b'Shirui Pan'

research

∙ 09/20/2023

Towards Data-centric Graph Machine Learning: Review and Outlook

Data-centric AI, with its primary focus on the collection, management, a...

0 Xin Zheng, et al. ∙

research

∙ 08/31/2023

Domain-adaptive Message Passing Graph Neural Network

Cross-network node classification (CNNC), which aims to classify nodes i...

0 Xiao Shen, et al. ∙

research

∙ 08/18/2023

CARLA: A Self-supervised Contrastive Representation Learning Approach for Time Series Anomaly Detection

We introduce a Self-supervised Contrastive Representation Learning Appro...

0 Zahra Zamanzadeh Darban, et al. ∙

research

∙ 08/10/2023

𝒢^2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns

Node classification is the task of predicting the labels of unlabeled no...

0 Qin Zhang, et al. ∙

research

∙ 07/17/2023

Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection

Multivariate time-series anomaly detection is critically important in ma...

0 Yu Zheng, et al. ∙

research

∙ 07/07/2023

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Time series are the primary data type used to record dynamic system meas...

0 Ming Jin, et al. ∙

research

∙ 06/13/2023

Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs

Real-world graphs generally have only one kind of tendency in their conn...

1 Yizhen Zheng, et al. ∙

research

∙ 06/05/2023

Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

Graph condensation, which reduces the size of a large-scale graph by syn...

0 Xin Zheng, et al. ∙

research

∙ 06/01/2023

A Survey on Fairness-aware Recommender Systems

As information filtering services, recommender systems have extremely en...

0 Di Jin, et al. ∙

research

∙ 05/21/2023

Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs

The great learning ability of deep learning models facilitates us to com...

0 Guangsi Shi, et al. ∙

research

∙ 05/06/2023

A Minimal Approach for Natural Language Action Space in Text-based Games

Text-based games (TGs) are language-based interactive environments for r...

0 Dongwon Kelvin Ryu, et al. ∙

research

∙ 05/04/2023

Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain

International maritime crime is becoming increasingly sophisticated, oft...

0 Fatemeh Shiri, et al. ∙

research

∙ 04/17/2023

Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion

Knowledge graphs (KGs), as a structured form of knowledge representation...

0 Linhao Luo, et al. ∙

research

∙ 02/23/2023

Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs

Graph neural architecture search (NAS) has gained popularity in automati...

0 Xin Zheng, et al. ∙

research

∙ 01/30/2023

On the Interaction between Node Fairness and Edge Privacy in Graph Neural Networks

Due to the emergence of graph neural networks (GNNs) and their widesprea...

0 He Zhang, et al. ∙

research

∙ 12/22/2022

TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems

The scalability problem has been one of the most significant barriers li...

0 Yuanzhe Zhang, et al. ∙

research

∙ 11/25/2022

Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

Unsupervised graph representation learning (UGRL) has drawn increasing r...

0 Yixin Liu, et al. ∙

research

∙ 11/15/2022

Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs

Link prediction on dynamic graphs is an important task in graph mining. ...

0 Linhao Luo, et al. ∙

research

∙ 11/10/2022

A Comprehensive Survey on Distributed Training of Graph Neural Networks

Graph neural networks (GNNs) have been demonstrated to be a powerful alg...

0 Haiyang Lin, et al. ∙

research

∙ 11/09/2022

Deep Learning for Time Series Anomaly Detection: A Survey

Time series anomaly detection has applications in a wide range of resear...

0 Zahra Zamanzadeh Darban, et al. ∙

research

∙ 10/30/2022

How Far are We from Robust Long Abstractive Summarization?

Abstractive summarization has made tremendous progress in recent years. ...

0 Huan Yee Koh, et al. ∙

research

∙ 10/17/2022

Unifying Graph Contrastive Learning with Flexible Contextual Scopes

Graph contrastive learning (GCL) has recently emerged as an effective le...

0 Yizhen Zheng, et al. ∙

research

∙ 09/02/2022

Rethinking Efficiency and Redundancy in Training Large-scale Graphs

Large-scale graphs are ubiquitous in real-world scenarios and can be tra...

3 Xin Liu, et al. ∙

research

∙ 08/10/2022

Fast Heterogeneous Federated Learning with Hybrid Client Selection

Client selection schemes are widely adopted to handle the communication-...

0 Guangyuan Shen, et al. ∙

research

∙ 07/06/2022

Simple and Efficient Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) deliver the powerful capabil...

