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Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey [PDF]
IEEE Access, 2021 Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also ...
Joakim Skarding +2 more
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Edge-labeling Graph Neural Network for Few-shot Learning [PDF]
Computer Vision and Pattern Recognition, 2019 In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning.
Kim, Jongmin +3 more
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Survey of Graph Neural Network [PDF]
Jisuanji gongcheng, 2021 With the continuous development of the computer and Internet technologies,graph neural network has become an important research area in artificial intelligence and big data.Graph neural network can effectively transmit and aggregate information between ...
WANG Jianzong, KONG Lingwei, HUANG Zhangcheng, XIAO Jing
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Graph Neural Network-Based Anomaly Detection in Multivariate Time Series [PDF]
AAAI Conference on Artificial Intelligence, 2021 Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains
Ailin Deng, Bryan Hooi
semanticscholar +1 more source
Atomistic Line Graph Neural Network for improved materials property predictions [PDF]
npj Computational Materials, 2021 Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models.
K. Choudhary, Brian L. DeCost
semanticscholar +1 more source
Survey of Graph Neural Network in Recommendation System [PDF]
Jisuanji kexue yu tansuo, 2022 Recommendation system (RS) was introduced because of a lot of information. Due to the diversity, complexity, and sparseness of data, traditional recommendation system can not solve the current problem well.
WU Jing, XIE Hui, JIANG Huowen
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Study on Degree of Node Based Personalized Propagation of Neural Predictions forSocial Networks [PDF]
Jisuanji kexue, 2023 Graph is an important and fundamental data structure that presents in a wide variety of practical scenarios.With the rapid development of the Internet in recent years,there has been a huge increase in social network graph data,and the analysis of this ...
SHAO Yunfei, SONG You, WANG Baohui
doaj +1 more source
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning [PDF]
Knowledge Discovery and Data Mining, 2021 Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN).
Xiao Wang, Nian Liu, Hui-jun Han, C. Shi
semanticscholar +1 more source
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding [PDF]
The Web Conference, 2020 A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types. Heterogeneous graph embedding is to embed rich structural and semantic information of a heterogeneous graph into low ...
Xinyu Fu +3 more
semanticscholar +1 more source
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [PDF]
Knowledge Discovery and Data Mining, 2020 Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph ...
J. Qiu +7 more
semanticscholar +1 more source