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13 results for “Homophily”
Evaluating homophily of human PPI with respect to chromosomes
<p>Homophily/heterophily evaluation, expressed in terms of z-score values, is related to the human Protein-Protein Interaction Network (PPI), obtained from the STRING v11.5 database (<a href="https://string-db.org/">https://string-db.org</a>) setting standard threshold on edge score (T=700). Each protein occurring in the PPI was assigned to a class corresponding to the chromosome the related gene belongs to.</p> <p>A total of 23 classes (<em>chr1</em>, <em>chr2</em>, ..., <em>chr22</em>, <em>chrX</em>) were considered (excluding the class corresponding to chromosome Y because of the small number of genes occurring in the network).</p> <p>The homophily/heterophily nature of the network, with respect to chromosome classes, was evaluated through HONTO tool (<a href="https://github.com/cumbof/honto">https://github.com/cumbof/honto</a>).</p> <p>In other words, the tendency of proteins to preferentially interact with proteins whose genes are physically located on the same chromosome (homophily) or on different chromosomes (heterophily) was investigated and evaluated in terms of z-scores.</p> <p>Values related to intra (along the diagonal) and inter chromosomal interactions (other than the diagonal) are also reported as a heatmap.</p> <p>As one can observe, values occurring in the diagonal are clearly higher than values out of the diagonal, leading to assess a homophilic nature of the network, confirming the link between shared chromosome and interaction in the PPI.</p>
Homophily in Voting Behavior: Evidence from Preferential Voting
<p>This is a dataset for the paper <em>"Homophily in Voting Behavior: Evidence from Preferential Voting"</em> (co-authored by Lucie Coufalová and Michal Ševčík) that allows for the replication of all regression tables and descriptive statistics.</p> <p>There are four files in the dataset:</p> <ul> <li>"rdata.csv" is the main file with data used in regressions</li> <li>"homo_occupation.csv" is an auxiliary file with data used to calculate some descriptive statistics</li> <li>"municipalities_age.csv" is an auxiliary file with data used to calculate some descriptive statistics</li> <li>"replication_data_20220910.RData" is a file that contains all tables packed and compressed for use in R.</li> </ul> <p><strong> Tables</strong><br> rdata...main data table used in regressions<br> homo_occupation...auxiliary data table used in descriptive stats<br> municipality_ages...auxiliary data table used in descriptive stats</p> <p><strong>Variables</strong><br> <em> Outcome:</em><br> pref...number of preferential votes</p> <p><em> Variables of interest:</em><br> homo_municipality...indicator variable for a candidate running in the municipality of his/her residence<br> homo_(education/occupation/age/gender)...percentage of the population sharing the characteristic of the candidate</p> <p><em> Other variables:</em><br> KOD_OBEC...municipality ID in CISOB classification<br> total_fe...ID of candidate-election pair<br> maxPORCISLO...number of candidates on the ballot<br> cluster_ID...ID for error term clustering<br> POC_HLASU...total number of votes cast for the party in the given municipality<br> small_municipality...indicator variable for a municipality with a population below the median (defined separately <br> for each year and constituency)<br> year...election year (election ID)<br> VOLKRAJ...constituency ID<br> PORCISLO...position of the candidate on the ballot<br> VEK...age<br> agecat...age category<br> MANDAT...indicator variable for elected candidates<br> tertiary_educ...indicator variable for candidates with tertiary education<br> gender...male/female<br> ger_share...indicator variable for municipality being dominated by ethnic Germans in 1930</p> <p> </p>
Homophily in social and demographic traits predict association patterns in female western and mountain gorillas
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The "Last.fm" data set used in the article "Cumulative effects of triadic closure and homophily in social networks"
<p>This is the "Last.fm" network used in the article:</p> <p>A. Asikainen, G. Iñiguez, J. Ureña-Carrión, K. Kaski, M. Kivelä. Cumulative effects of triadic closure and homophily in social networks. Science Advances (in press)</p> <p>https://doi.org/10.1126/sciadv.aax7310</p> <p>The data set is described in the article. Please cite the original article when using this data set.</p> <p>The original data in which this network is based on was donwloaded from audioscrobbler.net where it was licensed under the "Creative Commons Attribution-NonCommercial-ShareAlike 2.0 UK: England & Wales" licese, and accordinly this data set uses the same license.</p> <p>The data contains two files:</p> <p><strong>lastfm.edg</strong><br> This is the network formatted as an edge list, where each row in the file is an edge connecting the two nodes indicated by the two numbers separated by a whitespace. Each node number corresponds to a single account in the website.</p> <p><strong>lastfm_genders.txt</strong><br> This is the list of genders of the nodes. Each row corresponds to one node. The first number is the node id (matching the one in the edge list) and the second number indicates the gender such that 0=male and 1=female.</p>
Adjacency matrices and nodal attributes for prestige and homophily predict network structure for social learning of medicinal plant knowledge
<p>Human subsistence societies have thrived in environmental extremes while maintaining biodiversity through social learning of ecological knowledge, such as techniques to prepare food and medicine from local resources. However, there is limited understanding of which processes shape social learning patterns and configuration in ecological knowledge networks, or how these processes apply to resource management and biological conservation. In this study, we test the hypothesis that the prestige (rarity or exclusivity) of knowledge shapes social learning networks. In addition, we test whether people tend to select who to learn from based on prestige (knowledge or reputation), and homophily (e.g., people of the same age or gender). We used interviews to assess five types of medicinal plant knowledge and how 303 people share this knowledge across four villages in Solomon Islands. We developed exponential random graph models (ERGMs) to test whether hypothesized patterns of knowledge sharing based on prestige and homophily are more common in the observed network than in randomly simulated networks of the same size. We found that prestige predicts five hypothesized network configurations and all three hypothesized learning patterns, while homophily predicts one of three hypothesized network configurations and five of the seven hypothesized learning patterns. These results compare the strength of different prestige and homophily effects on social learning and show how cultural practices such as intermarriage can affect certain aspects of prestige and homophily. By advancing our understanding of how prestige and homophily affect ecological knowledge networks, we identify which social learning patterns have the largest effects on biocultural conservation of ecological knowledge.</p>
Figure 1 in Male homophily in South American herpetology: one of the major processes underlying the gender gap in publications
Figure 1. Co-authorship networks of two South American herpetological journals, considering multiple-authored articles published. Each point represents an author colored by gender, and each line represents a co-authorship connection in a publication. The enlightened areas are the most connected clusters in each network. Pie-charts show the distribution of connections by gender. Barplots show the gender distribution of the first 40 higher betweenness scores. (A-C) Cuadernos de Herpetología's network of papers published between 1985 and 2019. (D-F) South American Journal of Herpetology's network of papers published between 2006 and 2019. Abbreviations: MM = man–man connections; WM = woman–man connections; WW = woman–woman connections.
