Graph theory (2nd edn.), by Reinhard Diestel. Pp. 312. £24. 2000. ISBN 0 387
                  98976 5 (Springer-Verlag).

Anderson, Ian

doi:10.2307/3620535

Cited by 5 publications

(2 citation statements)

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“…Many real-world data can be represented as graphs [1], such as molecular structures, physical models, social networks [2], and traffic networks [3]. The nodes in the graph normally are entities with features, and the edges represent associations between entities.…”

Section: Introductionmentioning

confidence: 99%

Semi-AttentionAE: An Integrated Model for Graph Representation Learning

et al. 2021

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Graph embedding learns low-dimensional vector representations which capture and preserve information in original graphs. Common shallow neural networks and deep autoencoder only use adjacency matrix as input, and usually ignore node attributes and features. Shallow graph neural networks cannot spread the node characteristic information on a large scale. Many deep models suffer the problem of oversmoothing. Therefore, these methods can't fully incorporate network information. In this paper, we propose a novel Semi-AttentionAE model to fully utilize node features, node labels, and network structure. More specifically, we integrate a supervised information extraction graph attention network to capture both node features and network structure, with an unsupervised feature extraction autoencoder to reduce dimension while preserving structure information. Finally, ensemble learning is introduced to jointly train the combined model to obtain final embedding. We conduct the node classification and visualization experiments on four real-world datasets, including two citation networks, one co-occurrence network, and one commodity network. The results suggest that the proposed Semi-AttentionAE model is capable of embedding both graph structure and node features. The integrated model has successfully exceeded or matched performance across four well-established baselines.

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Section: Introductionmentioning

confidence: 99%

Semi-AttentionAE: An Integrated Model for Graph Representation Learning

et al. 2021

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“…Hence, it is widely used in image segmentation [ 17 ], bioinformatics [ 18 ], pattern recognition [ 19 ], data mining [ 20 ], and other fields [ 21 , 22 ]. Representative clustering algorithms cover K-means [ 23 , 24 ] and fuzzy c-means [ 25 , 26 ] based on partitioning; AGNES [ 27 ], BIRCH [ 28 , 29 ], and CURE [ 30 , 31 ] based on hierarchy; DBSCAN [ 32 ] and OPTICS [ 33 ] based on density; STING [ 34 ] based on grids; and statistical clustering CMM [ 35 ] and spectral clustering [ 36 ] based on graph theory [ 37 ]. K-means is extremely sensitive to noise and the selection of the initial clustering centers, and the number of clusters needs to be set a priori.…”

Section: Introductionmentioning

confidence: 99%

Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders

Lin

Wang

Chen

2020

Computational Intelligence and Neuroscience

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Density peaks clustering algorithm (DPC) has attracted the attention of many scholars because of its multiple advantages, including efficiently determining cluster centers, a lower number of parameters, no iterations, and no border noise. However, DPC does not provide a reliable and specific selection method of threshold (cutoff distance) and an automatic selection strategy of cluster centers. In this paper, we propose density peaks clustering by zero-pointed samples (DPC-ZPSs) of regional group borders. DPC-ZPS finds the subclusters and the cluster borders by zero-pointed samples (ZPSs). And then, subclusters are merged into individuals by comparing the density of edge samples. By iteration of the merger, the suitable dc and cluster centers are ensured. Finally, we compared state-of-the-art methods with our proposal in public datasets. Experiments show that our algorithm automatically determines cutoff distance and centers accurately.

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Construction and transformation method of 3D models based on the chain-type modular structure

Zhang,

Wang,

Zhang

et al. 2024

Complex Intell. Syst.

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This study proposes a method of constructing and transforming three-dimensional (3D) models that can convert a 3D model into a chain-type modular configuration and realize the mutual transformation between different configurations with a straight chain as the intermediate state through standard folding steps. A method for detailed representation of voxels is proposed. Based on detailed voxels, an accelerated generation algorithm for the connection forest, which can describe the possible chain configurations, is developed. The foldability verification of the configurations and the generation of the folding operations are realized according to the folding rules. A collision detection algorithm based on encoding and projection is also introduced to detect collisions in the process of folding sequence generation. In this work, an interactive platform is established for users to calculate the input model transformation through simple operations and obtain a simulation animation of the folding operations. The experimental cases prove the effectiveness of the method in constructing and transforming the chain-type modular configurations of the input 3D models.

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Graph theory (2nd edn.), by Reinhard Diestel. Pp. 312. £24. 2000. ISBN 0 387 98976 5 (Springer-Verlag).

Cited by 5 publications

References 0 publications

Semi-AttentionAE: An Integrated Model for Graph Representation Learning

Semi-AttentionAE: An Integrated Model for Graph Representation Learning

Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders

Construction and transformation method of 3D models based on the chain-type modular structure

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