Results 31 to 40 of about 1,639,507 (316)
Explainability in Graph Neural Networks: A Taxonomic Survey [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability.
Hao Yuan +3 more
semanticscholar +1 more source
Online social network user performance prediction by graph neural networks
IJAIN (International Journal of Advances in Intelligent Informatics), 2022 Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN)
Fail Gafarov +2 more
doaj +1 more source
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
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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Graph Neural Networks for Social Recommendation [PDF]
The Web Conference, 2019 In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data.
Wenqi Fan +6 more
semanticscholar +1 more source
Graph Neural Networks in Recommender Systems: A Survey [PDF]
ACM Computing Surveys, 2020 With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field.
Shiwen Wu, Fei Sun, Fei Sun, Bin Cui
semanticscholar +1 more source
Graph Neural Networks in Network Neuroscience
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph.
Alaa Bessadok +2 more
openaire +4 more sources
Session-based Recommendation with Graph Neural Networks [PDF]
AAAI Conference on Artificial Intelligence, 2018 The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations.
Shu Wu +5 more
semanticscholar +1 more source
GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks [PDF]
Web Search and Data Mining, 2021 Node classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification.
Tianxiang Zhao, Xiang Zhang, Suhang Wang
semanticscholar +1 more source
Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study
Molecules, 2022 Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein–protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological
Hang Zhou +4 more
doaj +1 more source
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks [PDF]
AAAI Conference on Artificial Intelligence, 2018 In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion.
Christopher Morris +6 more
semanticscholar +1 more source