210 citations · 584 across the 37 of their papers we have counts for
37 papers
Eliminating Propagation Delay: Attention-Based Spatial-Temporal Fusion Graph Convolution Network for Traffic Flow Prediction
Jinpeng Chen, Ziyu Yu, Tao Wang +5
Predicting traffic flow is crucial to optimizing transportation systems and improving urban mobility. Many graph convolution-based models have been proposed to extract spatial-temp…
Deep Multi-View Channel-Wise Spatio-Temporal Network for Traffic Flow Prediction
Hao Miao, Senzhang Wang, Meiyue Zhang +3
Accurately forecasting traffic flows is critically important to many real applications including public safety and intelligent transportation systems. The challenges of this proble…
Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation
Jiangnan Xia, Yu Yang, Senzhang Wang +3
POI recommendation is practically important to facilitate various Location-Based Social Network services, and has attracted rising research attention recently. Existing works gener…
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Peiyan Zhang, Yuchen Yan, Xi Zhang +4
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing GNN-…
Continual Learning on Dynamic Graphs via Parameter Isolation
Peiyan Zhang, Yuchen Yan, Chaozhuo Li +4
Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetti…
Robust Graph Structure Learning via Multiple Statistical Tests
Yaohua Wang, FangYi Zhang, Ming Lin +3
Graph structure learning aims to learn connectivity in a graph from data. It is particularly important for many computer vision related tasks since no explicit graph structure is a…
Geometric Interaction Augmented Graph Collaborative Filtering
Yiding Zhang, Chaozhuo Li, Senzhang Wang +2
Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly resear…
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks
Jiaqiang Zhang, Senzhang Wang, Songcan Chen
Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging d…
HousE: Knowledge Graph Embedding with Householder Parameterization
Rui Li, Jianan Zhao, Chaozhuo Li +9
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties. However, existing approaches can on…
Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space
Yaohua Wang, Yaobin Zhang, Fangyi Zhang +4
Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Gr…
Semi-Supervised Variational User Identity Linkage via Noise-Aware Self-Learning
Chaozhuo Li, Senzhang Wang, Zheng Liu +3
User identity linkage, which aims to link identities of a natural person across different social platforms, has attracted increasing research interest recently. Existing approaches…
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…
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…
Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks
Qingyun Sun, Hao Peng, Jianxin Li +5
Name disambiguation aims to identify unique authors with the same name. Existing name disambiguation methods always exploit author attributes to enhance disambiguation results. How…
Adversarial Directed Graph Embedding
Shijie Zhu, Jianxin Li, Hao Peng +2
Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly…
Interpretable Deep Learning Model for Online Multi-touch Attribution
Dongdong Yang, Kevin Dyer, Senzhang Wang
In online advertising, users may be exposed to a range of different advertising campaigns, such as natural search or referral or organic search, before leading to a final transacti…
SOM-based DDoS Defense Mechanism using SDN for the Internet of Things
Yunfei Meng, Zhiqiu Huang, Senzhang Wang +2
To effectively tackle the security threats towards the Internet of things, we propose a SOM-based DDoS defense mechanism using software-defined networking (SDN) in this paper. The…
DeepVS: An Efficient and Generic Approach for Source Code Modeling Usage
Yasir Hussain, Zhiqiu Huang, Yu Zhou +1
The source code suggestions provided by current IDEs are mostly dependent on static type learning. These suggestions often end up proposing irrelevant suggestions for a peculiar co…
Deep Transfer Learning for Source Code Modeling
Yasir Hussain, Zhiqiu Huang, Yu Zhou +1
In recent years, deep learning models have shown great potential in source code modeling and analysis. Generally, deep learning-based approaches are problem-specific and data-hungr…
Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
Chuan Shi, Xiaotian Han, Li Song +4
Recently, recommender systems play a pivotal role in alleviating the problem of information overload. Latent factor models have been widely used for recommendation. Most existing l…
Behavior-aware Service Access Control Mechanism using Security Policy Monitoring for SOA Systems
Yunfei Meng, Zhiqiu Huang, Senzhang Wang +3
Service-oriented architecture (SOA) system has been widely utilized at many present business areas. However, SOA system is loosely coupled with multiple services and lacks the rele…
Label-Aware Graph Convolutional Networks
Hao Chen, Yue Xu, Feiran Huang +5
Recent advances in Graph Convolutional Networks (GCNs) have led to state-of-the-art performance on various graph-related tasks. However, most existing GCN models do not explicitly…
Deep Learning for Spatio-Temporal Data Mining: A Survey
Senzhang Wang, Jiannong Cao, Philip S. Yu
With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly av…
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
Hao Peng, Jianxin Li, Qiran Gong +5
CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text clas…