476 citations · 930 across the 34 of their papers we have counts for
12 papers · 1 filter
Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
Jingyun Zhang, Hao Peng, Jianxin Li +2
Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks. Although Graph Contrastive Learning has demonstrated p…
Graph Structure Learning with Variational Information Bottleneck
Qingyun Sun, Jianxin Li, Hao Peng +4
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications. Most empirical studies of GNNs directly take the observed graph as input, assuming th…
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network
Xingcheng Fu, Jianxin Li, Jia Wu +6
Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. Most existingGNNs embed graph data into Euclidean space and thus are less effective to cap…
Distributed Optimization of Graph Convolutional Network using Subgraph Variance
Taige Zhao, Xiangyu Song, Jianxin Li +2
In recent years, Graph Convolutional Networks (GCNs) have achieved great success in learning from graph-structured data. With the growing tendency of graph nodes and edges, GCN tra…
A Robust and Generalized Framework for Adversarial Graph Embedding
Jianxin Li, Xingcheng Fu, Hao Peng +5
Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-qua…
Differentially Private Federated Knowledge Graphs Embedding
Hao Peng, Haoran Li, Yangqiu Song +2
Knowledge graph embedding plays an important role in knowledge representation, reasoning, and data mining applications. However, for multiple cross-domain knowledge graphs, state-o…
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks
Gongxu Luo, Jianxin Li, Jianlin Su +5
Graph representation learning has achieved great success in many areas, including e-commerce, chemistry, biology, etc. However, the fundamental problem of choosing the appropriate…
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks
Jianxin Li, Hao Peng, Yuwei Cao +4
Graph neural networks (GNNs) have been widely used in deep learning on graphs. They can learn effective node representations that achieve superior performances in graph analysis ta…
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism
Qingyun Sun, Jianxin Li, Hao Peng +4
Graph representation learning has attracted increasing research attention. However, most existing studies fuse all structural features and node attributes to provide an overarching…
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng +4
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a h…
Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market
Hao Peng, Jianxin Li, Zheng Wang +5
We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and…
Graph Convolutional Neural Networks via Motif-based Attention
Hao Peng, Jianxin Li, Qiran Gong +3
Many real-world problems can be represented as graph-based learning problems. In this paper, we propose a novel framework for learning spatial and attentional convolution neural ne…