Computational Social Networks最新文献

{"title":"Clustering 1-dimensional periodic network using betweenness centrality.","authors":"Norie Fu, Vorapong Suppakitpaisarn","doi":"10.1186/s40649-016-0031-1","DOIUrl":"10.1186/s40649-016-0031-1","url":null,"abstract":"<p><strong>Background: </strong>While the temporal networks have a wide range of applications such as opportunistic communication, there are not many clustering algorithms specifically proposed for them.</p><p><strong>Methods: </strong>Based on betweenness centrality for periodic graphs, we give a clustering pseudo-polynomial time algorithm for temporal networks, in which the transit value is always positive and the least common multiple of all transit values is bounded.</p><p><strong>Results: </strong>Our experimental results show that the centrality of networks with 125 nodes and 455 edges can be efficiently computed in 3.2 s. Not only the clustering results using the infinite betweenness centrality for this kind of networks are better, but also the nodes with biggest influences are more precisely detected when the betweenness centrality is computed over the periodic graph.</p><p><strong>Conclusion: </strong>The algorithm provides a better result for temporal social networks with an acceptable running time.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5749596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35755800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Why continuous discussion can promote the consensus of opinions?","authors":"Zhenpeng Li,&nbsp;Xijin Tang,&nbsp;Benhui Chen,&nbsp;Jian Yang,&nbsp;Peng Su","doi":"10.1186/s40649-016-0035-x","DOIUrl":"https://doi.org/10.1186/s40649-016-0035-x","url":null,"abstract":"<p><p>Why group opinions tend to be converged through continued communication, discussion and interactions? Under the framework of the social influence network model, we rigorously prove that the group consensus is almost surely within finite steps. This is a quite certain result, and reflects the real-world common phenomenon. In addition, we give a convergence time lower bound. Although our explanations are purely based on mathematic deduction, it shows that the latent social influence structure is the key factor for the persistence of disagreement and formation of opinions convergence or consensus in the real world social system.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-016-0035-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35755005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hashtag recommendation system for twitter data streams.","authors":"Eriko Otsuka,&nbsp;Scott A Wallace,&nbsp;David Chiu","doi":"10.1186/s40649-016-0028-9","DOIUrl":"https://doi.org/10.1186/s40649-016-0028-9","url":null,"abstract":"<p><strong>Background: </strong>Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world to post what is happening now. A hashtag, a keyword prefixed with a hash symbol (#), is a feature in Twitter to organize tweets and facilitate effective search among a massive volume of data. In this paper, we propose an automatic hashtag recommendation system that helps users find new hashtags related to their interests on-demand.</p><p><strong>Methods: </strong>For hashtag ranking, we propose the Hashtag Frequency-Inverse Hashtag Ubiquity (HF-IHU) ranking scheme, which is a variation of the well-known TF-IDF, that considers hashtag relevancy, as well as data sparseness which is one of the key challenges in analyzing microblog data. Our system is built on top of Hadoop, a leading platform for distributed computing, to provide scalable performance using Map-Reduce. Experiments on a large Twitter data set demonstrate that our method successfully yields relevant hashtags for user's interest and that recommendations are more stable and reliable than ranking tags based on tweet content similarity.</p><p><strong>Results and conclusions: </strong>Our results show that HF-IHU can achieve over 30 % hashtag recall when asked to identify the top 10 relevant hashtags for a particular tweet. Furthermore, our method out-performs kNN, k-popularity, and Naïve Bayes by 69, 54, and 17 %, respectively, on recall of the top 200 hashtags.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-016-0028-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35755805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A game theory-based trust measurement model for social networks.","authors":"Yingjie Wang,&nbsp;Zhipeng Cai,&nbsp;Guisheng Yin,&nbsp;Yang Gao,&nbsp;Xiangrong Tong,&nbsp;Qilong Han","doi":"10.1186/s40649-016-0027-x","DOIUrl":"https://doi.org/10.1186/s40649-016-0027-x","url":null,"abstract":"<p><strong>Background: </strong>In social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust is complicated and application dependent. Modeling trust needs to consider interaction history, recommendation, user behaviors and so on. Therefore, modeling trust is an important focus for online social networks.</p><p><strong>Methods: </strong>We propose a game theory-based trust measurement model for social networks. The trust degree is calculated from three aspects, service reliability, feedback effectiveness, recommendation credibility, to get more accurate result. In addition, to alleviate the free-riding problem, we propose a game theory-based punishment mechanism for specific trust and global trust, respectively.</p><p><strong>Results and conclusions: </strong>We prove that the proposed trust measurement model is effective. The free-riding problem can be resolved effectively through adding the proposed punishment mechanism.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-016-0027-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35755727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization problems in correlated networks.","authors":"Song Yang,&nbsp;Stojan Trajanovski,&nbsp;Fernando A Kuipers","doi":"10.1186/s40649-016-0026-y","DOIUrl":"https://doi.org/10.1186/s40649-016-0026-y","url":null,"abstract":"<p><strong>Background: </strong>Solving the shortest path and min-cut problems are key in achieving high-performance and robust communication networks. Those problems have often been studied in deterministic and uncorrelated networks both in their original formulations as well as in several constrained variants. However, in real-world networks, link weights (e.g., delay, bandwidth, failure probability) are often correlated due to spatial or temporal reasons, and these correlated link weights together behave in a different manner and are not always additive, as commonly assumed.</p><p><strong>Methods: </strong>In this paper, we first propose two correlated link weight models, namely (1) the deterministic correlated model and (2) the (log-concave) stochastic correlated model. Subsequently, we study the shortest path problem and the min-cut problem under these two correlated models.</p><p><strong>Results and conclusions: </strong>We prove that these two problems are NP-hard under the deterministic correlated model, and even cannot be approximated to arbitrary degree in polynomial time. However, these two problems are solvable in polynomial time under the (constrained) nodal deterministic correlated model, and can be solved by convex optimization under the (log-concave) stochastic correlated model.