10.1k citations · 17.4k across the 69 of their papers we have counts for
19 papers · 1 filter
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule
Miao Zhang, Jilin Hu, Steven Su +4
Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and…
Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting
Chuanpan Zheng, Xiaoliang Fan, Shirui Pan +5
Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem. Typically, they constructed a static spatial graph at e…
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
Yizhen Zheng, Ming Jin, Shirui Pan +4
Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information,…
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications
Bang Wu, Xiangwen Yang, Shirui Pan +1
Graph Neural Networks (GNNs) are widely adopted to analyse non-Euclidean data, such as chemical networks, brain networks, and social networks, modelling complex relationships and i…
Leveraging Information Bottleneck for Scientific Document Summarization
Jiaxin Ju, Ming Liu, Huan Yee Koh +3
This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle. Inspired by previous work which uses t…
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs
Xu Yan, Xiaoliang Fan, Peizhen Yang +5
Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems. Existi…
TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting
Zonghan Wu, Da Zheng, Shirui Pan +3
This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data. For spatial-t…
Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering
Zonghan Wu, Shirui Pan, Guodong Long +2
Graph convolutional networks are becoming indispensable for deep learning from graph-structured data. Most of the existing graph convolutional networks share two big shortcomings.…
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
Miao Zhang, Steven Su, Shirui Pan +3
\textit{Differentiable ARchiTecture Search} (DARTS) has recently become the mainstream of neural architecture search (NAS) due to its efficiency and simplicity. With a gradient-bas…
Anomaly Detection in Dynamic Graphs via Transformer
Yixin Liu, Shirui Pan, Yu Guang Wang +4
Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity. Recent deep learning-based a…
Pseudo-Riemannian Graph Convolutional Networks
Bo Xiong, Shichao Zhu, Nico Potyka +3
Graph convolutional networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally studied through the lens of Euclidean geometry.…
Graph Learning: A Survey
Feng Xia, Ke Sun, Shuo Yu +4
Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social sys…
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Ming Jin, Yizhen Zheng, Yuan-Fang Li +3
Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To…
Learning Graph Neural Networks with Positive and Unlabeled Nodes
Man Wu, Shirui Pan, Lan Du +1
Graph neural networks (GNNs) are important tools for transductive learning tasks, such as node classification in graphs, due to their expressive power in capturing complex interdep…
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
Yixin Liu, Zhao Li, Shirui Pan +3
Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recen…
Graph Self-Supervised Learning: A Survey
Yixin Liu, Ming Jin, Shirui Pan +4
Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised learning, resulting in shortcomings including he…
Task-adaptive Neural Process for User Cold-Start Recommendation
Xixun Lin, Jia Wu, Chuan Zhou +3
User cold-start recommendation is a long-standing challenge for recommender systems due to the fact that only a few interactions of cold-start users can be exploited. Recent studie…
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Shaoxiong Ji, Yue Tan, Teemu Saravirta +6
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, f…
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
Di Jin, Zhizhi Yu, Pengfei Jiao +5
Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection ha…