169 citations · 414 across the 24 of their papers we have counts for
24 papers
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…
Road Planning for Slums via Deep Reinforcement Learning
Yu Zheng, Hongyuan Su, Jingtao Ding +2
Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustai…
Advancements in Federated Learning: Models, Methods, and Privacy
Huiming Chen, Huandong Wang, Qingyue Long +2
Federated learning (FL) is a promising technique for addressing the rising privacy and security issues. Its main ingredient is to cooperatively learn the model among the distribute…
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…
A Review-aware Graph Contrastive Learning Framework for Recommendation
Jie Shuai, Kun Zhang, Le Wu +4
Most modern recommender systems predict users preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the au…
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…
Neighboring Backdoor Attacks on Graph Convolutional Network
Liang Chen, Qibiao Peng, Jintang Li +4
Backdoor attacks have been widely studied to hide the misclassification rules in the normal models, which are only activated when the model is aware of the specific inputs (i.e., t…
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization
Huiming Chen, Huandong Wang, Quanming Yao +3
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning. The prima…
Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction
Huandong Wang, Qiaohong Yu, Yu Liu +2
With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application servic…
Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks
Zirui Zhu, Chen Gao, Xu Chen +3
Incorporating social relations into the recommendation system, i.e. social recommendation, has been widely studied in academic and industrial communities. While many promising resu…
Improving Location Recommendation with Urban Knowledge Graph
Chang Liu, Chen Gao, Depeng Jin +1
Location recommendation is defined as to recommend locations (POIs) to users in location-based services. The existing data-driving approaches of location recommendation suffer from…
Knowledge-driven Site Selection via Urban Knowledge Graph
Yu Liu, Jingtao Ding, Yong Li
Site selection determines optimal locations for new stores, which is of crucial importance to business success. Especially, the wide application of artificial intelligence with mul…
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…
Efficient Data-specific Model Search for Collaborative Filtering
Chen Gao, Quanming Yao, Depeng Jin +1
Collaborative filtering (CF), as a fundamental approach for recommender systems, is usually built on the latent factor model with learnable parameters to predict users' preferences…
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…
Role-Aware Modeling for N-ary Relational Knowledge Bases
Yu Liu, Quanming Yao, Yong Li
N-ary relational knowledge bases (KBs) represent knowledge with binary and beyond-binary relational facts. Especially, in an n-ary relational fact, the involved entities play diffe…
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…
Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases
Yu Liu, Quanming Yao, Yong Li
With the rapid development of knowledge bases (KBs), link prediction task, which completes KBs with missing facts, has been broadly studied in especially binary relational KBs (a.k…
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…
DeepDPM: Dynamic Population Mapping via Deep Neural Network
Zefang Zong, Jie Feng, Kechun Liu +2
Dynamic high resolution data on human population distribution is of great importance for a wide spectrum of activities and real-life applications, but is too difficult and expensiv…