Detecting community structure in complex networks via node similarity

Pan, Ying; Li, Dehua; Liu, Jianguo; Jing-zhang, Liang

doi:10.1016/j.physa.2010.03.006

Cited by 150 publications

(69 citation statements)

References 36 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, Pan et al [60] have compared all the local indices appeared in Ref. [47] in a similarity-based community detection algorithm, and their experimental results again indicate that the RA index performs best.…”

Section: Local Similarity Indicesmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,384

1,621

View full text Add to dashboard Cite

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

show abstract

Section: Local Similarity Indicesmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,384

1,621

View full text Add to dashboard Cite

show abstract

“…In microblog networks, we treat the following persons of user x as the friends of . Using these neighborhood definitions, we calculate following 9 basic stand measures based on local information [12], and combine them together as the topology feature subset for training the best weight vector. Table 1 shows the definitions of these measures [20].…”

Section: Topology Featuresmentioning

confidence: 99%

“…Much work has been done to find a proper topology similarity index [12], [13]. However, there does not appear to be one best similarity index that is superior in all settings.…”

mentioning

confidence: 99%

Link Prediction in Microblog Network Using Supervised Learning with Multiple Features

Han¹,

Xu²

2016

JCP

View full text Add to dashboard Cite

Link prediction (LP) is a fundamental network analysis task. It aims to analyze the existing links and predict the missing or potential relations between users in a social network. It can help users in finding new friends, enhance their loyalties to the web sites and build a healthy social environment. In previous researches, much attention was focused on structure information or node attributes, in order to analyze the global or local properties. Considering the nature of Microblog social network, we proposed a link prediction system combining multiple features from different perspectives, and learn a classifier from these feature subsets to predict the potential links. We train classifiers using SVM, Naï ve Bayes, and Random Forest and Logistic Regression algorithms and evaluate them using the microblog network dataset. The results show that our features perform better than the traditional features, and the combination of multiple features can achieve highest accuracy.

show abstract

“…Community structures are the natural properties of social networks [1][2][3][4][5], which evolves with the network structures changing [6][7][8][9][10][11]. Detecting community structures in dynamic networks [12][13][14][15][16] has attracted much attention.…”

Section: Introductionmentioning

confidence: 99%

Evolution properties of the community members for dynamic networks

Yang

Guo

et al. 2017

Physics Letters A

Self Cite

View full text Add to dashboard Cite

The collective behaviors of community members for dynamic social networks are significant for understanding evolution features of communities. In this Letter, we empirically investigate the evolution properties of the new community members for dynamic networks. Firstly, we separate data sets into different slices, and analyze the statistical properties of new members as well as communities they joined in for these data sets. Then we introduce a parameter ϕ to describe community evolution between different slices and investigate the dynamic community properties of the new community members. The empirical analyses for the Facebook, APS, Enron and Wiki data sets indicate that both the number of new members and joint communities increase, the ratio declines rapidly and then becomes stable over time, and most of the new members will join in the small size communities that is s ≤ 10. Furthermore, the proportion of new members in existed communities decreases firstly and then becomes stable and relatively small for these data sets. Our work may be helpful for deeply understanding the evolution properties of community members for social networks.

show abstract

Detecting community structure in complex networks via node similarity

Cited by 150 publications

References 36 publications

Link prediction in complex networks: A survey

Link prediction in complex networks: A survey

Link Prediction in Microblog Network Using Supervised Learning with Multiple Features

Evolution properties of the community members for dynamic networks

Contact Info

Product

Resources

About