Visual analysis for evaluation of community detection algorithms

Linhares, Cláudio D. G.; Ponciano, Jean R.; Pereira, Fabíola S. F.; Rocha, Luis E. C.; Paiva, Jose Gustavo S.; Travençolo, Bruno Augusto Nassif

doi:10.1007/s11042-020-08700-4

Cited by 23 publications

(16 citation statements)

References 37 publications

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“…The computation of a modularity score of 0.312 supports the interpretation that community structures in the network were not very strong. The modularity score ranges between −1 and 1 with higher modularity scores indicating stronger community structures [52]. The graphical layout by the Fruchterman-Reingold algorithm also supports this interpretation since many actors of the different communities were placed close to each other and communities partially overlap in the centre of the graph.…”

Section: Actor Coalitions In the Policy Debatementioning

confidence: 62%

“…The method identifies communities within networks based on a modularity measure and a hierarchical approach [9]. Modularity measures the strength of community structures compared to a random network with identical sets of nodes and edges [52]. Communities are more similar subsets of nodes and may, therefore, represent actor coalitions as their similarity is determined based on their shared policy beliefs.…”

Section: Which Coalition Structures Make Policy Change More Likely?mentioning

confidence: 99%

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Transition towards sustainable pharmacy? The influence of public debates on policy responses to pharmaceutical contaminants in water

Schaub

Braunbeck

2020

Environ Sci Eur

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Background Despite clear-cut scientific evidence for pharmaceutical contaminants causing adverse effects in aquatic life, the regulatory response in Germany has been weak. In principle, there are different policy approaches to address pharmaceutical contaminants: German water protection policies mostly follows a control approach, complemented by end-of-pipe solutions in some German states. The approach leaves the activities of key target groups, such as the pharmaceutical industry, largely unaffected. A stakeholder consultation initiated in 2016 by the German Federal Ministry of the Environment did not lead to significant changes in regulation. Empirical research in political science has shown that analysing the public debate can be helpful in explaining policy responses and, in particular, policy change. This study follows this approach and investigates whether the German policy response to pharmaceutical contaminants can be explained by characteristics of the public debate on the issue. Results A discourse network analysis based on newspaper reporting in Germany was conducted between 2013 and 2017 to investigate the public debate on pharmaceutical contaminants. German newspapers actually paid considerable attention to the issue. In fact, the debate was not controversial, and participating organisations expressed similar views with regard to the risk of the contaminants, the causes of contamination and the approaches to be taken to mitigate the release of contaminants to the environment. The main narrative in the debate was supportive to the current policy approach applied in Germany. There were no concerted efforts by organisations such as environmental organisations or ecological parties to mobilise for an alternative policy approach. Conclusions The low level of polarisation in the policy subsystem and the absence of a strong narrative mobilising a major policy change may explain the persistence of the policy approach to pharmaceutical contaminants applied in Germany. A significant change to the current approach in the near future seems unlikely. Nevertheless, literature in political science shows that a polarised public debate and a strong pro-change actor coalition often preceded policy change. Actors with an interest in stricter regulation might want to reconsider their mobilisation strategies.

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Section: Actor Coalitions In the Policy Debatementioning

confidence: 62%

Section: Which Coalition Structures Make Policy Change More Likely?mentioning

confidence: 99%

Transition towards sustainable pharmacy? The influence of public debates on policy responses to pharmaceutical contaminants in water

Schaub

Braunbeck

2020

Environ Sci Eur

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“…Network community detection can be seen as a clustering task, highly used in data mining scenarios, but applied to complex networks (Guidotti and Coscia, 2017). In this way, traditional clustering methods, such as DBSCAN (Ester et al, 1996), can be adapted to the community detection task (Gialampoukidis et al, 2016;Linhares et al, 2020). Among the existent community detection algorithms, Louvain (Blondel et al, 2008) and Infomap (Rosvall and Bergstrom, 2008) represent two of the most recommended approaches due to their performances and low computational complexity (Fortunato and Hric, 2016).…”

Section: Temporal Network Visualisationmentioning

confidence: 99%

Visual Analysis of Contact Patterns in School Environments

Ponciano¹,

Linhares²,

Melo³

et al. 2020

Informatics in Education

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Information Visualisation strategies can be applied in a variety of domains. In the context of temporal networks, i.e., networks in which interactions between individuals occur throughout time, efforts have been conducted to develop visual approaches that allow finding interaction patterns, anomalies, and other behaviours not previously perceived in the data. This paper presents two case studies involving real-world education networks from a primary school and a high school. For this purpose, we used the Massive Sequence View (MSV) layout with the Community-based Node Ordering (CNO) method, two well established approaches for visual analysis of temporal networks. Our results show that the identified patterns involving students/students and students/ teachers represent important information to benefit and support decision making about school management and teaching strategies, especially those related to strategic group formation.

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“…Considering that the best node reordering algorithm according to the quantitative analysis may not represent the best visual user experience, we also performed a visual analysis. Figure 34 shows two days of the Hospital network using different node reordering strategies: 1Appearance; (2) RN ; (3) CNO (Infomap, RN, RN); (4) CNO (Louvain, RN, RN); (5) CNO (Infomap, RN, RN) Intra; (6) CNO (Louvain, RN, RN) Intra. The Appearance layout has the most visual clutter amongst all strategies.…”

Section: Small Dataset -Hospitalmentioning

confidence: 99%

“…The published book chapter [4] contains the visualization of dynamic processes, detailed in Chapter 5. In [5] we present a visualization method that allows the analysis of two community detection algorithms in four different networks, detailed in Chapter 3.…”

Section: Bibliographical Publicationsmentioning

confidence: 99%

Técnicas avançadas para análise visual de redes temporais

Linhares¹

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To my parents, Enivaldo and Elcione, and my brother, Hélder, by the love, incentive and unconditional support, because without them, nothing would be possible. To my beautiful lifemate, Sara Fernandes, that always give me strength and support in all my decisions. Her dedication inspires me and it is very important to the conclusion of this thesis. To my friends Jean Ponciano and Everton Lira that were a crucial part of all my academic courses, always being helpful and partners. To the professors Bruno Travençolo, José Gustavo and Luis Rocha, by the guidance, support, and trust. To all that were directly or indirectly a part of my formation, I appreciate it. To the research funding agency CAPES for the scholarship granted to me.

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Visual analysis for evaluation of community detection algorithms

Cited by 23 publications

References 37 publications

Transition towards sustainable pharmacy? The influence of public debates on policy responses to pharmaceutical contaminants in water

Transition towards sustainable pharmacy? The influence of public debates on policy responses to pharmaceutical contaminants in water

Visual Analysis of Contact Patterns in School Environments

Técnicas avançadas para análise visual de redes temporais

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