75 citations · 218 across the 11 of their papers we have counts for
4 papers · 1 filter
On the Initialization of Graph Neural Networks
Jiahang Li, Yakun Song, Xiang Song +1
Graph Neural Networks (GNNs) have displayed considerable promise in graph representation learning across various applications. The core learning process requires the initialization…
From Hypergraph Energy Functions to Hypergraph Neural Networks
Yuxin Wang, Quan Gan, Xipeng Qiu +2
Hypergraphs are a powerful abstraction for representing higher-order interactions between entities of interest. To exploit these relationships in making downstream predictions, a v…
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
Qitian Wu, Wentao Zhao, Zenan Li +2
Graph neural networks have been extensively studied for learning with inter-connected data. Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing,…
Learning Manifold Dimensions with Conditional Variational Autoencoders
Yijia Zheng, Tong He, Yixuan Qiu +1
Although the variational autoencoder (VAE) and its conditional extension (CVAE) are capable of state-of-the-art results across multiple domains, their precise behavior is still not…