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In this work, we propose SCARA, a scalable GNN with feature-oriented optimization for graph computation. SCARA efficiently computes graph embedding from node features, and further selects and reuses feature computation results to reduce overhead. Theoretical analysis indicates that our model achieves sub-linear time complexity with a guaranteed precision in propagation process as well as GNN training and inference. We conduct extensive experiments on various datasets to evaluate the efficacy and efficiency of SCARA. Performance comparison with baselines shows that SCARA can reach up to 100x graph propagation acceleration than current state-of-the-art methods with fast convergence and comparable accuracy. Most notably, it is efficient to process precomputation on the largest available billion-scale GNN dataset Papers100M (111M nodes, 1.6B edges) in 100 seconds.<\/jats:p>","DOI":"10.14778\/3551793.3551866","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:25:03Z","timestamp":1664490303000},"page":"3240-3248","source":"Crossref","is-referenced-by-count":4,"title":["SCARA"],"prefix":"10.14778","volume":"15","author":[{"given":"Ningyi","family":"Liao","sequence":"first","affiliation":[{"name":"Nanyang Technological University"}]},{"given":"Dingheng","family":"Mo","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}]},{"given":"Siqiang","family":"Luo","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Pengcheng","family":"Yin","sequence":"additional","affiliation":[{"name":"Google Research"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_2_1","volume-title":"DDGK: Learning Graph Representations for Deep Divergence Graph Kernels. 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