Results 71 to 80 of about 11,781 (210)
Dynamic frequent subgraph mining algorithms over evolving graphs: a survey [PDF]
PeerJ Computer ScienceFrequent subgraph mining (FSM) is an essential and challenging graph mining task used in several applications of the modern data science. Some of the FSM algorithms have the objective of finding all frequent subgraphs whereas some of the algorithms focus
Belgin Ergenç Bostanoğlu +1 more
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Macromolecular Rapid Communications, EarlyView.This work presents a structure‐aware graph convolutional network that models polymers as statistical ensembles to predict macroscopic properties. By combining topologically realistic graphs generated via kinetic Monte Carlo simulations with explicit molar mass distributions, the framework achieves high accuracy in classifying architectures and ...
Julian Kimmig +7 more
wiley +1 more source
TopoMAS: Large Language Model Driven Topological Materials Multi‐Agent System
Materials Genome Engineering Advances, EarlyView.TopoMAS is an interactive multi‐agent framework that revolutionizes topological materials discovery through human–AI collaborative intelligence. The system integrates natural language processing, knowledge retrieval from literature and databases, crystal structure generation, and automated first‐principles calculations within a unified workflow.
Baohua Zhang +5 more
wiley +1 more source
Modification and completion of geological structure knowledge graph based on pattern matching
Scientific ReportsAs a knowledge representation method, knowledge graph is widely used in intelligent question answering systems and recommendation systems. At present, the research on knowledge graph mainly focuses on information query and retrieval based on knowledge ...
Cai Lu, Xinran Xu, Bingbin Zhang
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Mathematical Methods in the Applied Sciences, EarlyView.ABSTRACT We study eigenvalue problems for the de Rham complex on varying three‐dimensional domains. Our analysis includes the Helmholtz equation as well as the Maxwell system with mixed boundary conditions and non‐constant coefficients. We provide Hadamard‐type formulas for the shape derivatives under weak regularity assumptions on the domain and its ...
Pier Domenico Lamberti +2 more
wiley +1 more source
IEEE AccessThis paper proposes a subgraph-aware classification framework that integrates efficient frequent subgraph mining with graph neural networks (GNNs) to address the limitations of existing GNNs in capturing explicit local structures.
Weiyao Xu +7 more
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High Relative Accuracy Computations With Covariance Matrices of Order Statistics
Mathematical Methods in the Applied Sciences, EarlyView.ABSTRACT In many statistical applications, numerical computations with covariance matrices need to be performed. The error made when performing such numerical computations increases with the condition number of the covariance matrix, which is related to the number of variables and the strength of the correlation between the variables. In a recent work,
Juan Baz +3 more
wiley +1 more source
Networks, EarlyView.ABSTRACT The analysis of certain properties of the underlying graph of a public transport network generates insights about the network's structure. Hereby, the choice of the graph representation depends on a trade‐off between complexity reduction and information preservation to adequately model a public transport network.
Michael Palk +2 more
wiley +1 more source
Subgraph and object context‐masked network for scene graph generation
IET Computer Vision, 2020 Scene graph generation is to recognise objects and their semantic relationships in an image and can help computers understand visual scene. To improve relationship prediction, geometry information is essential and usually incorporated into relationship ...
Zhenxing Zheng +3 more
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