Figure 2 in Male homophily in South American herpetology: one of the major processes underlying the gender gap in publications
Figure 2. Bibliometric data of nine herpetological journals. (A-C) Gender ratio in papers published during 2018 and 2019, considering different taxonomic groups and sub-areas. Multi authored papers considering the journal's qualies: (D) gender composition within publishing groups, and (E-J) gender participation in protagonic roles and its effect on the proportion
Figure 2 in Male homophily in South American herpetology: one of the major processes underlying the gender gap in publications
Figure 2. Bibliometric data of nine herpetological journals. (A-C) Gender ratio in papers published during 2018 and 2019, considering different taxonomic groups and sub-areas. Multi authored papers considering the journal's qualies: (D) gender composition within publishing groups, and (E-J) gender participation in protagonic roles and its effect on the proportion of female co-authors. Single authored papers: (K) gender distribution on average and discriminated by journal's qualies. Editorial Boards by journals in 2019: (L) gender proportion within each journal, and (M) percentage of similarity between members of different journals. Abbreviations: ACTH = Acta Herpetologica; AFJH = African Journal of Herpetology; BG = Biogeography; CHER = Cuadernos de Herpetología; CM = Communication; CS = Conservation; EC = Ecology; ET = Ethology; HBUL = Herpetological Bulletin; HEGI = Herpetologica; HEMO = Herpetological Monographs; HREV = Herpetological Review; MO = Morphology; PHYL = Phyllomedusa; SAJH = South American Journal of Herpetology; SI = Systematics; Q1 = 1st quartile; Q2 = 2nd quartile; Q3 = 3rd quartile; Q4 = 4 Downloaded th quartilefrom
Data from: Homophily around specialized foraging underlies dolphin social preferences
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Adjacency matrices and nodal attributes for prestige and homophily predict network structure for social learning of medicinal plant knowledge
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Data from: Can longitudinal generalized estimating equation models distinguish network influence and homophily? an agent-based modeling approach to measurement characteristics
Background: Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model. Methods: We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of homophily and network influence. Results: In cohorts with both static and dynamic networks, we find that the GEE models have excellent sensitivity and reasonable specificity for determining the presence or absence of network influence, but little ability to distinguish whether or not homophily is present. Conclusions: The GEE models are a valuable tool to examine for the presence of network influence in longitudinal data, but are quite limited with respect to homophily.
Data from: Can longitudinal generalized estimating equation models distinguish network influence and homophily? an agent-based modeling approach to measurement characteristics
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Pluralistic Homophily
<p>This ZIP file contains the datasets and results associated with the research on Pluralistic Homophily. The included data and results can be processed with code available at this <a href="https://github.com/fernandobarraza/pluralisticHomophily" target="_blank" rel="noopener">Github repository</a>.</p> <p><strong>File Contents:</strong></p> <p>This ZIP includes datasets from 9 networks that were the primary sources for the research:</p> <p> - StackOverflow<br> - DBLP<br> - Amazon<br> - LiveJournal<br> - YouTube<br> - Orkut<br> - Protein-Protein Interaction<br> - Drug-Drug Interaction<br> - C. elegans</p> <p>Additionally, an example dataset based on the definition of a bee is included.<br> <br>Each dataset consists of:</p> <ul> <li>A network file in <strong>NCol</strong> format.</li> <li>Files containing the ground-truth communities and those detected using the HLC algorithm (Anh et al.).</li> <li>The NCol files were generated from datasets available at <a href="https://snap.stanford.edu/index.html" target="_blank" rel="noopener">SNAP</a> and <a href="https://snap.stanford.edu/biodata/index.html" target="_blank" rel="noopener">BioSnap</a> and were pre-processed for use in the experiments.</li> </ul> <p>Results Files: The ZIP file also includes the files with the results of the research:</p> <p>- Files in <strong>CSV</strong> format containing the calculations of pluralistic homophily at both the network and local levels for each of the mentioned networks. </p> <p>- These results include all metrics associated with the research and allow for the replication of the figures and tables presented in the paper.</p>
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