Boosting graph embedding on a single GPU

Alabsi Aljundi, Amro and Akyıldız, Taha Atahan and Kaya, Kamer (2022) Boosting graph embedding on a single GPU. IEEE Transactions on Parallel and Distributed Systems, 33 (11). pp. 3092-3105. ISSN 1045-9219 (Print) 1558-2183 (Online)

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Graphs are ubiquitous, and they can model unique characteristics and complex relations of real-life systems. Although using machine learning (ML) on graphs is promising, their raw representation is not suitable for ML algorithms. Graph embedding represents each node of a graph as a $d$d-dimensional vector which is more suitable for ML tasks. However, the embedding process is expensive, and CPU-based tools do not scale to real-world graphs. In this work, we present GOSH, a GPU-based tool for embedding large-scale graphs with minimum hardware constraints. GOSH employs a novel graph coarsening algorithm to enhance the impact of updates and minimize the work for embedding. It also incorporates a decomposition schema that enables any arbitrarily large graph to be embedded with a single GPU. As a result, GOSH sets a new state-of-the-art in link prediction both in accuracy and speed, and delivers high-quality embeddings for node classification at a fraction of the time compared to the state-of-the-art. For instance, it can embed a graph with over 65 million vertices and 1.8 billion edges in less than 30 minutes on a single GPU.
Item Type: Article
Uncontrolled Keywords: GPU; graph coarsening; link prediction; machine learning; node classification; Parallel graph embedding
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Kamer Kaya
Date Deposited: 27 Aug 2022 11:53
Last Modified: 27 Aug 2022 11:53

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