MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs

• URL: http://arxiv.org/abs/2406.10426v3
• Date: Sat, 15 Feb 2025 04:10:36 GMT
• Title: MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs
• Authors: Kiarash Shamsi, Tran Gia Bao Ngo, Razieh Shirzadkhani, Shenyang Huang, Farimah Poursafaei, Poupak Azad, Reihaneh Rabbany, Baris Coskunuzer, Guillaume Rabusseau, Cuneyt Gurcan Akcora,
• Abstract summary: Temporal Graph Learning (TGL) has become a robust framework for discovering patterns in dynamic networks and predicting future interactions.<n>We introduce Temporal Multi-network Training MiNT, a novel pre-training approach that learns from multiple temporal networks.<n>MiNT achieves state-of-the-art results in zero-shot inference, surpassing models individually trained on each network.
• Score: 16.27236883013554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal Graph Learning (TGL) has become a robust framework for discovering patterns in dynamic networks and predicting future interactions. While existing research has largely concentrated on learning from individual networks, this study explores the potential of learning from multiple temporal networks and its ability to transfer to unobserved networks. To achieve this, we introduce Temporal Multi-network Training MiNT, a novel pre-training approach that learns from multiple temporal networks. With a novel collection of 84 temporal transaction networks, we pre-train TGL models on up to 64 networks and assess their transferability to 20 unseen networks. Remarkably, MiNT achieves state-of-the-art results in zero-shot inference, surpassing models individually trained on each network. Our findings further demonstrate that increasing the number of pre-training networks significantly improves transfer performance. This work lays the groundwork for developing Temporal Graph Foundation Models, highlighting the significant potential of multi-network pre-training in TGL.

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