Graph Clustering Via Intra-Cluster Density Maximization

2020 ◽  
pp. 37-48
Author(s):  
Pierre Miasnikof ◽  
Leonidas Pitsoulis ◽  
Anthony J. Bonner ◽  
Yuri Lawryshyn ◽  
Panos M. Pardalos
Keyword(s):  
Graph Clustering ◽  
Cluster Density

Clustering Vertices in Weighted Graphs

2011 ◽  
pp. 285-298
Author(s):  
Derry Tanti Wijaya ◽  
Stephane Bressan
Keyword(s):  
Graph Algorithms ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Weighted Graph ◽  
Graph Clustering ◽  
Weighted Graphs ◽  
Similarity Relations ◽  
Natural Notion ◽  
Cluster Density ◽  
Markov Clustering

Clustering is the unsupervised process of discovering natural clusters so that objects within the same cluster are similar and objects from different clusters are dissimilar. In clustering, if similarity relations between objects are represented as a simple, weighted graph where objects are vertices and similarities between objects are weights of edges; clustering reduces to the problem of graph clustering. A natural notion of graph clustering is the separation of sparsely connected dense sub graphs from each other based on the notion of intra-cluster density vs. inter-cluster sparseness. In this chapter, we overview existing graph algorithms for clustering vertices in weighted graphs: Minimum Spanning Tree (MST) clustering, Markov clustering, and Star clustering. This includes the variants of Star clustering, MST clustering and Ricochet.

scholarly journals A density-based statistical analysis of graph clustering algorithm performance

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Pierre Miasnikof ◽  
Alexander Y Shestopaloff ◽  
Anthony J Bonner ◽  
Yuri Lawryshyn ◽  
Panos M Pardalos
Keyword(s):  
Clustering Algorithm ◽  
Statistical Significance ◽  
Null Model ◽  
Graph Clustering ◽  
Significance Test ◽  
Statistical Interpretation ◽  
Algorithm Performance ◽  
Graph Theoretic ◽  
Clustering Quality ◽  
Cluster Density

Abstract We introduce graph clustering quality measures based on comparisons of global, intra- and inter-cluster densities, an accompanying statistical significance test and a step-by-step routine for clustering quality assessment. Our work is centred on the idea that well-clustered graphs will display a mean intra-cluster density that is higher than global density and mean inter-cluster density. We do not rely on any generative model for the null model graph. Our measures are shown to meet the axioms of a good clustering quality function. They have an intuitive graph-theoretic interpretation, a formal statistical interpretation and can be tested for significance. Empirical tests also show they are more responsive to graph structure, less likely to breakdown during numerical implementation and less sensitive to uncertainty in connectivity than the commonly used measures.

Formation Mechanisms for Multiply Twinned Particles During Nucleation in the Au/MICA System

1975 ◽  
Vol 33 ◽  
pp. 84-85
Author(s):  
Eal H. Lee ◽  
Helmut Poppa
Keyword(s):  
Deposition Time ◽  
High Vacuum ◽  
Dark Field ◽  
Ultra High Vacuum ◽  
Formation Mechanisms ◽  
Heat Treated ◽  
Cluster Density ◽  
Diffraction Patterns ◽  
Crystal Particle ◽  
Multiply Twinned Particles

The formation of thin films of gold on mica has been studied in ultra-high vacuum (5xl0-10 torr) . The mica substrates were heat-treated for 24 hours at 375°C, cleaved, and annealed for 15 minutes at the deposition temperature of 300°C prior to deposition. An impingement flux of 3x1013 atoms cm-2 sec-1 was used. These conditions were found to give high number densities of multiple twin particles and are based on a systematic series of nucleation experiments described elsewhere. Individual deposits of varying deposition time were made and examined by bright and dark field TEM after "cleavage preparation" of highly transparent specimens. In the early stages of growth, the films generally consist of small particles which are either single crystals or multiply twinned; a strong preference for multiply twinned particles was found whenever the particle number densities were high. Fig. 1 shows the stable cluster density ns and the variation with deposition time of multiple twin particle and single crystal particle densities, respectively. Corresponding micrographs and diffraction patterns are shown in Fig. 2.

An Efficient Method for Attributed Graph Clustering

2014 ◽  
Vol 36 (8) ◽  
pp. 1704-1713 ◽  
Author(s):  
Ye WU ◽  
Zhi-Nong ZHONG ◽  
Wei XIONG ◽  
Luo CHEN ◽  
Ning JING
Keyword(s):  
Efficient Method ◽  
Graph Clustering ◽  
Attributed Graph

Fuzzy graph clustering

2021 ◽  
Author(s):  
Yong Peng ◽  
Xin Zhu ◽  
Feiping Nie ◽  
Wanzeng Kong ◽  
Yuan Ge
Keyword(s):  
Graph Clustering ◽  
Fuzzy Graph

scholarly journals Local Graph Clustering with Network Lasso

2020 ◽  
pp. 1-1
Author(s):  
Alexander Jung ◽  
Yasmin Sarcheshmehpour
Keyword(s):  
Graph Clustering ◽  
Local Graph

Image Co-saliency Detection and Instance Co-segmentation using Attention Graph Clustering based Graph Convolutional Network

2021 ◽  
pp. 1-1
Author(s):  
Tengpeng Li ◽  
Kaihua Zhang ◽  
Shiwen Shen ◽  
Bo Liu ◽  
Qingshan Liu ◽  
...  
Keyword(s):  
Saliency Detection ◽  
Graph Clustering ◽  
Convolutional Network
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