232 citations · 1.4k across the 72 of their papers we have counts for
31 papers · 1 filter
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation
Siwei Zhang, Yun Xiong, Yao Zhang +3
Continuous-time dynamic graph modeling is a crucial task for many real-world applications, such as financial risk management and fraud detection. Though existing dynamic graph mode…
Measuring and Sampling: A Metric-guided Subgraph Learning Framework for Graph Neural Network
Jiyang Bai, Yuxiang Ren, Jiawei Zhang
Graph neural network (GNN) has shown convincing performance in learning powerful node representations that preserve both node attributes and graph structural information. However,…
A Self-supervised Mixed-curvature Graph Neural Network
Li Sun, Zhongbao Zhang, Junda Ye +4
Graph representation learning received increasing attentions in recent years. Most of existing methods ignore the complexity of the graph structures and restrict graphs in a single…
Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs
Li Sun, Zhongbao Zhang, Jiawei Zhang +4
Learning representations for graphs plays a critical role in a wide spectrum of downstream applications. In this paper, we summarize the limitations of the prior works in three fol…
Label Contrastive Coding based Graph Neural Network for Graph Classification
Yuxiang Ren, Jiyang Bai, Jiawei Zhang
Graph classification is a critical research problem in many applications from different domains. In order to learn a graph classification model, the most widely used supervision co…
Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
Yizhu Jiao, Yun Xiong, Jiawei Zhang +3
Graph representation learning has attracted lots of attention recently. Existing graph neural networks fed with the complete graph data are not scalable due to limited computation…
G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning
Jiawei Zhang
The recent GRAPH-BERT model introduces a new approach to learning graph representations merely based on the attention mechanism. GRAPH-BERT provides an opportunity for transferring…
Graph Neural Distance Metric Learning with Graph-Bert
Jiawei Zhang
Graph distance metric learning serves as the foundation for many graph learning problems, e.g., graph clustering, graph classification and graph matching. Existing research works o…
Segmented Graph-Bert for Graph Instance Modeling
Jiawei Zhang
In graph instance representation learning, both the diverse graph instance sizes and the graph node orderless property have been the major obstacles that render existing representa…
Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network
Jiyang Bai, Yuxiang Ren, Jiawei Zhang
Graph neural networks (GNNs) have achieved outstanding performance in learning graph-structured data and various tasks. However, many current GNNs suffer from three common problems…
Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification
Jiawei Zhang
Existing graph neural networks may suffer from the "suspended animation problem" when the model architecture goes deep. Meanwhile, for some graph learning scenarios, e.g., nodes wi…
Graph-Bert: Only Attention is Needed for Learning Graph Representations
Jiawei Zhang, Haopeng Zhang, Congying Xia +1
The dominant graph neural networks (GNNs) over-rely on the graph links, several serious performance problems with which have been witnessed already, e.g., suspended animation probl…
EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph
Yuxiang Ren, Hao Zhu, Jiawei Zhang +2
Fraud detection is extremely critical for e-commerce business. It is the intent of the companies to detect and prevent fraud as early as possible. Existing fraud detection methods…
Heterogeneous Deep Graph Infomax
Yuxiang Ren, Bo Liu, Chao Huang +3
Graph representation learning is to learn universal node representations that preserve both node attributes and structural information. The derived node representations can be used…
JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation
Zhiwei Liu, Lei Zheng, Jiawei Zhang +2
Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighbo…
GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation
Jiawei Zhang, Lin Meng
The existing graph neural networks (GNNs) based on the spectral graph convolutional operator have been criticized for its performance degradation, which is especially common for th…
Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview
Jiawei Zhang
Graph neural networks denote a group of neural network models introduced for the representation learning tasks on graph data specifically. Graph neural networks have been demonstra…
DEAM: Adaptive Momentum with Discriminative Weight for Stochastic Optimization
Jiyang Bai, Yuxiang Ren, Jiawei Zhang
Optimization algorithms with momentum, e.g., (ADAM), have been widely used for building deep learning models due to the faster convergence rates compared with stochastic gradient d…
Graph Neural Lasso for Dynamic Network Regression
Yixin Chen, Lin Meng, Jiawei Zhang
The regression of multiple inter-connected sequence data is a problem in various disciplines. Formally, we name the regression problem of multiple inter-connected data entities as…
IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
Lin Meng, Jiawei Zhang
Deep learning models have achieved huge success in numerous fields, such as computer vision and natural language processing. However, unlike such fields, it is hard to apply tradit…
Derivative-Free Global Optimization Algorithms: Population based Methods and Random Search Approaches
Jiawei Zhang
In this paper, we will provide an introduction to the derivative-free optimization algorithms which can be potentially applied to train deep learning models. Existing deep learning…
Derivative-Free Global Optimization Algorithms: Bayesian Method and Lipschitzian Approaches
Jiawei Zhang
In this paper, we will provide an introduction to the derivative-free optimization algorithms which can be potentially applied to train deep learning models. Existing deep learning…
Gradient Descent based Optimization Algorithms for Deep Learning Models Training
Jiawei Zhang
In this paper, we aim at providing an introduction to the gradient descent based optimization algorithms for learning deep neural network models. Deep learning models involving mul…
A Self-Organizing Tensor Architecture for Multi-View Clustering
Lifang He, Chun-ta Lu, Yong Chen +4
In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other. Although sev…