NeuralGLS is a hybrid algorithm that combines deep learning techniques with guided local search (GLS). It incorporates a self-adaptive graph convolutional network (GCN) that takes into account neighborhoods of varying sizes, accommodating TSP instances with different graph sizes. This GCN calculates a...
Graph Neural Networks with Generated Parameters for Relation Extraction Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun ACL 2019 Dynamically Fused Graph Network for Multi-hop Reasoning Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu ACL 2019...
Graph Neural Networks with Generated Parameters for Relation Extraction Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun ACL 2019 Dynamically Fused Graph Network for Multi-hop Reasoning Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu ACL 2019...
Graph Neural Network Review Taylor Wu Graph Neural Networks for Recommender Systems 图神经网络在推荐系统中的应用。 主要方式: 1)没有额外信息,对user-item interactions构成的graph使用。 2)加入knowledge graph,对knowledge graph使用。 3)加入user social network… 时雨苍剑发表于推荐系统文... Graph Convoluti...
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural
2024 Graph Local Homophily Network for Anomaly Detection CIKM 2024 Link Link 2024 Effective Illicit Account Detection on Large Cryptocurrency MultiGraphs CIKM 2024 Link Link 2024 LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection CIKM 2024 Link Link 2024 Collaborative Fraud Detecti...
Data clustering uses two major approaches using this method: the statistical approach and the neural network approach (Sanse and Sharma, 2015b). Examples of the Model-Based Clustering method includes EM (Expectation-Maximization) (Fraley and Raftery, 1998; Dempster et al., 1977; Mclachlan and ...
14. NAS:Graph Neural Network Architecture Search for Molecular Property PredictionICBD 20201. Graph RegressionNoneQM7, QM8, QM9, ESOL, FreeSolv, Lipophilicity 13.Neural Pooling for Graph Neural NetworksArXiv 20201. Graph ClassificationNoneMUTAG, PTC PROTEINS, NCI1, COLLAB, IMDB-B, IMDB-M, RE...
despite the success of the meta-path-guided heterogeneous graph embedding method, The choice of meta-path is still an open and challenging problem. the design of the meta-path scheme largely depends on domain knowledge. in this paper, We propose a heterogeneous graph neural network with ...
GNN: graph neural network Contributed by Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai. Content 1. Survey 2. Models 2.1 Basic Models 2.2 Graph Types 2.3 Pooling Methods 2.4 Analysis 2.5 Efficiency 2.6 Explainability 3. Applications 3.1 Physics 3.2 Chemistry an...