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Our system addresses data management, algorithmic, and systems challenges, enabling continuous capturing of the dynamic state of the graph and updating node representations with fault-tolerance and optimal latency, load-balance, and throughput. D3-GNN utilizes streaming GNN aggregators and an unrolled, distributed computation graph architecture to handle cascading graph updates. To counteract data skew and neighborhood explosion issues, we introduce inter-layer and intra-layer windowed forward pass solutions. Experiments on large-scale graph streams demonstrate that D3-GNN achieves high efficiency and scalability. Compared to DGL, D3-GNN achieves a significant throughput improvement of about 76x for streaming workloads. The windowed enhancement further reduces running times by around 10x and message volumes by up to 15x at higher parallelism.<\/jats:p>","DOI":"10.14778\/3681954.3681961","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T16:23:36Z","timestamp":1725035016000},"page":"2764-2777","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks"],"prefix":"10.14778","volume":"17","author":[{"given":"Rustam","family":"Guliyev","sequence":"first","affiliation":[{"name":"University of Warwick, Coventry, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aparajita","family":"Haldar","sequence":"additional","affiliation":[{"name":"University of Warwick, Coventry, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hakan","family":"Ferhatosmanoglu","sequence":"additional","affiliation":[{"name":"University of Warwick, Coventry, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/3236187.3236208"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3205215"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3383131"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/214451.214456"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2463697"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447818.3460372"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3210259.3210262"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCT52962.2021.9658097"},{"key":"e_1_2_1_10_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. 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