We identify four major challenges in graph deep learning: dynamic and evolving graphs, learning with edge signals and information, graph estimation, and the generalization of graph models. For each ...

In this paper, based on the factorization framework, we propose a novel robust matrix completion scheme via using the truncated-quadratic loss function, which is non-convex and non-smooth, and ...

GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection This is the official implementation of the following paper: GADBench: Revisiting and Benchmarking Supervised Graph Anomaly ...

BY JOHN PAWLAKWhite RockAt the grocery store, the total for my purchases came to an even $60. The cashier looked at the number and said, “Wow, right on an even ten dollar amount. What are the ...