing implementations…