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ACM Computing Surveys, 2021 In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data.
Aidan Hogan +17 more
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Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition [PDF]
AAAI Conference on Artificial Intelligence, 2018 Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and ...
Sijie Yan, Yuanjun Xiong, Dahua Lin
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Interaction graphs: Graphings [PDF]
Annals of Pure and Applied Logic, 2017 In two previous papers, we exposed a combinatorial approach to the program of Geometry of Interaction, a program initiated by Jean-Yves Girard. The strength of our approach lies in the fact that we interpret proofs by simpler structures - graphs - than Girard's constructions, while generalizing the latter since they can be recovered as special cases of
Thomas Seiller, Thomas Seiller
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A Comprehensive Survey on Graph Neural Networks [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding.
Zonghan Wu +5 more
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Graphs Cospectral with Kneser Graphs [PDF]
SSRN Electronic Journal, 2009 AMS Subject Classification ...
Haemers, W.H., Ramezani, F.
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Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting [PDF]
International Joint Conference on Artificial Intelligence, 2017 Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect ...
Ting Yu, Haoteng Yin, Zhanxing Zhu
semanticscholar +1 more source
Dynamic Graph CNN for Learning on Point Clouds [PDF]
ACM Transactions on Graphics, 2018 Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed
Yue Wang +5 more
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Neural Graph Collaborative Filtering [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019 Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or ...
Xiang Wang +4 more
semanticscholar +1 more source
Graph Convolutional Neural Networks for Web-Scale Recommender Systems [PDF]
Knowledge Discovery and Data Mining, 2018 Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items
Rex Ying +5 more
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Graph WaveNet for Deep Spatial-Temporal Graph Modeling [PDF]
International Joint Conference on Artificial Intelligence, 2019 Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying ...
Zonghan Wu +4 more
semanticscholar +1 more source