0 Xiaocheng Yang, et al. ∙

research

∙ 07/03/2022

An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics

Long documents such as academic articles and business reports have been ...

0 Huan Yee Koh, et al. ∙

research

∙ 06/25/2022

Geometry Contrastive Learning on Heterogeneous Graphs

Self-supervised learning (especially contrastive learning) methods on he...

0 Shichao Zhu, et al. ∙

research

∙ 06/04/2022

Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation

Automatic generation of ophthalmic reports using data-driven neural netw...

0 Mingjie Li, et al. ∙

research

∙ 06/03/2022

Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination

Graph contrastive learning (GCL) alleviates the heavy reliance on label ...

8 Yizhen Zheng, et al. ∙

research

∙ 05/16/2022

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Graph neural networks (GNNs) have emerged as a series of competent graph...

0 He Zhang, et al. ∙

research

∙ 04/28/2022

Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting–Full Version

A variety of real-world applications rely on far future information to m...

1 Razvan-Gabriel Cirstea, et al. ∙

research

∙ 03/29/2022

Towards Spatio-Temporal Aware Traffic Time Series Forecasting–Full Version

Traffic time series forecasting is challenging due to complex spatio-tem...

0 Razvan-Gabriel Cirstea, et al. ∙

research

∙ 03/21/2022

Paraphrasing Techniques for Maritime QA system

There has been an increasing interest in incorporating Artificial Intell...

6 Fatemeh Shiri, et al. ∙

research

∙ 02/25/2022

Projective Ranking-based GNN Evasion Attacks

Graph neural networks (GNNs) offer promising learning methods for graph-...

0 He Zhang, et al. ∙

research

∙ 02/25/2022

MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation

Large-scale e-commercial platforms in the real-world usually contain var...

0 Linhao Luo, et al. ∙

research

∙ 02/17/2022

Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

Multivariate time series forecasting has long received significant atten...

0 Ming Jin, et al. ∙

research

∙ 02/14/2022

Graph Neural Networks for Graphs with Heterophily: A Survey

Recent years have witnessed fast developments of graph neural networks (...

10 Xin Zheng, et al. ∙

research

∙ 02/11/2022

From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach

Anomaly detection from graph data is an important data mining task in ma...

0 Yu Zheng, et al. ∙

research

∙ 02/10/2022

Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

Graph neural networks (GNNs) have been a hot spot of recent research and...

0 Xin Liu, et al. ∙

research

∙ 01/24/2022

Multi-Graph Fusion Networks for Urban Region Embedding

Learning the embeddings for urban regions from human mobility data can r...

42 Shangbin Wu, et al. ∙

research

∙ 01/19/2022

Dual Space Graph Contrastive Learning

Unsupervised graph representation learning has emerged as a powerful too...

26 Haoran Yang, et al. ∙

research

∙ 01/17/2022

Towards Unsupervised Deep Graph Structure Learning

In recent years, graph neural networks (GNNs) have emerged as a successf...

0 Yixin Liu, et al. ∙

research

∙ 01/15/2022

Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection

Client selection strategies are widely adopted to handle the communicati...

0 Guangyuan Shen, et al. ∙

research

∙ 11/25/2021

BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule

Differentiable Architecture Search (DARTS) has received massive attentio...

0 Miao Zhang, et al. ∙

research

∙ 11/25/2021

Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting

Recent studies focus on formulating the traffic forecasting as a spatio-...

10 Chuanpan Zheng, et al. ∙

research

∙ 11/20/2021

Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming

Graph representation learning (GRL) is critical for graph-structured dat...

0 Yizhen Zheng, et al. ∙

research

∙ 10/17/2021

Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications

Graph Neural Networks (GNNs) are widely adopted to analyse non-Euclidean...

0 Bang Wu, et al. ∙

research

∙ 10/04/2021

Leveraging Information Bottleneck for Scientific Document Summarization

This paper presents an unsupervised extractive approach to summarize sci...

0 Jiaxin Ju, et al. ∙

research

∙ 09/20/2021

Robust Physical-World Attacks on Face Recognition

Face recognition has been greatly facilitated by the development of deep...

0 Xin Zheng, et al. ∙

research

∙ 07/10/2021

Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering

Graph convolutional networks are becoming indispensable for deep learnin...

0 Zonghan Wu, et al. ∙

Shirui Pan

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