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arXiv Machine Learning By Nguyen Hong Quan, Sitao Luan, Yilun Zheng, Lihui Chen

MambaTG: Candidate-Aware State Space Modeling for Future Link Prediction on Temporal Graphs

Read the original on arXiv Machine Learning →

MambaTG is a new framework for future link prediction on temporal graphs that addresses both repeated and unseen edge prediction. It leverages two complementary signals: the historical-sequence evolution of a node’s interactions and the compatibility between a candidate edge and the node’s evolving history. By using a candidate-aware Mamba architecture, the model captures ordered interaction dynamics and adapts state transitions to different candidates, achieving strong performance across 13 datasets while remaining computationally efficient.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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