Generating scale-free networks with adjustable clustering coefficient via random walks

Herrera, Carlos; Zufiria, Pedro J.

doi:10.1109/nsw.2011.6004642

Cited by 29 publications

(20 citation statements)

References 41 publications

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“…To do this, we first created a scale-free network (Supplementary Figure 4) using the algorithm of Herrera and Zufiria [18], and verified that the degree of connectivity follows the power law as expected in such a network ( P ( k ) ~ k −2.03 ). Next we allocated 150 differentially expressed proteins in local subnetworks (see Supplementary Information) so that these proteins are network neighbors with one another.…”

Section: Methodsmentioning

confidence: 99%

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry

Teo

Kim

Tsou

et al. 2015

Journal of Proteomics

144

163

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Data independent acquisition (DIA) mass spectrometry is an emerging technique that offers more complete detection and quantification of peptides and proteins across multiple samples. DIA allows fragment-level quantification, which can be considered as repeated measurements of the abundance of the corresponding peptides and proteins in the downstream statistical analysis. However, few statistical approaches are available for aggregating these complex fragment-level data into peptide- or protein-level statistical summaries. In this work, we describe a software package, mapDIA, for statistical analysis of differential protein expression using DIA fragment-level intensities. The workflow consists of three major steps: intensity normalization, peptide/fragment selection, and statistical analysis. First, mapDIA offers normalization of fragment-level intensities by total intensity sums as well as a novel alternative normalization by local intensity sums in retention time space. Second, mapDIA removes outlier observations and selects peptides/fragments that preserve the major quantitative patterns across all samples for each protein. Last, using the selected fragments and peptides, mapDIA performs model-based statistical significance analysis of protein-level differential expression between specified groups of samples. Using a comprehensive set of simulation datasets, we show that mapDIA detects differentially expressed proteins with accurate control of the false discovery rates. We also describe the analysis procedure in detail using two recently published DIA datasets generated for 14-3-3β dynamic interaction network and prostate cancer glycoproteome.

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Section: Methodsmentioning

confidence: 99%

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry

Teo

Kim

Tsou

et al. 2015

Journal of Proteomics

144

163

View full text Add to dashboard Cite

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“…First, the traditional BA model does not produce the requisite levels of transitivity present in observed social networks (Ravasz and Barabási, 2003;Varga, 2015). Second, while M a n u s c r i p t modified versions of the BA model exist to try and make this property an adjustable parameter (Jin et al, 2001;Varga, 2015), as well as novel procedures to produce scale-free networks with tunable transitivity (Chakrabarti et al, 2017;Herrera and Zufiria, 2011), no method yet exists that accounts for the distribution of connection density in a way that mimics social networks, and/or allows for manipulation of that density measure beyond a narrow range. A key issue is that in large, scale-free human social networks, the local transitivity of an agent is inversely proportional to its number of connections, i.e.…”

Section: Network Architecture and Communicationmentioning

confidence: 99%

Social Network Limits Language Complexity

Lou-Magnuson¹,

Onnis²

2017

Preprint

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Natural languages vary widely in the degree to which they make use of nested compositional structure in their grammars. It has long been noted by linguists that the languages historically spoken in small communities develop much deeper levels of compositional embedding than those spoken by larger groups. Recently this observation has been confirmed by a robust statistical analysis of the World Atlas of Language Structures. In order to examine this connection mechanistically, we propose an agent-based model that accounts for key cultural evolutionary features of language transfer and language change. We identify transitivity as a physical parameter of social networks critical for the evolution of compositional structure, and the hierarchical patterning of scale-free distributions as inhibitory.Natural languages vary widely in grammatical structure, especially in the degree to which they make use of nested composition. A graduated divide in this typological space is the extent to which words themselves make use of compositional patterning. That is, the degree on average to which words possess internal structural hierarchies (morphological composition), beyond their arrangement in the phrasal hierarchy of a sentence (syntactic composition). In some languages, such as Chinese, morphological structure is virtually nonexistent, whereas others, like Lushootseed (an indigenous, Salishan language of North America), have so much morphological structure that entire utterances can be a single, highly complex word. It is not yet understood how such typological patterns emerge historically, and why natural languages vary in this property over time.Computational modeling researchers have proposed both symbolic (Smith et al., 2003) and neural architectures (Batali, 1998) to demonstrate that compositional language structure develops where cultural evolutionary forces are at * Equally contributing authors.

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“…We envision this process as a post processing step where the graph is iteratively rewired until the desired values are achieved, in a hill climbing fashion. For similar techniques on this topic please refer to [8] and [20].…”

Section: Data Generationmentioning

confidence: 99%

Graphalytics

Capotă

Hegeman

Iosup

et al. 2015

Proceedings of the GRADES'15

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Graphs are increasingly used in industry, governance, and science. This has stimulated the appearance of many and diverse graph-processing platforms. Although platform diversity is beneficial, it also makes it very challenging to select the best platform for an application domain or one of its important applications, and to design new and tune existing platforms. Continuing a long tradition of using benchmarking to address such challenges, in this work we present our vision for Graphalytics, a big data benchmark for graphprocessing platforms. We have already benchmarked with Graphalytics a variety of popular platforms, such as Giraph, GraphX, and Neo4j.

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Generating scale-free networks with adjustable clustering coefficient via random walks

Cited by 29 publications

References 41 publications

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry

Social Network Limits Language Complexity

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