79 citations · 286 across the 15 of their papers we have counts for
4 papers · 1 filter
Powerful Graph Convolutioal Networks with Adaptive Propagation Mechanism for Homophily and Heterophily
Tao Wang, Rui Wang, Di Jin +2
Graph Convolutional Networks (GCNs) have been widely applied in various fields due to their significant power on processing graph-structured data. Typical GCN and its variants work…
BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks
Di Jin, Xiangchen Song, Zhizhi Yu +4
Graph convolutional networks (GCNs), aiming to integrate high-order neighborhood information through stacked graph convolution layers, have demonstrated remarkable power in many ne…
Graph Neural Network for Large-Scale Network Localization
Wenzhong Yan, Di Jin, Zhidi Lin +1
Graph neural networks (GNNs) are popular to use for classifying structured data in the context of machine learning. But surprisingly, they are rarely applied to regression problems…
From Static to Dynamic Node Embeddings
Di Jin, Sungchul Kim, Ryan A. Rossi +1
We introduce a general framework for leveraging graph stream data for temporal prediction-based applications. Our proposed framework includes novel methods for learning an appropri…