257 citations · 514 across the 14 of their papers we have counts for
3 papers · 1 filter
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…
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao +3
We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i.e., in networks where connect…
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…