We just released a general and high-performance graph embedding system, GraphVite.

Compared to existing machine learning systems that are mainly designed for data with regular structures (e.g., images, speech, and natural language), GraphVite is specifically designed for large-scale graphs. It runs on the CPU-GPU hybrid architectures and scales linearly to the number of GPUs. The system is one or two magnitudes faster than existing implementations. For example, for a graph with one million nodes, it only takes around one minute to learn the node representations with 4 GPUs. Besides the superior efficiency, GraphVite also supports a variety of applications and models, including

• Node Embedding: DeepWalk, LINE, node2vec
• Knowledge Graph Embedding: TransE, DistMult, ComplEx, SimplE, RotatE
• Graph and High-dimensional Data Visualization: LargeVis

There are already more than 30 configurations and benchmarks on standard datasets. We are actively developing new applications and models. The system is expected to support the community of graph embedding or in general, deep learning for graphs.

• Paper: https://arxiv.org/abs/1903.00757
• Website: https://graphvite.io/
• GitHub: https://github.com/DeepGraphLearning/graphvite