Centrality-based Approach Research Articles

CDCA: Community detection in RNA-seq data using centrality-based approach

CDCA: Community detection in RNA-seq data using centrality-based approach

Influence maximization in community-structured social networks: a centrality-based approach

Influence maximization in community-structured social networks: a centrality-based approach

XProtCAS: A Toolkit for Extracting Conserved Accessible Surfaces from Protein Structures.

The identification of protein surfaces required for interaction with other biomolecules broadens our understanding of protein function, their regulation by post-translational modification, and the deleterious effect of disease mutations. Protein interaction interfaces are often identifiable as patches of conserved residues on a protein's surface. However, finding conserved accessible surfaces on folded regions requires an understanding of the protein structure to discriminate between functional and structural constraints on residue conservation. With the emergence of deep learning methods for protein structure prediction, high-quality structural models are now available for any protein. In this study, we introduce tools to identify conserved surfaces on AlphaFold2 structural models. We define autonomous structural modules from the structural models and convert these modules to a graph encoding residue topology, accessibility, and conservation. Conserved surfaces are then extracted using a novel eigenvector centrality-based approach. We apply the tool to the human proteome identifying hundreds of uncharacterised yet highly conserved surfaces, many of which contain clinically significant mutations. The xProtCAS tool is available as open-source Python software and an interactive web server.

Identifying the influential nodes in complex social networks using centrality-based approach

Estimating the importance of nodes in complex social networks is significant for understanding the robustness and stability of a network such as preventing the spread of disease, rumors, or power grids from shutting down. Existing relevant literature presents diverse network centrality measures for quantifying a node’s importance, however, each of the centrality measures is based on a single criterion having its own limitations. This study introduces a novel centrality model that is based on a combination of existing centrality measures for ranking important nodes in complex social networks. The study employs the entropy weighting technique for assigning objective weights to each single criterion, and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is utilized for ranking the importance of a node in the network. For a comprehensive empirical analysis, four real-world social networks are utilized for evaluating the effectiveness of the proposed model against the existing methods. The results of the study confirm the superiority and effectiveness of the proposed model in ranking the importance of network nodes as compared to a single centrality criterion.

A new model for predicting the winner in tennis based on the eigenvector centrality

The use of statistical tools for predicting the winner in tennis matches has enjoyed an increase in popularity over the last two decades and, currently, a variety of methods are available. In particular, paired comparison approaches make use of latent ability estimates or rating calculations to determine the probability that a player will win a match. In this paper, we extend this latter class of models by using network indicators for the predictions. We propose a measure based on eigenvector centrality. Unlike what happens for the standard paired comparisons class (where the rates or latent abilities only change at time t for those players involved in the matches at time t), the use of a centrality measure allows the ratings of the whole set of players to vary every time there is a new match. The resulting ratings are then used as a covariate in a simple logit model. Evaluating the proposed approach with respect to some popular competing specifications, we find that the centrality-based approach largely and consistently outperforms all the alternative models considered in terms of the prediction accuracy. Finally, the proposed method also achieves positive betting results.

A graph centrality-based approach for candidate gene prediction for type 1 diabetes.

Type 1 diabetes mellitus (T1DM) or insulin-dependent diabetes is an autoimmune disease that may pose life-threatening situations to individuals. In most cases, cytotoxic T lymphocytes (CTLs) promotes killing of islets of Langerhans in the pancreas, which harbour insulin-producing beta cells. The trigger for autoimmune attack is still unclear; therefore, identifying and targeting candidate genes are imperative to hinder its deleterious effects. In the present study, we focused on identification of new candidate genes for T1DM. For our study, we exclusively selected immune-related genes as they play a crucial role in T1DM. We constructed and analysed a human immunome signalling network (directed network) to identify the new candidate genes through various graph centrality measures combining with Gene Ontology (GO). As a result, we identified 4 new candidate genes which may act as potential drug targets for T1DM. We further validated for their disease relevance through literature survey and pathway analysis and found that 3 out of 4 predicted genes mirrored their well-established roles as potential targets for T1DM.

Hindi EmotionNet

In this study, we create an emotion lexicon for the Hindi language called Hindi EmotionNet. It can assign emotional affinity to words in IndoWordNet. This lexicon contains 3,839 emotion words, with 1,246 positive and 2,399 negative words. We also introduce ambiguous (217 words) and neutral (95 words) emotions to Hindi. Positive emotion words covered nine types of positive emotions, negative emotion words covered eleven types of negative emotions, ambiguous emotion words covered seven types of ambiguous emotions, and neutral emotion words covered two neutral emotions. The proposed Hindi EmotionNet was then applied to opinion classification and emotion classification. We introduce a centrality-based approach for emotion classification that uses degree, closeness, betweenness, and page rank as centrality measures. We also created a dataset of Hindi based on screenplays, stories, and blogs in the language. We translated emotion data from SemEval 2017 into Hindi for further comparison. The proposed approach delivered promising results on opinion and emotion classification, with an accuracy of 85.78% for the former and 75.91% for the latter.

Degree and centrality-based approaches in network-based variable selection: Insights from the Singapore Longitudinal Aging Study.

We describe a network-based method to obtain a subset of representative variables from clinical data of subjects of the second Singapore Longitudinal Aging Study (SLAS-2), while preserving to a good extent the predictive performance of the full set with regards to a multi-faceted index of successful aging, SAGE. To examine differences in predictive performance of high-degree nodes (“hubs”) and high-centrality ones (“cores”), we implement four subsetting strategies (two degree-based, two centrality-based) and obtain four surrogate sets of variables, which we use as input features for machine learning models to predict the SAGE index of subjects. All four models have variables belonging to the physical, cardiovascular, cognitive and immunological domains among their fifteen most important predictors. A fifth domain (leisure-time activities, LTA) is also present in some form. From a comparison of the surrogate sets’ size and predictive performance, a centrality-based approach (selection of the most central variable-nodes within each cluster) yielded the smallest-sized surrogate set, while having high prediction accuracy (measured by its model’s area-under-curve, AUC) in comparison to its analogous degree-based strategy (selection of the highest-degree nodes per cluster). Inclusion of the next most-central variables yielded negligible changes in predictive performance while more than doubling the surrogate set size. The centrality-based approach thus yields a surrogate set which offers a good balance between number of variables and prediction performance, and can act as a representative subset of the SLAS-2 clinical dataset.

Source Estimation for Propagation Processes on Complex Networks With an Application to Delays in Public Transportation Systems

SummaryThe correct identification of the source of a propagation process is crucial in many research fields. As a specific application, we consider source estimation of delays in public transportation networks. We propose two approaches: an effective distance median and a backtracking method. The former is based on a structurally generic effective distance-based approach for the identification of infectious disease origins, and the latter is specifically designed for delay propagation. We examine the performance of both methods in simulation studies and in an application to the German railway system, and we compare the results with those of a centrality-based approach for source detection.