Results 1 to 10 of about 123,539 (262)

Knowledge graph augmentation: consistency, immutability, reliability, and context [PDF]

PeerJ Computer Science, 2023
A knowledge graph is convenient for storing knowledge in artificial intelligence applications. On the other hand, it has some shortcomings that need to be improved.
Savaş Takan
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A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation [PDF]

BMC Bioinformatics
Background The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechnology
Xianghu Jia, Weiwen Luo, Jiaqi Li, Jieqi Xing, Hongjie Sun, Shunyao Wu, Xiaoquan Su   +6 more
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Domain adaptation, self-supervision, and generative augmentation enhance GNNs for breast cancer prediction [PDF]

Scientific Reports
Breast cancer presents substantial molecular heterogeneity, requiring accurate subtype classification, receptor-status prediction, and survival estimation for precision care.
Shi Qiu, Yun Zhao, Xiuchang Li
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Noise-augmented contrastive learning with attention for knowledge-aware collaborative recommendation [PDF]

Scientific Reports
Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, Graph Convolutional Network (GCN) and Graph Attention Network (GAT) based model has gradually become the theme of Collaborative Knowledge Graph (CKG).
Wanyi Gu, Hua Xu, Xiang Peng, Ning Rao, Huali Zhu   +4 more
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G-CutMix: A CutMix-based graph data augmentation method for bot detection in social networks. [PDF]

PLoS ONE
The CutMix technique is a sophisticated approach for augmenting data in order to train neural network-based image classifiers. Essentially, it involves cutting out a portion of a random image and pasting it into the same location as another image ...
Yan Li, Shuhao Shi, Xiaofeng Guo, Chunhua Zhou, Qian Hu   +4 more
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Geometric Biplane Graphs II: Graph Augmentation [PDF]

Graphs and Combinatorics, 2015
We study biplane graphs drawn on a finite point set $S$ in the plane in general position. This is the family of geometric graphs whose vertex set is $S$ and which can be decomposed into two plane graphs. We show that every sufficiently large point set admits a 5-connected biplane graph and that there are arbitrarily large point sets that do not admit ...
Hurtado Díaz, Fernando Alfredo, Garcia Olaverri, Alfredo Martin, Korman Cozzetti, Matías, Matos, Inés P., Saumell, Maria, Silveira, Rodrigo Ignacio, Tejel Altarriba, Francisco Javier, Tóth, Csaba D.   +7 more
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Graph Augmentation Learning

Companion Proceedings of the Web Conference 2022, 2022
Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for graph-based applications such as social network analysis and traffic flow forecasting. However, the underlying reasons for the effectiveness of these GAL methods are still unclear.
Yu, Shuo, Huang, Huafei, Dao, Minh N., Xia, Feng   +3 more
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Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation

Applied Sciences, 2021
The task of charge prediction is to predict the charge based on the fact description. Existing methods have a good effect on the prediction of high-frequency charges, but the prediction of low-frequency charges is still a challenge. Moreover, there exist
Peipeng Wang, Xiuguo Zhang, Zhiying Cao
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Graph contrastive learning with implicit augmentations

Neural Networks, 2023
Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e.g., randomly adding or dropping edges and nodes). Nevertheless, altering certain edges or nodes can unexpectedly change the graph characteristics, and choosing the optimal perturbing ratio for each dataset requires onerous manual tuning. In this
Huidong Liang, Xingjian Du, Bilei Zhu, Zejun Ma, Ke Chen, Junbin Gao   +5 more
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Joint data and feature augmentation for self-supervised representation learning on point clouds

Graphical Models, 2023
To deal with the exhausting annotations, self-supervised representation learning from unlabeled point clouds has drawn much attention, especially centered on augmentation-based contrastive methods.
Zhuheng Lu, Yuewei Dai, Weiqing Li, Zhiyong Su   +3 more
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