791 citations · 2.5k across the 66 of their papers we have counts for
9 papers · 1 filter
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach
Lanning Wei, Zhiqiang He, Huan Zhao +1
In recent years, Graph Neural Networks (GNNs) have been popular in graph representation learning which assumes the homophily property, i.e., the connected nodes have the same label…
Search to Pass Messages for Temporal Knowledge Graph Completion
Zhen Wang, Haotong Du, Quanming Yao +1
Completing missing facts is a fundamental task for temporal knowledge graphs (TKGs). Recently, graph neural network (GNN) based methods, which can simultaneously explore topologica…
Searching a High-Performance Feature Extractor for Text Recognition Network
Hui Zhang, Quanming Yao, James T. Kwok +1
Feature extractor plays a critical role in text recognition (TR), but customizing its architecture is relatively less explored due to expensive manual tweaking. In this work, inspi…
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
Yinfeng Li, Chen Gao, Quanming Yao +3
Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Exist…
AutoWeird: Weird Translational Scoring Function Identified by Random Search
Hansi Yang, Yongqi Zhang, Quanming Yao
Scoring function (SF) measures the plausibility of triplets in knowledge graphs. Different scoring functions can lead to huge differences in link prediction performances on differe…
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search
Xu Wang, Huan Zhao, Lanning Wei +1
Aiming at two molecular graph datasets and one protein association subgraph dataset in OGB graph classification task, we design a graph neural network framework for graph classific…
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
Quanming Yao, Yaqing Wang, Bo Han +1
Nonconvex regularization has been popularly used in low-rank matrix learning. However, extending it for low-rank tensor learning is still computationally expensive. To address this…
KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning
Yongqi Zhang, Zhanke Zhou, Quanming Yao +1
While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently. To solve this problem, we first analyze the properti…
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Zhen Xu, Lanning Wei, Huan Zhao +4
Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in mode…