Author

Xinda Zheng

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Conference 2026

Constraint-Aware Self-Supervised Learning for Edge Selection

A reusable self-supervised framework for edge-selection optimization that learns directly from unlabeled instances is proposed, and a lightweight graph architecture centered on a cost-attention convolution is introduced, where edge costs and feasibility information directly shape message passing.

Xinda Zheng, Frits de Nijs, Edward Lam · International Conference on... · 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