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Graph learning based suicidal ideation detection via tree-drawing test [PDF]
Frontiers in PsychiatryIntroductionAdolescent suicide is a critical public health concern worldwide, necessitating effective methods for early detection of high suicidal ideation.
Ye Liu +5 more
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Dual-Gated Graph Convolutional Recurrent Unit with Integrated Graph Learning (DG3L): A Novel Recurrent Network Architecture with Dynamic Graph Learning for Spatio-Temporal Predictions [PDF]
EntropySpatio-temporal prediction is crucial in intelligent transportation systems (ITS) to enhance operational efficiency and safety. Although Transformer-based models have significantly advanced spatio-temporal prediction performance, recent research ...
Yuxuan Wang +4 more
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Towards Better Dynamic Graph Learning: New Architecture and Unified Library [PDF]
Neural Information Processing Systems, 2023 We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop interactions by: (1) a neighbor co-occurrence encoding scheme that explores ...
Le Yu, Leilei Sun, Bowen Du, Weifeng Lv
semanticscholar +1 more source
Self-supervised Graph Learning for Recommendation [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020 Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors.
Jiancan Wu +6 more
semanticscholar +1 more source
ROLAND: Graph Learning Framework for Dynamic Graphs [PDF]
Knowledge Discovery and Data Mining, 2022 Graph Neural Networks (GNNs) have been successfully applied to many real-world static graphs. However, the success of static graphs has not fully translated to dynamic graphs due to the limitations in model design, evaluation settings, and training ...
Jiaxuan You, Tianyu Du, J. Leskovec
semanticscholar +1 more source
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting [PDF]
AAAI Conference on Artificial Intelligence, 2022 Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we ...
Renhe Jiang +8 more
semanticscholar +1 more source
Network representation learning based on social similarities
Frontiers in Environmental Science, 2022 Analysis of large-scale networks generally requires mapping high-dimensional network data to a low-dimensional space. We thus need to represent the node and connections accurate and effectively, and representation learning could be a promising method. In
Ziwei Mo +5 more
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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
Mutual Graph Learning for Camouflaged Object Detection [PDF]
Computer Vision and Pattern Recognition, 2021 Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between such foreground objects and background surroundings make the features ...
Qiang Zhai +5 more
semanticscholar +1 more source
Generative-Contrastive Graph Learning for Recommendation [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023 By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering~(CF) based recommendation.
Yonghui Yang +7 more
semanticscholar +1 more source