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Graph embedding maps graph nodes to low-dimensional vectors, and is widelyadopted in machine learning tasks. The increasing availability of billion-edgegraphs underscores the importance of learning efficient and effectiveembeddings on large graphs, such as link prediction on Twitter with over onebillion edges. Most existing graph embedding methods fall short of reachinghigh data scalability. In this paper, we present a general-purpose,distributed, information-centric random walk-based graph embedding framework,DistGER, which can scale to embed billion-edge graphs. DistGER incrementallycomputes information-centric random walks. It further leverages amulti-proximity-aware, streaming, parallel graph partitioning strategy,simultaneously achieving high local partition quality and excellent workloadbalancing across machines. DistGER also improves the distributed Skip-Gramlearning model to generate node embeddings by optimizing the access locality,CPU throughput, and synchronization efficiency. Experiments on real-worldgraphs demonstrate that compared to state-of-the-art distributed graphembedding frameworks, including KnightKing, DistDGL, and Pytorch-BigGraph,DistGER exhibits 2.33x-129x acceleration, 45% reduction in cross-machinescommunication, and > 10% effectiveness improvement in downstream tasks.

Distributed Graph Embedding with Information-Oriented Random Walks