除了作为local clustering的度量,看待clustering coefficient的另一种观点是,它计算每个节点的local neighborhood内的闭合三角形(closed triangles)的数量。用更精确的术语来说,clustering coefficient与节点的ego graph(即包含该节点及其邻居,以及节点和邻居节点之间的所有边的子图)中的实际三角形数量与所有可能的三角形数量之...
基于图的聚类集成与数据可视化分析-graph - based clustering integration and data visualization analysis.docx,摘要聚类分析是一门重要学科,其依据测量对象的内在特性或相似度将对象进行分组,在多种社会科学领域中都有应用,如数据压缩、数据挖掘、图像分割和信息检索
” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 1, pp. 17–37, 2017. [103] N. Lao and W. W. Cohen, “Relational retrieval using a combination of path-constrained random walks,” Machine learning, vol. 81, no. 1, pp. ...
In this paper, we propose KANO, a new KG-enhanced molecular contrastive learning with functional prompt method, which consists of three main components: (1) ElementKG construction and embedding, (2) contrastive-based pre-training and (3) prompt-enhanced fine-tuning. An overview of KANO is show...
Her research interests are data mining, information retrieval and machine learning.References (28) S. Schaeffer Graph clustering Comput. Sci. Rev. (2007) S. Fortunato Community detection in graphs Phys. Rep. (2010) M. Fellows et al. Graph-based data clustering with overlaps Discrete Optim. (...
Reproduces the results of MinCutPool as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Pooling". unsupervised-learningspectral-clusteringgraph-neural-networksgraph-pooling UpdatedMar 15, 2025 Python
Node drop pooling: Graph U-net中的Top-K池化 Node clustering pooling: DiffPool 图粗粒化在链路预测中的应用 概述:基于h跳封闭子图的图粗粒化 Weisfeiler-Lehman Neural Machine (WLNM) SEAL框架 A Multi-Scale Approach for Graph Link Prediction
PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks (ICLR, 2024, Poster) [paper][code] Hypergraph Dynamic System (ICLR, 2024, Poster) [paper] Deep Temporal Graph Clustering (ICLR, 2024, Poster) [paper][code] GraphPulse: Topological representations for temporal graph property predictio...
Jiang, “MGAE: Marginal- ized graph autoencoder for graph clustering,” in CIKM, 2017. [45] F. Manessi and A. Rozza, “Graph-based neural network models with multiple self-supervised auxiliary tasks,” Pattern Recognition Letters, vol. 148, pp. 15–21, 2021. [46] J. Park, M. Lee...
As a unique non-Euclidean* data structure for machine learning, graph analysis focuses on node classification, link prediction, and clustering.Graph neural networks (GNNs) are deep learning based methods that operate on graph domain. *数据可以分两大类:欧几里得数据和非欧几里得数据。欧几里得数据的特点...