1.2k citations · 2.8k across the 60 of their papers we have counts for
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Efficient and Scalable Multi-View Masked Autoencoder for Heterogeneous Graph Self-Supervised Learning
Hao Geng, Deqing Wang, Lihao Ge +5
Recent years have witnessed the flourishing of self-supervised learning on heterogeneous graphs, e.g., masked autoencoder-based methods. However, existing methods suffer from three…
Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
Xiaodong Lu, Xiaohan Wang, Jiajun Chai +7
Reinforcement Learning with Verifiable Rewards (RLVR) is an effective paradigm for improving the reasoning capabilities of large language models. However, existing RLVR methods uti…
Graph Dimension Attention Networks for Enterprise Credit Assessment
Shaopeng Wei, Beni Egressy, Xingyan Chen +4
Enterprise credit assessment is critical for evaluating financial risk, and Graph Neural Networks (GNNs), with their advanced capability to model inter-entity relationships, are a…
Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking
Xi Chen, Chuan Qin, Chuyu Fang +5
In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills a…
Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph
Chenguang Du, Kaichun Yao, Hengshu Zhu +3
Recent years have witnessed the rapid development of heterogeneous graph neural networks (HGNNs) in information retrieval (IR) applications. Many existing HGNNs design a variety of…
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Zhenwei Tang, Shichao Pei, Zhao Zhang +4
Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given…
Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks
Yu Zhao, Shaopeng Wei, Huaming Du +5
Bi-type multi-relational heterogeneous graph (BMHG) is one of the most common graphs in practice, for example, academic networks, e-commerce user behavior graph and enterprise know…
Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection
Yongchun Zhu, Dongbo Xi, Bowen Song +4
With the explosive growth of the e-commerce industry, detecting online transaction fraud in real-world applications has become increasingly important to the development of e-commer…
Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources
Yongchun Zhu, Fuzhen Zhuang, Deqing Wang
While Unsupervised Domain Adaptation (UDA) algorithms, i.e., there are only labeled data from source domains, have been actively studied in recent years, most algorithms and theore…
Neural Hierarchical Factorization Machines for User's Event Sequence Analysis
Dongbo Xi, Fuzhen Zhuang, Bowen Song +6
Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance. However, existing popular…
Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification
Dongbo Xi, Fuzhen Zhuang, Yanchi Liu +3
Human mobility data accumulated from Point-of-Interest (POI) check-ins provides great opportunity for user behavior understanding. However, data quality issues (e.g., geolocation i…
Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction
Qiang Zhou, Jingjing Gu, Xinjiang Lu +4
Potential crowd flow prediction for new planned transportation sites is a fundamental task for urban planners and administrators. Intuitively, the potential crowd flow of the new c…
Combat Data Shift in Few-shot Learning with Knowledge Graph
Yongchun Zhu, Fuzhen Zhuang, Xiangliang Zhang +4
Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learn…
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection
Dongbo Xi, Bowen Song, Fuzhen Zhuang +5
With the explosive growth of e-commerce, online transaction fraud has become one of the biggest challenges for e-commerce platforms. The historical behaviors of users provide rich…
Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems
Jingjing Gu, Qiang Zhou, Jingyuan Yang +4
Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility. However, the flexibility of these dockless systems c…
Transfer Learning Toolkit: Primers and Benchmarks
Fuzhen Zhuang, Keyu Duan, Tongjia Guo +4
The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function. It…
A Comprehensive Survey on Transfer Learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan +5
Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this wa…
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning
Changying Du, Jia He, Changde Du +3
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices…
Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling
Changying Du, Fuzhen Zhuang, Jia He +2
In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data. To simultaneously recognize n…
Policy Gradients for Contextual Recommendations
Feiyang Pan, Qingpeng Cai, Pingzhong Tang +2
Decision making is a challenging task in online recommender systems. The decision maker often needs to choose a contextual item at each step from a set of candidates. Contextual ba…