Eb&D: A new clustering approach for signed social networks based on both edge-betweenness centrality and density of subgraphs

2017 ◽  
Vol 482 ◽  
pp. 147-157 ◽  
Author(s):  
Xingqin Qi ◽  
Huimin Song ◽  
Jianliang Wu ◽  
Edgar Fuller ◽  
Rong Luo ◽  
...  
Keyword(s):  
Social Networks ◽  
Betweenness Centrality ◽  
Edge Betweenness ◽  
Signed Social Networks ◽  
Clustering Approach

scholarly journals BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks

2021 ◽  
Vol 27 (7) ◽  
pp. 667-692
Author(s):  
Lamia Berkani ◽  
Lylia Betit ◽  
Louiza Belarif
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Recommendation Algorithms ◽  
Clustering Approach ◽  
Real World Datasets ◽  
Multiview Clustering ◽  
Improving Accuracy

Clustering-based approaches have been demonstrated to be efficient and scalable to large-scale data sets. However, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we propose in this article an optimized multiview clustering approach for the recommendation of items in social networks. First, the selection of the initial medoids is optimized using the Bees Swarm optimization algorithm (BSO) in order to generate better partitions (i.e. refining the quality of medoids according to the objective function). Then, the multiview clustering (MV) is applied, where users are iteratively clustered from the views of both rating patterns and social information (i.e. friendships and trust). Finally, a framework is proposed for testing the different alternatives, namely: (1) the standard recommendation algorithms; (2) the clustering-based and the optimized clustering-based recommendation algorithms using BSO; and (3) the MV and the optimized MV (BSO-MV) algorithms. Experimental results conducted on two real-world datasets demonstrate the effectiveness of the proposed BSO-MV algorithm in terms of improving accuracy, as it outperforms the existing related approaches and baselines.

scholarly journals Prediction of drug-protein interaction and drug repositioning using machine learning model

2020 ◽  
Author(s):  
Yu-Ting Lin ◽  
Sheh-Yi Sheu ◽  
Chen-Ching Lin
Keyword(s):  
Machine Learning ◽  
Betweenness Centrality ◽  
Drug Repositioning ◽  
Protein Network ◽  
Training Data ◽  
Support Vector ◽  
Binding Interaction ◽  
Binding Strength ◽  
Edge Betweenness ◽  
Protein Models

AbstractBackgroundTraditional drug development is time-consuming and expensive, while computer-aided drug repositioning can improve efficiency and productivity. In this study, we proposed a machine learning pipeline to predict the binding interaction between proteins and marketed or studied drugs. We then extended the predicted interactions to construct a protein network that could be applied to discover the potentially shared drugs between proteins and thus predict drug repositioning.MethodsBinding information between proteins and drugs from the Binding Database and the physicochemical properties of drugs from the ChEMBL database were used to build the machine learning models, i.e. support vector regression. We further measured proportionalities between proteins by the predicted binding affinity and introduced edge betweenness centrality to construct a protein similarity network for drug repositioning.ResultsAs the proof of concept, we demonstrated our machine learning approach is capable of reflecting the binding strength between drugs and the target protein. When comparing coefficients of protein models, we found proteins SYUA and TAU that may share common ligand which were not in our training data. Using the edge betweenness centrality network based on the prediction proportionality of protein models, we found a potential target, AK1C2, of aspirin and of which the binding interaction had been validated.ConclusionsOur study could not only be applied to drug repositioning by comparing protein models or searching the protein-protein network, but also to predict the binding strength once the sufficient experimental data was provided to train the protein models.

Ranking Nodes in Signed Social Networks

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Moshen Shahriari ◽  
Mahdi Jalili
Keyword(s):  
Social Networks ◽  
Signed Social Networks
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