Detecting hierarchical structure in networks

Herlau, Tue; Mørup, Morten; Schmidt, Mikkel N.; Hansen, Lars Kai

doi:10.1109/cip.2012.6232913

Cited by 15 publications

(21 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the scenario in which nodes 33 and 34 belong to the same community at level 1 (Figure 4(c)) (where the Q H value of the community with nodes 33 and 34 at level 1 is -0.02) is strange because no link exists between the two nodes. Consequently, the detected hierarchical community structure for Zachary's karate club network by the proposed approach provides more reasonable information than that detected by the approach in [17].…”

Section: Methods Levelmentioning

confidence: 96%

“…The Q H values are as follows: this work, 0.802; [17], 0.411; and [24], 0.752 (Table 1). The total Q H value (i.e., 0.802) and the Q H value at each level (i.e., 0.417 at level 1 and 0.384 at level 2) in this work are largest.…”

Section: Network For Zachary's Karate Clubmentioning

confidence: 99%

“…The detection result for Zachary's karate club (Figure 4(b)) by the proposed approach is compared with the detection result in [17] (Figure 4(c)) and the detection result in [24] (Figure 4(d)). The Q H values are as follows: this work, 0.802; [17], 0.411; and [24], 0.752 (Table 1).…”

Section: Network For Zachary's Karate Clubmentioning

confidence: 99%

“…In the drawing of the detected structure (Figure 4(b)), nodes of the same color belong to the same community at level 1; each community at level 2 is encircled by red dashes; and all the nodes are grouped together at level 3 but are not marked. [17]. (d) Drawing of the structure detected by [24].…”

Section: Network For Zachary's Karate Clubmentioning

confidence: 99%

See 3 more Smart Citations

An integer programming approach and visual analysis for detecting hierarchical community structures in social networks

Lin

Kang

Chen

2015

Information Sciences

View full text Add to dashboard Cite

Section: Methods Levelmentioning

confidence: 96%

Section: Network For Zachary's Karate Clubmentioning

confidence: 99%

Section: Network For Zachary's Karate Clubmentioning

confidence: 99%

Section: Network For Zachary's Karate Clubmentioning

confidence: 99%

See 2 more Smart Citations

An integer programming approach and visual analysis for detecting hierarchical community structures in social networks

Lin

Kang

Chen

2015

Information Sciences

View full text Add to dashboard Cite

“…The probability of the existence of a link is obtained by the conditional probability on the estimated parameters. Many variations of the probability relational model were proposed in various alternative forms such as a binary tree (Clauset et al 2008, Park et al 2010, a generative Bayesian model (Herlau et al 2012), and local fitness function (Lancichinetti et al 2009). …”

Section: Introductionmentioning

confidence: 99%

A Network Structural Approach to the Link Prediction Problem

Lee

Pham

Jeong

et al. 2015

INFORMS Journal on Computing

View full text Add to dashboard Cite

The link prediction problem is an emerging real-life social network problem in which data mining techniques have played a critical role. It arises in many practical applications such as recommender systems, information retrieval, and marketing analysis of social networks. We propose a new mathematical programming approach for predicting a future network using estimated node degree distribution identified from historical data. The link prediction problem is formulated as an integer programming problem that maximizes the sum of link scores (probabilities) with respect to the estimated node degree distribution. The performance of the proposed framework is tested on real-life social networks, and the computational results show that the proposed approach can improve the performance of previously published link prediction methods.

show abstract

Recent advances on mechanisms of network generation: Community, exchangeability, and scale‐free properties

Li,

Hou,

Wang

2024

WIREs Computational Stats

View full text Add to dashboard Cite

The mechanisms of network generation have undergone extensive analysis and found broad applications in various real‐world scenarios. Among the fruitful literature on network models, numerous studies seek to explore and interpret fundamental graph structure properties, including the clustering effect, exchangeability, and scale‐free properties. In this paper, we present a comprehensive review of the statistical modeling methods for the mechanisms of network generation. We specifically focus on three representative classes of models, namely the stochastic block models, the exchangeable network models, and the preferential attachment models. For each model type, our approach begins by reviewing existing methods and model setups, followed by an exploration of the core modeling principles behind them. We also summarize relevant statistical inference techniques and provide a unified understanding of theoretical analyses. Furthermore, we emphasize several challenges and open problems that could shed light on future research. We conclude this review with the identification of some possible directions for future study.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms Algorithms and Computational Methods > Networks and Security

show abstract

Detecting hierarchical structure in networks

Cited by 15 publications

References 27 publications

An integer programming approach and visual analysis for detecting hierarchical community structures in social networks

An integer programming approach and visual analysis for detecting hierarchical community structures in social networks

A Network Structural Approach to the Link Prediction Problem

Recent advances on mechanisms of network generation: Community, exchangeability, and scale‐free properties

Contact Info

Product

Resources

About