1 citations · 1 across the 5 of their papers we have counts for
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TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
Jacob Chmura, Shenyang Huang, Tran Gia Bao Ngo +7
Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like PyTorch Geometric and DGL, ML for temporal…
T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs
Alireza Dizaji, Benedict Aaron Tjandra, Mehrab Hamidi +2
Dynamic graph learning methods have recently emerged as powerful tools for modelling relational data evolving through time. However, despite extensive benchmarking efforts, it rema…
UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs
Shenyang Huang, Farimah Poursafaei, Reihaneh Rabbany +2
Many real world graphs are inherently dynamic, constantly evolving with node and edge additions. These graphs can be represented by temporal graphs, either through a stream of edge…
MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs
Kiarash Shamsi, Tran Gia Bao Ngo, Razieh Shirzadkhani +7
Temporal Graph Learning (TGL) has become a robust framework for discovering patterns in dynamic networks and predicting future interactions. While existing research has largely con…
Temporal Graph Rewiring with Expander Graphs
Katarina Petrović, Shenyang Huang, Farimah Poursafaei +1
Evolving relations in real-world networks are often modelled by temporal graphs. Temporal Graph Neural Networks (TGNNs) emerged to model evolutionary behaviour of such graphs by le…