33 citations · 135 across the 23 of their papers we have counts for
24 papers
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…
SAFT: Structure-aware Transformers for Textual Interaction Classification
Hongtao Wang, Renchi Yang, Hewen Wang +2
Textual interaction networks (TINs) are an omnipresent data structure used to model the interplay between users and items on e-commerce websites, social networks, etc., where each…
Adaptive Local Clustering over Attributed Graphs
Haoran Zheng, Renchi Yang, Jianliang Xu
Given a graph and a seed node , the objective of local graph clustering (LGC) is to identify a subgraph (a.k.a. local cluster) surrounding in time roughl…
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…
Efficient High-Quality Clustering for Large Bipartite Graphs
Renchi Yang, Jieming Shi
A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely used to model real-world data, such as user-item purchase records, author-article publica…
Efficient and Effective Similarity Search over Bipartite Graphs
Renchi Yang
Similarity search over a bipartite graph aims to retrieve from the graph the nodes that are similar to each other, which finds applications in various fields such as online adverti…
Efficient Estimation of Pairwise Effective Resistance
Renchi Yang, Jing Tang
Given an undirected graph G, the effective resistance r(s,t) measures the dissimilarity of node pair s,t in G, which finds numerous applications in real-world problems, such as rec…
Node-wise Diffusion for Scalable Graph Learning
Keke Huang, Jing Tang, Juncheng Liu +2
Graph Neural Networks (GNNs) have shown superior performance for semi-supervised learning of numerous web applications, such as classification on web services and pages, analysis o…
Efficient Approximation Algorithms for Spanning Centrality
Shiqi Zhang, Renchi Yang, Jing Tang +2
Given a graph , the spanning centrality (SC) of an edge measures the importance of for to be connected. In practice, SC has seen extensive applic…
Effective and Efficient PageRank-based Positioning for Graph Visualization
Shiqi Zhang, Renchi Yang, Xiaokui Xiao +2
Graph visualization is a vital component in many real-world applications (e.g., social network analysis, web mining, and bioinformatics) that enables users to unearth crucial insig…
Effective and Scalable Clustering on Massive Attributed Graphs
Renchi Yang, Jieming Shi, Yin Yang +3
Given a graph G where each node is associated with a set of attributes, and a parameter k specifying the number of output clusters, k-attributed graph clustering (k-AGC) groups nod…
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…
Fast Approximate CoSimRanks via Random Projections
Renchi Yang, Xiaokui Xiao
Given a graph with nodes and two nodes , the {\em CoSimRank} value quantifies the similarity between and based on graph topology. Compared to Sim…
PANE: scalable and effective attributed network embedding
Renchi Yang, Jieming Shi, Xiaokui Xiao +3
Given a graph G where each node is associated with a set of attributes, attributed network embedding (ANE) maps each node v in G to a compact vector Xv, which can be used in downst…
Realtime Index-Free Single Source SimRank Processing on Web-Scale Graphs
Jieming Shi, Tianyuan Jin, Renchi Yang +2
Given a graph G and a node u in G, a single source SimRank query evaluates the similarity between u and every node v in G. Existing approaches to single source SimRank computation…
Efficient Algorithms for Approximate Single-Source Personalized PageRank Queries
Sibo Wang, Renchi Yang, Runhui Wang +5
Given a graph , a source node and a target node , the personalized PageRank (PPR) of with respect to is the probability that a random walk starting from termi…
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank
Renchi Yang, Jieming Shi, Xiaokui Xiao +2
Given an input graph G and a node v in G, homogeneous network embedding (HNE) maps the graph structure in the vicinity of v to a compact, fixed-dimensional feature vector. This pap…
Efficient Estimation of Heat Kernel PageRank for Local Clustering
Renchi Yang, Xiaokui Xiao, Zhewei Wei +3
Given an undirected graph G and a seed node s, the local clustering problem aims to identify a high-quality cluster containing s in time roughly proportional to the size of the clu…