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arXiv · 2509.23409

Mind the Links: Cross-Layer Attention for Link Prediction in Multiplex Networks

Devesh Sharma·Aditya Kishore·Ayush Garg·Debajyoti Mazumder·Debasis Mohapatra·Jasabanta Patro
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Abstract

Multiplex graphs capture diverse relations among shared nodes. Most predictors either collapse layers or treat them independently. This loses crucial inter-layer dependencies and struggles with scalability. To overcome this, we frame multiplex link prediction as multi-view edge classification. For each node pair, we construct a sequence of per-layer edge views and apply cross-layer self-attention to fuse evidence for the target layer. We present two models as instances of this framework: Trans-SLE, a lightweight transformer over static embeddings, and Trans-GAT, which combines layer-specific GAT encoders with transformer fusion. To ensure scalability and fairness, we introduce a Union--Set candidate pool and two leakage-free protocols: cross-layer and inductive subgraph generalization. Experiments on six public multiplex datasets show consistent macro-F_1 gains over strong baselines (MELL, HOPLP-MUL, RMNE). Our approach is simple, scalable, and compatible with both precomputed embeddings and GNN encoders.

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Devesh Sharma, Aditya Kishore, Ayush Garg, Debajyoti Mazumder, Debasis Mohapatra, Jasabanta Patro. 2025-09-27. Mind the Links: Cross-Layer Attention for Link Prediction in Multiplex Networks. https://arxiv.org/abs/2509.23409

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