169 citations · 414 across the 24 of their papers we have counts for
11 papers · 1 filter
Spatio-temporal Diffusion Point Processes
Yuan Yuan, Jingtao Ding, Chenyang Shao +2
Spatio-temporal point process (STPP) is a stochastic collection of events accompanied with time and space. Due to computational complexities, existing solutions for STPPs compromis…
Understanding Expressivity of GNN in Rule Learning
Haiquan Qiu, Yongqi Zhang, Yong Li +1
Rule learning is critical to improving knowledge graph (KG) reasoning due to their ability to provide logical and interpretable explanations. Recently, Graph Neural Networks (GNNs)…
Learning to Simulate Daily Activities via Modeling Dynamic Human Needs
Yuan Yuan, Huandong Wang, Jingtao Ding +2
Daily activity data that records individuals' various types of activities in daily life are widely used in many applications such as activity scheduling, activity recommendation, a…
KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning
Yongqi Zhang, Zhanke Zhou, Quanming Yao +1
While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently. To solve this problem, we first analyze the properti…
TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction
Xiawei Guo, Yuhan Quan, Huan Zhao +3
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance. However, existing wo…
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey
Zefang Zong, Jingwei Wang, Tao Feng +3
Recent technology development brings the boom of numerous new Demand-Driven Services (DDS) into urban lives, including ridesharing, on-demand delivery, express systems and warehous…
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
Fuxian Li, Jie Feng, Huan Yan +3
Traffic prediction is the cornerstone of an intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent tra…
Learnable Embedding Sizes for Recommender Systems
Siyi Liu, Chen Gao, Yihong Chen +2
The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manne…
Automorphic Equivalence-aware Graph Neural Network
Fengli Xu, Quanming Yao, Pan Hui +1
Distinguishing the automorphic equivalence of nodes in a graph plays an essential role in many scientific domains, e.g., computational biologist and social network analysis. Howeve…
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
Jingtao Ding, Yuhan Quan, Quanming Yao +2
Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data. As two major concerns in negative sampling…
Efficient Neural Interaction Function Search for Collaborative Filtering
Quanming Yao, Xiangning Chen, James Kwok +2
In collaborative filtering (CF), interaction function (IFC) plays the important role of capturing interactions among items and users. The most popular IFC is the inner product, whi…