44 citations · 181 across the 13 of their papers we have counts for
12 papers · 1 filter
: 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…
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…
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…
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…