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arXiv AI By Yuen Chen, Yulun Wu, Samuel Sharpe, Igor Melnyk, Nam H. Nguyen, Furong Huang, C. Bayan Bruss, Rizal Fathony

Bridging the Divide: End-to-End Sequence-Graph Learning

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The paper "Bridging the Divide: End-to-End Sequence-Graph Learning" introduces BRIDGE, an architecture that jointly learns from sequential and relational data by coupling a sequence model with a graph module under a single objective. It adds a token-level cross-attention layer, TOKENXATTN, to enable fine-grained message passing between events in neighboring sequences. Experiments on relationship prediction and fraud detection show that BRIDGE outperforms static graph models, temporal graph methods, and sequence-only baselines on ranking and classification metrics.

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 AI.

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