IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T21:07:33ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2017-08-04T11:01:58Z2017-08-04T11:01:58Zhttp://eprints.imtlucca.it/id/eprint/3754This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/37542017-08-04T11:01:58ZModel-based evaluation of scientific impact indicatorsUsing bibliometric data artificially generated through a model of citation dynamics calibrated on empirical data, we compare several indicators for the scientific impact of individual researchers. The use of such a controlled setup has the advantage of avoiding the biases present in real databases, and it allows us to assess which aspects of the model dynamics and which traits of individual researchers a particular indicator actually reflects. We find that the simple average citation count of the authored papers performs well in capturing the intrinsic scientific ability of researchers, regardless of the length of their career. On the other hand, when productivity complements ability in the evaluation process, the notorious h and g indices reveal their potential, yet their normalized variants do not always yield a fair comparison between researchers at different career stages. Notably, the use of logarithmic units for citation counts allows us to build simple indicators with performance equal to that of h and g. Our analysis may provide useful hints for a proper use of bibliometric indicators. Additionally, our framework can be extended by including other aspects of the scientific production process and citation dynamics, with the potential to become a standard tool for the assessment of impact metrics.Matúš MedoGiulio Ciminigiulio.cimini@imtlucca.it2015-11-06T12:41:43Z2015-11-06T12:41:43Zhttp://eprints.imtlucca.it/id/eprint/2849This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28492015-11-06T12:41:43ZNetwork-Driven Reputation in Online Scientific CommunitiesThe ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here an algorithm which simultaneously computes reputation of users and fitness of papers in a bipartite network representing an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method's best-performing variants. We show that when the input data is extended to a multilayer network including users, papers and authors and the algorithm is correspondingly modified, the resulting performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher <italic>h</italic>-index than top papers and top authors chosen by other algorithms. We finally show that our algorithm is robust against persistent authors (spammers) which makes the method readily applicable to the existing online scientific communities.Hao LiaoRui XiaoGiulio Ciminigiulio.cimini@imtlucca.itMatúš Medo2015-11-06T12:10:19Z2015-11-06T12:10:19Zhttp://eprints.imtlucca.it/id/eprint/2846This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28462015-11-06T12:10:19ZThe Role of Taste Affinity in Agent-Based Models for Social RecommendationIn the Internet era, online social media emerged as the main tool for sharing opinions and information among individuals. In this work, we study an adaptive model of a social network where directed links connect users with similar tastes, and over which information propagates through social recommendation. Agent-based simulations of two different artificial settings for modeling user tastes are compared with patterns seen in real data, suggesting that users differing in their scope of interests is a more realistic assumption than users differing only in their particular interests. We further introduce an extensive set of similarity metrics based on users' past assessments, and evaluate their use in the given social recommendation model with both artificial simulations and real data. Superior recommendation performance is observed for similarity metrics that give preference to users with small scope — who thus act as selective filters in social recommendation.Giulio Ciminigiulio.cimini@imtlucca.itAn ZengMatúš MedoDuanbing Chen2015-11-06T11:17:03Z2015-11-06T11:17:03Zhttp://eprints.imtlucca.it/id/eprint/2845This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28452015-11-06T11:17:03ZMeasuring Quality, Reputation and Trust in Online CommunitiesIn the Internet era the information overload and the challenge to detect quality content has raised the issue of how to rank both resources and users in online communities. In this paper we develop a general ranking method that can simultaneously evaluate users’ reputation and objects’ quality in an iterative procedure, and that exploits the trust relationships and social acquaintances of users as an additional source of information. We test our method on two real online communities, the EconoPhysics forum and the Last.fm music catalogue, and determine how different variants of the algorithm influence the resultant ranking. We show the benefits of considering trust relationships, and define the form of the algorithm better apt to common situations.Hao LiaoGiulio Ciminigiulio.cimini@imtlucca.itMatúš Medo2015-11-06T11:09:48Z2015-11-06T11:09:48Zhttp://eprints.imtlucca.it/id/eprint/2843This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28432015-11-06T11:09:48ZEnhancing topology adaptation in information-sharing social networksThe advent of the Internet and World Wide Web has led to unprecedent growth of the information available. People usually face the information overload by following a limited number of sources which best fit their interests. It has thus become important to address issues like who gets followed and how to allow people to discover new and better information sources. In this paper we conduct an empirical analysis of different online social networking sites and draw inspiration from its results to present different source selection strategies in an adaptive model for social recommendation. We show that local search rules which enhance the typical topological features of real social communities give rise to network configurations that are globally optimal. These rules create networks which are effective in information diffusion and resemble structures resulting from real social systems.Giulio Ciminigiulio.cimini@imtlucca.itDuanbing ChenMatúš MedoLinyuan LüYi-Cheng ZhangTao Zhou2015-11-06T11:07:28Z2015-11-06T11:07:28Zhttp://eprints.imtlucca.it/id/eprint/2842This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28422015-11-06T11:07:28ZTemporal Effects in the Growth of NetworksWe show that to explain the growth of the citation network by preferential attachment (PA), one has to accept that individual nodes exhibit heterogeneous fitness values that decay with time. While previous PA-based models assumed either heterogeneity or decay in isolation, we propose a simple analytically treatable model that combines these two factors. Depending on the input assumptions, the resulting degree distribution shows an exponential, log-normal or power-law decay, which makes the model an apt candidate for modeling a wide range of real systems.Matúš MedoGiulio Ciminigiulio.cimini@imtlucca.itStanislao Gualdi2015-11-06T11:04:00Z2015-11-06T11:04:00Zhttp://eprints.imtlucca.it/id/eprint/2841This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28412015-11-06T11:04:00ZEmergence of Scale-Free Leadership Structure in Social Recommender Systems<p>The study of the organization of social networks is important for the understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a “good get richer” mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems.</p>Tao ZhouMatúš MedoGiulio Ciminigiulio.cimini@imtlucca.itZi-Ke ZhangYi-Cheng Zhang2015-11-06T10:56:49Z2016-04-06T10:36:53Zhttp://eprints.imtlucca.it/id/eprint/2839This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28392015-11-06T10:56:49ZHeterogeneity, quality, and reputation in an adaptive recommendation modelRecommender systems help people cope with the problem of information overload. A recently proposed adaptive news recommender model [M. Medo, Y.-C. Zhang, T. Zhou, Europhys. Lett. 88, 38005 (2009)] is based on epidemic-like spreading of news in a social network. By means of agent-based simulations we study a “good get richer” feature of the model and determine which attributes are necessary for a user to play a leading role in the network. We further investigate the filtering efficiency of the model as well as its robustness against malicious and spamming behaviour. We show that incorporating user reputation in the recommendation process can substantially improve the outcome.Giulio Ciminigiulio.cimini@imtlucca.itMatúš MedoTao ZhouDong WeiYi-Cheng Zhang