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-016-0026-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35754747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of strong attractors in social media networks.","authors":"Ziyaad Qasem,&nbsp;Marc Jansen,&nbsp;Tobias Hecking,&nbsp;H Ulrich Hoppe","doi":"10.1186/s40649-016-0036-9","DOIUrl":"https://doi.org/10.1186/s40649-016-0036-9","url":null,"abstract":"<p><strong>Background: </strong>Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing.</p><p><strong>Methods: </strong>The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time.</p><p><strong>Results: </strong>We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs.</p><p><strong>Conclusions: </strong>Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-016-0036-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35754952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factorization threshold models for scale-free networks generation.","authors":"Akmal Artikov,&nbsp;Aleksandr Dorodnykh,&nbsp;Yana Kashinskaya,&nbsp;Egor Samosvat","doi":"10.1186/s40649-016-0029-8","DOIUrl":"https://doi.org/10.1186/s40649-016-0029-8","url":null,"abstract":"<p><strong>Background: </strong>Several models for producing scale-free networks have been suggested; most of them are based on the preferential attachment approach. In this article, we suggest a new approach for generating scale-free networks with an alternative source of the power-law degree distribution.</p><p><strong>Methods: </strong>The model derives from matrix factorization methods and geographical threshold models that were recently proven to show good results in generating scale-free networks. We associate each node with a vector having latent features distributed over a unit sphere and with a weight variable sampled from a Pareto distribution. We join two nodes by an edge if they are spatially close and/or have large weights.</p><p><strong>Results and conclusion: </strong>The network produced by this approach is scale free and has a power-law degree distribution with an exponent of 2. In addition, we propose an extension of the model that allows us to generate directed networks with tunable power-law exponents.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-016-0029-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35755729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time topic-aware influence maximization using preprocessing.","authors":"Wei Chen,&nbsp;Tian Lin,&nbsp;Cheng Yang","doi":"10.1186/s40649-016-0033-z","DOIUrl":"https://doi.org/10.1186/s40649-016-0033-z","url":null,"abstract":"<p><strong>Background: </strong>Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics.</p><p><strong>Methods: </strong>In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods to avoid redoing influence maximization for each mixture from scratch.</p><p><strong>Results: </strong>We explore two preprocessing algorithms with theoretical justifications.</p><p><strong>Conclusions: </strong>Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-016-0033-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35754956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient method for link prediction in weighted multiplex networks.","authors":"Shikhar Sharma,&nbsp;Anurag Singh","doi":"10.1186/s40649-016-0034-y","DOIUrl":"https://doi.org/10.1186/s40649-016-0034-y","url":null,"abstract":"<p><strong>Background: </strong>A great variety of artificial and natural systems can be abstracted into a set of entities interacting with each other. Such abstractions can very well represent the underlying dynamics of the system when modeled as the network of vertices coupled by edges. Prediction of dynamics in these structures based on topological attribute or dependency relations is an important task. Link Prediction in such complex networks is regarded useful in almost all types of networks as it can be used to extract missing information, identify spurious interactions, and evaluate network evolving mechanisms. Various similarity and likelihood-based indices have been employed to infer different topological and relation-based information to form a link prediction algorithm. These algorithms, however, are too specific to the domain and do not encapsulate the generic nature of the real-world information. In most natural and engineered systems, the entities are linked with multiple types of associations and relations which play a factor in the dynamics of the network. It forms multiple subsystems or multiple layers of networked information. These networks are regarded as Multiplex Networks.</p><p><strong>Methods: </strong>This work presents an approach for link prediction in Multiplex networks where the associations are learned from the multiple layers of networks for link prediction purposes. Most of the real-world networks are represented as weighted networks. Weight prediction coupled with Link Prediction can be of great use. Link scores are received using various similarity measures and used to predict weights. This work further proposes and testifies a strategy for weight prediction.</p><p><strong>Results and conclusions: </strong>This work successfully proposes an algorithm for Weight Prediction using Link similarity measures on multiplex networks. The predicted weights show very less deviation from their actual weights. In comparison to other indices, the proposed method has a far low error rate and outperforms them concerning the metric performance NRMSE.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-016-0034-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35755561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Text normalization for named entity recognition in Vietnamese tweets.","authors":"Vu H Nguyen,&nbsp;Hien T Nguyen,&nbsp;Vaclav Snasel","doi":"10.1186/s40649-016-0032-0","DOIUrl":"https://doi.org/10.1186/s40649-016-0032-0","url":null,"abstract":"<p><strong>Background: </strong>Named entity recognition (NER) is a task of detecting named entities in documents and categorizing them to predefined classes, such as person, location, and organization. This paper focuses on tweets posted on Twitter. Since tweets are noisy, irregular, brief, and include acronyms and spelling errors, NER in those tweets is a challenging task. Many approaches have been proposed to deal with this problem in tweets written in English, Germany, Chinese, etc., but none for Vietnamese tweets.</p><p><strong>Methods: </strong>We propose a method that normalizes a tweet before taking as an input of a learning model for NER in Vietnamese tweets. The normalization step detects spelling errors in a tweet and corrects them using an improved Dice's coefficient or n-grams. A Support Vector Machine learning algorithm is employed to learn a classifier using six different types of features.</p><p><strong>Results and conclusion: </strong>We train our method on a training set consisting of more than 40,000 named entities and evaluate it on a testing set consisting of 3,186 named entities. The experimental results showed that our system achieves state-of-the-art performance with F1 score of 82.13%.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-016-0032-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35754953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}