33 citations · 135 across the 23 of their papers we have counts for
6 papers · 1 filter
GegenNet: Spectral Convolutional Neural Networks for Link Sign Prediction in Signed Bipartite Graphs
Hewen Wang, Renchi Yang, Xiaokui Xiao
Given a signed bipartite graph (SBG) G with two disjoint node sets U and V, the goal of link sign prediction is to predict the signs of potential links connecting U and V based on…
GraSP: Simple yet Effective Graph Similarity Predictions
Haoran Zheng, Jieming Shi, Renchi Yang
Graph similarity computation (GSC) is to calculate the similarity between one pair of graphs, which is a fundamental problem with fruitful applications in the graph community. In G…
Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs
Hewen Wang, Renchi Yang, Xiaokui Xiao
Graph representation learning (GRL) is to encode graph elements into informative vector representations, which can be used in downstream tasks for analyzing graph-structured data a…
Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks
Yurui Lai, Xiaoyang Lin, Renchi Yang +1
In recent years, graph neural networks (GNNs) have emerged as a potent tool for learning on graph-structured data and won fruitful successes in varied fields. The majority of GNNs…
SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network
Ziang Zhou, Jieming Shi, Renchi Yang +2
Heterogeneous graphs are ubiquitous to model complex data. There are urgent needs on powerful heterogeneous graph neural networks to effectively support important applications. We…
LSCALE: Latent Space Clustering-Based Active Learning for Node Classification
Juncheng Liu, Yiwei Wang, Bryan Hooi +2
Node classification on graphs is an important task in many practical domains. It usually requires labels for training, which can be difficult or expensive to obtain in practice. Gi…