Zobrazeno 1 - 10
of 2 433
pro vyhledávání: '"clustering in graphs"'
Unsupervised node clustering (or community detection) is a classical graph learning task. In this paper, we study algorithms, which exploit the geometry of the graph to identify densely connected substructures, which form clusters or communities. Our
Externí odkaz:
http://arxiv.org/abs/2307.10155
Cut-based directed graph (digraph) clustering often focuses on finding dense within-cluster or sparse between-cluster connections, similar to cut-based undirected graph clustering methods. In contrast, for flow-based clusterings the edges between clu
Externí odkaz:
http://arxiv.org/abs/2203.01388
Publikováno v:
2022 IEEE Conference on Control Technology and Applications (CCTA)
We propose a novel robust decentralized graph clustering algorithm that is provably equivalent to the popular spectral clustering approach. Our proposed method uses the existing wave equation clustering algorithm that is based on propagating waves th
Externí odkaz:
http://arxiv.org/abs/2203.00004
Structural Clustering ($DynClu$) is one of the most popular graph clustering paradigms. In this paper, we consider $StrClu$ under two commonly adapted similarities, namely Jaccard similarity and cosine similarity on a dynamic graph, $G = \langle V, E
Externí odkaz:
http://arxiv.org/abs/2108.11549
Relationship between agents can be conveniently represented by graphs. When these relationships have different modalities, they are better modelled by multilayer graphs where each layer is associated with one modality. Such graphs arise naturally in
Externí odkaz:
http://arxiv.org/abs/2103.03235
Publikováno v:
International Symposium on Intelligent Data Analysis, pp. 350-361. Springer, Cham, 2021
Recent advances in specialized hardware for solving optimization problems such quantum computers, quantum annealers, and CMOS annealers give rise to new ways for solving real-word complex problems. However, given current and near-term hardware limita
Externí odkaz:
http://arxiv.org/abs/2012.11391
Publikováno v:
In Computers and Electrical Engineering July 2022 101
Publikováno v:
In Data & Knowledge Engineering March 2022 138
Modern graph or network datasets often contain rich structure that goes beyond simple pairwise connections between nodes. This calls for complex representations that can capture, for instance, edges of different types as well as so-called "higher-ord
Externí odkaz:
http://arxiv.org/abs/1910.09943
Graph clustering is a basic technique in machine learning, and has widespread applications in different domains. While spectral techniques have been successfully applied for clustering undirected graphs, the performance of spectral clustering algorit
Externí odkaz:
http://arxiv.org/abs/1908.02096