IJCAI19-Attributed Graph Clustering: A Deep Attentional Embedding Approach motivation 1)如名,这篇文章的侧重点之一是想探索更好的利用节点信息。 2)大部分嵌入式方法都在开发深度学习方法以学习更好的的图形嵌入,然后在其上应用经典的聚类方法。但是这样两阶段式的方法由于缺乏目标导向通常会导致性能欠佳。针对这个...
We propose a community-based clustering framework that is used to identify the users having similar interests. For community detection, we propose a hybrid framework with a combination of minibatch K-means and DBSCAN. The framework scales well in terms of the size of the data set. We ...
Zhang, Community detection in complex networks using density-based clustering algorithm and manifold learning, Physica A, 464 (2016) 221-230.Community detection in complex networks using density-based clustering algorithm and manifold learning. You T,Cheng HM,Ning YZ,et al. Physica a-Statistical ...
Research ArticleCommunity Detection Based on Density Peak Clustering Modeland Multiple Attribute Decision-Making Strategy TOPSISJianjunCheng ,XuWang ,WenshuangGong ,JunLi ,NuoChen ,andXiaoyunChenSchool of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, ChinaCorrespondence shoul...
Among these detection methods, those based on hierarchical clustering, modularity maximisation, statistical inference or clique detection in graphs stand out [18]. In this work, we have relied on a method developed by Girvan and Newman (GN) based on the centrality measure called edge betweenness ...
we use an improved density peak clustering to obtain the number of cores as the pre-defined parameter of nonnegative matrix factorization. Then we adopt nonnegative double singular value decomposition initialization which can rapidly reduce the approximation error of nonnegative matrix factorization. Fina...
The communities are detected by the hierarchical clustering algorithm based on the edge-weighted similarity. Finally, the number of detected communities is detected by the partition density. Also, the extensively experimental study shows that the performance of the proposed user interest detection (PUID...
proposed a density-based network clustering algorithm SCAN [4], which can efficiently and effectively detect the communities in networks. However, it relies on a manual density threshold parameter ε, and the clustering result is quite sensitive to the parameter ε. To overcome the above problem ...
The problem of community detection in a multilayer network can effectively be addressed by aggregating the community structures separately generated for each network layer, in order to infer a consensus solution for the input network. To this purpose, clustering ensemble methods developed in the data ...
Correction to: Community detection and unveiling of hierarchy in networks: a density-based clustering approachAn amendment to this paper has been published and can be accessed via the original article.doi:10.1007/s41109-020-00285-zFelfli, Zineb...