Results 101 to 110 of about 76,384 (242)
COMPOSITIONS CONSTRAINED BY GRAPH LAPLACIAN MINORS
, 2014 Motivated by examples of symmetrically constrained compositions, super convex partitions, and super convex compositions, we initiate the study of partitions and compositions constrained by graph Laplacian minors. We provide a complete description of the multivariate generating functions for such compositions in the case of trees.
Braun, Benjamin +5 more
openaire +3 more sources
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma +4 more
wiley +1 more source
Enhancing generalized spectral clustering with embedding Laplacian graph regularization
CAAI Transactions on Intelligence Technology, EarlyView.Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang +5 more
wiley +1 more source
Graph Laplacian-based spectral multi-fidelity modeling. [PDF]
Sci Rep, 2023 Pinti O, Oberai AA.
europepmc +1 more source
Boosted unsupervised feature selection for tumor gene expression profiles
CAAI Transactions on Intelligence Technology, EarlyView.Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi +5 more
wiley +1 more source
On two energy-like invariants of line graphs and related graph operations
Journal of Inequalities and Applications, 2016 For a simple graph G of order n, let μ 1 ≥ μ 2 ≥ ⋯ ≥ μ n = 0 $\mu_{1}\geq\mu_{2}\geq\cdots\geq\mu_{n}=0$ be its Laplacian eigenvalues, and let q 1 ≥ q 2 ≥ ⋯ ≥ q n ≥ 0 $q_{1}\geq q_{2}\geq\cdots\geq q_{n}\geq0$ be its signless Laplacian eigenvalues.
Xiaodan Chen, Yaoping Hou, Jingjian Li
doaj +1 more source
Bio‐Inspired Optimisation Methods Applied to Low Carbon Power and Energy Problems: A Survey
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Bio‐inspired optimisation methods have been widely applied to complex real‐world problems, particularly in low‐carbon power and energy systems, where optimisation tasks often involve high‐dimensional, constrained and mixed‐integer characteristics.
Tianyu Hu +4 more
wiley +1 more source
Efficient Learning of Transform-Domain LMS Filter Using Graph Laplacian. [PDF]
IEEE Trans Neural Netw Learn Syst, 2023 Batabyal T, Weller D, Kapur J, Acton ST.
europepmc +1 more source
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Accurately predicting line loss rates is crucial for effective management in distribution networks, particularly for short‐term multihorizon forecasts ranging from 1 hour to 1 week. In this study, we propose attention‐GCN–LSTM, a novel method that integrates graph convolutional networks (GCN), long short‐term memory (LSTM) and a three‐level ...
Jie Liu +4 more
wiley +1 more source
The Laplacian Spread of a Tree
Discrete Mathematics & Theoretical Computer Science, 2008 The Laplacian spread of a graph is defined to be the difference between the largest eigenvalue and the second smallest eigenvalue of the Laplacian matrix of the graph.
Yi-Zheng Fan +3 more
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