14 citations · 15 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 1 cited
GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs
Marios Papachristou, Rishab Goel, Frank Portman +2
In graph learning, there have been two predominant inductive biases regarding graph-inspired architectures: On the one hand, higher-order interactions and message passing work well…
cs.LG2022★ 14 cited
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Xinwei Zhang, Jianwen Jiang, Yutong Feng +6
Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new c…
cs.CV2022
Robust Graph Structure Learning via Multiple Statistical Tests
Yaohua Wang, FangYi Zhang, Ming Lin +3
Graph structure learning aims to learn connectivity in a graph from data. It is particularly important for many computer vision related tasks since no explicit graph structure is a…