257 citations · 1.2k across the 74 of their papers we have counts for
19 papers · 1 filter
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks
Donald Loveland, Jiong Zhu, Mark Heimann +3
Graph Neural Network (GNN) research has highlighted a relationship between high homophily (i.e., the tendency of nodes of the same class to connect) and strong predictive performan…
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment
Jing Zhu, Danai Koutra, Mark Heimann
Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains. We propose CAPER, a multilevel…
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods
Donald Loveland, Jiong Zhu, Mark Heimann +3
We study the task of node classification for graph neural networks (GNNs) and establish a connection between group fairness, as measured by statistical parity and equal opportunity…
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding
Jing Zhu, Xingyu Lu, Mark Heimann +1
While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node ro…
Refining Network Alignment to Improve Matched Neighborhood Consistency
Mark Heimann, Xiyuan Chen, Fatemeh Vahedian +1
Network alignment, or the task of finding meaningful node correspondences between nodes in different graphs, is an important graph mining task with many scientific and industrial a…
G-CREWE: Graph CompREssion With Embedding for Network Alignment
Kyle K. Qin, Flora D. Salim, Yongli Ren +3
Network alignment is useful for multiple applications that require increasingly large graphs to be processed. Existing research approaches this as an optimization problem or comput…