256 citations · 925 across the 21 of their papers we have counts for
22 papers
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
Wei Jin, Haitao Mao, Zheng Li +17
Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences i…
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Enyan Dai, Limeng Cui, Zhengyang Wang +5
Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured data. However, recent works show that GNNs are vulnerable to adversarial attacks which can foo…
Self-Explainable Graph Neural Networks for Link Prediction
Huaisheng Zhu, Dongsheng Luo, Xianfeng Tang +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance for link prediction. However, GNNs suffer from poor interpretability, which limits their adoptions in critic…
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers
Chen Liang, Haoming Jiang, Zheng Li +3
Knowledge distillation has been shown to be a powerful model compression approach to facilitate the deployment of pre-trained language models in practice. This paper focuses on tas…
Short Text Pre-training with Extended Token Classification for E-commerce Query Understanding
Haoming Jiang, Tianyu Cao, Zheng Li +6
E-commerce query understanding is the process of inferring the shopping intent of customers by extracting semantic meaning from their search queries. The recent progress of pre-tra…
DiP-GNN: Discriminative Pre-Training of Graph Neural Networks
Simiao Zuo, Haoming Jiang, Qingyu Yin +3
Graph neural network (GNN) pre-training methods have been proposed to enhance the power of GNNs. Specifically, a GNN is first pre-trained on a large-scale unlabeled graph and then…
Condensing Graphs via One-Step Gradient Matching
Wei Jin, Xianfeng Tang, Haoming Jiang +4
As training deep learning models on large dataset takes a lot of time and resources, it is desired to construct a small synthetic dataset with which we can train deep learning mode…
MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose
Chenyan Wu, Yandong Li, Xianfeng Tang +1
Reconstructing multi-human body mesh from a single monocular image is an important but challenging computer vision problem. In addition to the individual body mesh models, we need…
Task-Agnostic Graph Explanations
Yaochen Xie, Sumeet Katariya, Xianfeng Tang +4
Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph-structured data. Due to their broad applications, there is an increasing need to develop tools to explai…
Jointly Attacking Graph Neural Network and its Explanations
Wenqi Fan, Wei Jin, Xiaorui Liu +7
Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are highly vulnerable to adve…
Semi-Supervised Graph-to-Graph Translation
Tianxiang Zhao, Xianfeng Tang, Xiang Zhang +1
Graph translation is very promising research direction and has a wide range of potential real-world applications. Graph is a natural structure for representing relationship and int…
Graph Structure Learning for Robust Graph Neural Networks
Wei Jin, Yao Ma, Xiaorui Liu +3
Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, cal…
Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps
Xianfeng Tang, Yozen Liu, Neil Shah +3
With the rapid growth and prevalence of social network applications (Apps) in recent years, understanding user engagement has become increasingly important, to provide useful insig…
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks
Xianfeng Tang, Huaxiu Yao, Yiwei Sun +5
Graph Convolutional Networks (GCNs) show promising results for semi-supervised learning tasks on graphs, thus become favorable comparing with other approaches. Despite the remarkab…
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Xianfeng Tang, Huaxiu Yao, Yiwei Sun +3
Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, rea…
Node Injection Attacks on Graphs via Reinforcement Learning
Yiwei Sun, Suhang Wang, Xianfeng Tang +2
Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes. However, in such scenarios, ther…
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh +2
Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e.g., friendship, shar…
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
Xianfeng Tang, Yandong Li, Yiwei Sun +3
Graph neural networks (GNNs) are widely used in many applications. However, their robustness against adversarial attacks is criticized. Prior studies show that using unnoticeable m…
Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference
Xianfeng Tang, Boqing Gong, Yanwei Yu +4
Real-time traffic volume inference is key to an intelligent city. It is a challenging task because accurate traffic volumes on the roads can only be measured at certain locations w…
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
Huaxiu Yao, Yiding Liu, Ying Wei +2
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy maki…
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Huaxiu Yao, Xianfeng Tang, Hua Wei +2
Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For exam…
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Huaxiu Yao, Fei Wu, Jintao Ke +6
Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate re…