Results 11 to 20 of about 123,539 (262)

Augmenting Graphs to Minimize the Diameter [PDF]

Algorithmica, 2013
15 pages, 3 ...
FRATI, FABRIZIO, Serge Gaspers, Joachim Gudmundsson, Luke Mathieson   +3 more
openaire   +5 more sources

Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities.

PLoS ONE, 2021
Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding ...
Jeongtae Son, Dongsup Kim
doaj   +1 more source

Are Graph Augmentations Necessary?

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022
10 pages.
Yu, Junliang, Yin, Hongzhi, Xia, Xin, Chen, Tong, Cui, Lizhen, Nguyen, Quoc Viet Hung   +5 more
openaire   +2 more sources

Adding Edges for Maximizing Weighted Reachability

Algorithms, 2020
In this paper, we consider the problem of improving the reachability of a graph. We approach the problem from a graph augmentation perspective, in which a limited set size of edges is added to the graph to increase the overall number of reachable nodes ...
Federico Corò, Gianlorenzo D'Angelo, Cristina M. Pinotti   +2 more
doaj   +1 more source

GCL-ALG: graph contrastive learning with adaptive learnable view generators [PDF]

PeerJ Computer Science
Data augmentation is a pivotal part of graph contrastive learning, which can mine implicit graph data information to improve the quality of representation learning.
Yafang Li, Jie Kang, Zhihua Chu, Baokai Zu   +3 more
doaj   +2 more sources

Combinatorics and Algorithms for Augmenting Graphs [PDF]

Graphs and Combinatorics, 2015
13 pages, 5 ...
Dabrowski, Konrad K., Lozin, Vadim V., de Werra, Dominique, Zamaraev, Viktor   +3 more
openaire   +5 more sources

Study on Graph Data Augmentation Based on Graph Entropy Theory [PDF]

Jisuanji kexue
Graph data augmentation,as a technique aiming to enhance the performance of graph neural networks,involves transforming and expanding the graph structure and node features to increase the diversity and quantity of training data.The integrity of ...
FU Kun, CUI Jingyuan, DANG Xing, CHENG Xiao, YING Shicong, LI Jianwei
doaj   +1 more source

Correlation Clustering and Two-edge-connected Augmentation for Planar Graphs [PDF]

, 2015
In correlation clustering, the input is a graph with edge-weights, where every edge is labelled either + or - according to similarity of its endpoints.
Klein, Philip N., Mathieu, Claire, Zhou, Hang   +2 more
core   +1 more source

MDA: An Intelligent Medical Data Augmentation Scheme Based on Medical Knowledge Graph for Chinese Medical Tasks

Applied Sciences, 2022
Text data augmentation is essential in the field of medicine for the tasks of natural language processing (NLP). However, most of the traditional text data augmentation focuses on the English datasets, and there is little research on the Chinese datasets
Binbin Shi, Lijuan Zhang, Jie Huang, Huilin Zheng, Jian Wan, Lei Zhang   +5 more
doaj   +1 more source

Improving Knowledge Graph Entity Alignment with Graph Augmentation

, 2023
Entity alignment (EA) which links equivalent entities across different knowledge graphs (KGs) plays a crucial role in knowledge fusion. In recent years, graph neural networks (GNNs) have been successfully applied in many embedding-based EA methods. However, existing GNN-based methods either suffer from the structural heterogeneity issue that especially
Xie, Feng, Zeng, Xiang, Zhou, Bin, Tan, Yusong   +3 more
openaire   +2 more sources