AI RESEARCH
Explaining Temporal Graph Predictions With Shapley Values
arXiv CS.LG
•
ArXi:2604.24078v1 Announce Type: new Temporal Graph Neural Networks (TGNNs) have become increasingly popular in recent years due to their superior predictive performance by combining both spatial and temporal information. However, how these models utilize the information to make predictions is rather unexplored, leading to potentially faulty or biased models. This work