Results 21 to 30 of about 2,166 (199)
An edge sensitivity based gradient attack on graph isomorphic networks for graph classification problems. [PDF]
Sci RepAbstract Graph Neural Networks have gained popularity over the past few years. Their ability to model relationships between entities of the same and different kind, represent molecules, model flow etc. have made them a go to tool for researchers.
Srinivasan S, OmKumar C.
europepmc +4 more sources
Discover Applied SciencesAccurately predicting molecular properties is crucial in chemistry and materials science, as it facilitates faster discovery, minimizes experimental costs, and guides rational molecular design. While traditional quantum chemistry approaches remain widely
Jamshaid Ul Rahamn +3 more
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Isomorphic Boolean networks and dense interaction graphs
CoRR, 2021 13 ...
Aymeric Picard Marchetto, Adrien Richard
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A Zero Knowledge Authentication Protocol Based on Novel Heuristic Algorithm of Dense Induced Subgraphs Isomorphism [PDF]
Engineering and Technology Journal, 2015 Graphs provide an useful mathematical tool for modeling various real world phenomena. Dense graphs arise in many places of interest, for instance the internet and social networks to name just two. The density of a graph should be a real number reflecting
N. M. G. Al-Saidi +2 more
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Learning to Count Isomorphisms with Graph Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational cost.
Xingtong Yu +3 more
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Training Sensitivity in Graph Isomorphism Network [PDF]
Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020 Graph neural network (GNN) is a popular tool to learn the lower-dimensional representation of a graph. It facilitates the applicability of machine learning tasks on graphs by incorporating domain-specific features. There are various options for underlying procedures (such as optimization functions, activation functions, etc.) that can be considered in ...
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Application of dynamic expansion tree for finding large network motifs in biological networks [PDF]
PeerJ, 2019 Network motifs play an important role in the structural analysis of biological networks. Identification of such network motifs leads to many important applications such as understanding the modularity and the large-scale structure of biological networks,
Sabyasachi Patra, Anjali Mohapatra
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Efficient streaming subgraph isomorphism with graph neural networks
Proceedings of the VLDB Endowment, 2021 Queries to detect isomorphic subgraphs are important in graph-based data management. While the problem of subgraph isomorphism search has received considerable attention for the static setting of a single query, or a batch thereof, existing approaches do not scale to a dynamic setting of a continuous stream of queries.
Chi Thang Duong +5 more
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Isomorphic Graph Classification Model Based on Reconstruction Error [PDF]
Jisuanji kexue yu tansuo, 2022 At present, the application of deep learning method in graph classification model focuses on the migration of convolutional neural network to graph data field, including redefinition of convolutional layer and pooling layer. Generalization of convolution
JIANG Guangfeng, HU Pengcheng, YE Hua, YANG Yanlan
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Capturing Topology in Graph Pattern Matching [PDF]
, 2011 Graph pattern matching is often defined in terms of subgraph isomorphism, an np-complete problem. To lower its complexity, various extensions of graph simulation have been considered instead.
Huai, Jinpeng +9 more
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