Results 241 to 250 of about 6,652,811 (287)

Effective Temporal Graph Learning via Personalized PageRank. [PDF]

Entropy (Basel)
Liao Z, Liu T, He Y, Lin L.
europepmc   +1 more source

GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learning. [PDF]

Brief Bioinform
Zhang W, Tian Q, Cao Y, Fan W, Jiang D, Wang Y, Li Q, Wei XY.   +7 more
europepmc   +1 more source

Accurately predicting optimal conditions for microorganism proteins through geometric graph learning and language model. [PDF]

Commun Biol
Zhu M, Song Y, Yuan Q, Yang Y.
europepmc   +1 more source

Bi-level graph learning unveils prognosis-relevant tumor microenvironment patterns in breast multiplexed digital pathology. [PDF]

Patterns (N Y)
Wang Z, Santa-Maria CA, Popel AS, Sulam J.   +3 more
europepmc   +1 more source

Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures. [PDF]

Nat Commun
Song Y, Yuan Q, Chen S, Zeng Y, Zhao H, Yang Y.   +5 more
europepmc   +1 more source

DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data. [PDF]

Transl Psychiatry
Chen W, Yang J, Sun Z, Zhang X, Tao G, Ding Y, Gu J, Bu J, Wang H.   +8 more
europepmc   +1 more source

Graph Learning

Foundations and Trends® in Signal Processing
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of graph neural networks (GNNs). Over
Feng Xia, Ciyuan Peng, Jing Ren, F. Febrinanto, Renqiang Luo, Vidya Saikrishna, Shuo Yu, Xiangjie Kong   +7 more
semanticscholar   +3 more sources

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Dynamic Graph Learning with Content-guided Spatial-Frequency Relation Reasoning for Deepfake Detection

Computer Vision and Pattern Recognition, 2023
With the springing up of face synthesis techniques, it is prominent in need to develop powerful face forgery detection methods due to security concerns.
Yuan Wang, Kun Yu, Chen Chen, Xiyuan Hu, Silong Peng   +4 more
semanticscholar   +1 more source

LGMRec: Local and Global Graph Learning for Multimodal Recommendation

AAAI Conference on Artificial Intelligence, 2023
The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various ...
Zhiqiang Guo, Jianjun Li, Guohui Li, Chaoyang Wang, Si Shi, Bin Ruan   +5 more
semanticscholar   +1 more source

Data Augmentation for Deep Graph Learning

SIGKDD Explorations, 2022
Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks.
Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu   +3 more
semanticscholar   +1 more source