50 citations · 76 across the 7 of their papers we have counts for
7 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…
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…
CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding
Xiyuan Chen, Mark Heimann, Fatemeh Vahedian +1
Network alignment, the process of finding correspondences between nodes in different graphs, has many scientific and industrial applications. Existing unsupervised network alignmen…
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching
Di Jin, Mark Heimann, Ryan Rossi +1
Identity stitching, the task of identifying and matching various online references (e.g., sessions over different devices and timespans) to the same user in real-world web services…