Resampling Effects on Significance Analysis of Network Clustering and Ranking

Mirshahvalad, Atieh; Beauchesne, Olivier H.; Archambault, Éric; Rosvall, Martin

doi:10.1371/journal.pone.0053943

Cited by 9 publications

(9 citation statements)

References 34 publications

(33 reference statements)

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“…The promising results obtained for Infomap are in line with earlier findings reported in the network science literature [60]. Although Infomap has been introduced in the bibliometric literature [61] and has been applied to citation networks in a number of studies [19,20,62,63], the method has not yet gained a widespread popularity in the bibliometric community, where researchers seem to prefer the use of modularity-based methods. Our findings suggest that the bibliometric community could benefit from exploring the use of other clustering methods in addition to modularity-based methods.…”

Section: /24supporting

confidence: 83%

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods

2016

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Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.

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Section: /24supporting

confidence: 83%

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods

2016

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“…Radicchi et al (2011) introduced a GloSS filtering technique preserving both the weight distribution and network topology. Recently a comparison of several (re)-sampling methods was given (Mirshahvalad et al, 2012; Wang, 2012). Guimerà & Sales-Pardo (2009) provided a method to detect missing interactions (false negatives) and spurious interactions (false positives).…”

Section: An Inventory Of Network Analysis Tools Helping Drug Designmentioning

confidence: 99%

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

809

661

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Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

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“…Reichardt and Bornholdt (2006) computed the z-values of modularity after estimating its empirical distribution through multiple random network realizations. Mirshahvalad et al (2013) studied how different resampling schemes influence significance analysis.…”

Section: Introductionmentioning

confidence: 99%

To cut or not to cut? Assessing the modular structure of brain networks

2014

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A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks.

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Resampling Effects on Significance Analysis of Network Clustering and Ranking

Cited by 9 publications

References 34 publications

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

To cut or not to cut? Assessing the modular structure of brain networks

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