Author

Yong-Chun Jiang

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#graph neural networks Book Open access Sep 2026

ATLAS: Enabling an Asynchronous State-Aware Pipeline for Efficient Training of Temporal Graph Neural Networks

Temporal Graph Neural Networks (T-GNNs) are widely used to model continuous-time dynamic graphs. However, training T-GNNs on large temporal graphs remains computationally prohibitive due to a fundamental challenge: existing systems enforce a stop-line execution paradigm that serializes batch data preparation with GPU c...

Yong-Chun Jiang, Yong-Chao Liu, Kai-Fan Jia et al. · Proceedings of the Internati... · 0 citations

Conference Open access Sep 2026

History Doesn’t Repeat, but Its Patterns Echo: A Parallel Pairwise Negative-Sampling Framework for Temporal Link Prediction

This paper proposes ATNSF, a temporal graph learning framework with a hybrid negative sampling strategy that uses a portion of historical edges as hard negatives and introduces a pairwise sampled softmax loss that contrasts each positive instance with a batch of negatives to learn more discriminative representations.

Yong-Chun Jiang, Heng Zhang, Jian Gao et al. · Proceedings of the Thirty-Fi... · 0 citations