1.2k citations · 2.1k across the 31 of their papers we have counts for
31 papers
: Generative Open-Set Node Classification on Graphs with Proxy Unknowns
Qin Zhang, Zelin Shi, Xiaolin Zhang +3
Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when a…
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Jiapu Wang, Boyue Wang, Meikang Qiu +8
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and i…
Domain-adaptive Message Passing Graph Neural Network
Xiao Shen, Shirui Pan, Kup-Sze Choi +1
Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels,…
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection
Yu Zheng, Huan Yee Koh, Ming Jin +5
Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approach…
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Ming Jin, Huan Yee Koh, Qingsong Wen +5
Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time…
Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach
Zicheng Zhao, Linhao Luo, Shirui Pan +2
Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transdu…
Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs
Yizhen Zheng, He Zhang, Vincent CS Lee +3
Real-world graphs generally have only one kind of tendency in their connections. These connections are either homophily-prone or heterophily-prone. While graphs with homophily-pron…
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Shirui Pan, Linhao Luo, Yufei Wang +3
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability…
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Xin Zheng, Miao Zhang, Chunyang Chen +3
Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has immediate benefits for various graph learni…
A Survey on Fairness-aware Recommender Systems
Di Jin, Luzhi Wang, He Zhang +4
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, whic…
Learning Strong Graph Neural Networks with Weak Information
Yixin Liu, Kaize Ding, Jianling Wang +3
Graph Neural Networks (GNNs) have exhibited impressive performance in many graph learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input graph data suf…
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
Di Jin, Luzhi Wang, Yizhen Zheng +5
Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations n…
Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
Ming Jin, Guangsi Shi, Yuan-Fang Li +7
Time series forecasting has remained a focal point due to its vital applications in sectors such as energy management and transportation planning. Spectral-temporal graph neural ne…
A Minimal Approach for Natural Language Action Space in Text-based Games
Dongwon Kelvin Ryu, Meng Fang, Shirui Pan +2
Text-based games (TGs) are language-based interactive environments for reinforcement learning. While language models (LMs) and knowledge graphs (KGs) are commonly used for handling…
Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion
Linhao Luo, Yuan-Fang Li, Gholamreza Haffari +1
Knowledge graphs (KGs), as a structured form of knowledge representation, have been widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC), which ai…
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs
Xin Zheng, Miao Zhang, Chunyang Chen +3
Graph neural architecture search (NAS) has gained popularity in automatically designing powerful graph neural networks (GNNs) with relieving human efforts. However, existing graph…
Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
He Zhang, Xingliang Yuan, Shirui Pan
Graph neural networks (GNNs) have gained significant attraction due to their expansive real-world applications. To build trustworthy GNNs, two aspects - fairness and privacy - have…
A Comprehensive Survey on Distributed Training of Graph Neural Networks
Haiyang Lin, Mingyu Yan, Xiaochun Ye +4
Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN tra…
How Far are We from Robust Long Abstractive Summarization?
Huan Yee Koh, Jiaxin Ju, He Zhang +2
Abstractive summarization has made tremendous progress in recent years. In this work, we perform fine-grained human annotations to evaluate long document abstractive summarization…
Rethinking Efficiency and Redundancy in Training Large-scale Graphs
Xin Liu, Xunbin Xiong, Mingyu Yan +4
Large-scale graphs are ubiquitous in real-world scenarios and can be trained by Graph Neural Networks (GNNs) to generate representation for downstream tasks. Given the abundant inf…
An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics
Huan Yee Koh, Jiaxin Ju, Ming Liu +1
Long documents such as academic articles and business reports have been the standard format to detail out important issues and complicated subjects that require extra attention. An…
Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation
Mingjie Li, Wenjia Cai, Karin Verspoor +3
Automatic generation of ophthalmic reports using data-driven neural networks has great potential in clinical practice. When writing a report, ophthalmologists make inferences with…
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
He Zhang, Bang Wu, Xingliang Yuan +3
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation syst…
Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version
Razvan-Gabriel Cirstea, Chenjuan Guo, Bin Yang +3
A variety of real-world applications rely on far future information to make decisions, thus calling for efficient and accurate long sequence multivariate time series forecasting. W…