Web Reference: Aug 6, 2019 · In this paper we study the problem of dynamically maintaining graph properties under batches of edge insertions and deletions in the massively parallel model of computation. Nov 12, 2025 · Memory-based temporal graph neural networks (MTGNN) use node memory to store historical information, enabling efficient processing of large dynamic graphs through batch parallel training, with larger batch sizes leading to increased training efficiency. Jul 19, 2024 · To address this issue, in this paper, we propose a flexible Dynamic Batch-Graph Representation (DyBGR) model, to automatically explore the intrinsic relationship of samples for contextual sample representation.
YouTube Excerpt: Laxman Dhulipala (University of Maryland) https://simons.berkeley.edu/talks/laxman-dhulipala-university-maryland-2023-09-19 ...
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