33 citations · 135 across the 23 of their papers we have counts for
7 papers · 1 filter
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…
Spectral Subspace Clustering for Attributed Graphs
Xiaoyang Lin, Renchi Yang, Haoran Zheng +1
Subspace clustering seeks to identify subspaces that segment a set of n data points into k (k<<n) groups, which has emerged as a powerful tool for analyzing data from various domai…
A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation
Yiran Li, Gongyao Guo, Jieming Shi +4
Attributed networks containing entity-specific information in node attributes are ubiquitous in modeling social networks, e-commerce, bioinformatics, etc. Their inherent network to…
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…
Effective Clustering on Large Attributed Bipartite Graphs
Renchi Yang, Yidu Wu, Xiaoyang Lin +3
Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes that are associated with rich attributes, su…
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…