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...
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...
community detection介绍
5.Density-based Clustering（基于密度的聚类）http://www.cs.ecu.edu/dingq/CSCI6905/readings/CLARANS...
Fortunato (2010) 2010 Discussed critical issues like the importance of clustering, a procedure to test methods for comparison of static and overlapping community detection methods. Reported very few applications related to real-world networks, Outlier aware models were not included in the survey. Papad...
provided the original work is properly cited.Community detection is one of the key research directions in complex network studies. We propose a community detectionalgorithm based on a density peak clustering model and multiple attribute decision-making strategy, TOPSIS (Technique forOrderPreferencebySimila...
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...
To overcome these drawbacks, we introduce a community detection-based clustering method. Community detection-based approaches do not need a priori knowledge of the number of clusters [65], are not heavily parametrized, and can handle multivariate and multidimensional data without dimensionality reduction...
Clustering algorithms evaluate how nodes are clustered in communities, in closely-knit sets, or in highly or loosely interconnected groups. These algorithms can identify meaningful groups or clusters of nodes in a network, revealing hidden patterns and structures that can provide insights into the orga...
This paper proposes the embedding-based Silhouette community detection (SCD), an approach for detecting communities, based on clustering of network node embeddings, i.e. real valued representations of nodes derived from their neighborhoods. We investigate the performance of the proposed SCD approach on...