Force2Vec: Parallel force-directed graph embedding
This provides a faster and more versatile graph embedding method for researchers and practitioners working with large-scale graphs in both machine learning and visualization contexts.
The authors tackled graph embedding by connecting it to force-directed graph layout models, creating Force2Vec which is 43x faster than DeepWalk on average and can process graphs with billions of edges in hours while performing comparably or better in ML tasks and better in visualization.
A graph embedding algorithm embeds a graph into a low-dimensional space such that the embedding preserves the inherent properties of the graph. While graph embedding is fundamentally related to graph visualization, prior work did not exploit this connection explicitly. We develop Force2Vec that uses force-directed graph layout models in a graph embedding setting with an aim to excel in both machine learning (ML) and visualization tasks. We make Force2Vec highly parallel by mapping its core computations to linear algebra and utilizing multiple levels of parallelism available in modern processors. The resultant algorithm is an order of magnitude faster than existing methods (43x faster than DeepWalk, on average) and can generate embeddings from graphs with billions of edges in a few hours. In comparison to existing methods, Force2Vec is better in graph visualization and performs comparably or better in ML tasks such as link prediction, node classification, and clustering. Source code is available at https://github.com/HipGraph/Force2Vec.