Implementation of the PaperRank and AuthorRank indices in the Scopus database

We implement the PaperRank and AuthorRank indices introduced in [Amodio & Brugnano, 2014] in the Scopus database, in order to highlight quantitative and qualitative information that the bare number of citations and/or the h-index of an author are unable to provide. In addition to this, the new indices can be cheaply updated in Scopus, since this has a cost comparable to that of updating the number of citations. Some examples are reported to provide insight in their potentialities, as well as possible extensions

TimeRank: A dynamic approach to rate scholars using citations

Rating has become a common practice of modern science. No rating system can be considered as final, but instead several approaches can be taken, which magnify different aspects of the fabric of science. We introduce an approach for rating scholars which uses citations in a dynamic fashion, allocating ratings by considering the relative position of two authors at the time of the citation among them. Our main goal is to introduce the notion of citation timing as a complement to the usual suspects of popularity and prestige. We aim to produce a rating able to account for a variety of interesting phenomena, such as positioning raising stars on a more even footing with established researchers. We apply our method on the bibliometrics community using data from the Web of Science from 2000 to 2016, showing how the dynamic method is more effective than alternatives in this respect

Measuring Time-Dynamics and Time-Stability of Journal Rankings in Mathematics and Physics by Means of Fractional p-Variations

[EN] Journal rankings of specific research fields are often used for evaluation purposes, both of authors and institutions. These rankings can be defined by means of several methods, as expert assessment, scholarly-based agreements, or by the ordering induced by a numeric index associated to the prestige of the journals. In order to be efficient and accepted by the research community, it must preserve the ordering over time, at least up to a point. Otherwise, the procedure for defining the ranking must be revised to assure that it reflects the presumably stable characteristic prestige that it claims to be quantifying. A mathematical model based on fractional p-variations of the values of the order number of each journal in a time series of journal rankings is explained, and its main properties are shown. As an example, we study the evolution of two given ordered lists of journals through an eleven-year series. These journal ranks are defined by using the 2-year Impact Factor of Thomson-Reuters (nowadays Clarivate Analytics) lists for MATHEMATICS and PHYSICS, APPLIED from 2002 to 2013. As an application of our model, we define an index that precludes the use of journal ranks for evaluation purposes when some minimal requirements on the associated fractional p-variations are not satisfied. The final conclusion is that the list of mathematics does not satisfy the requirements on the p-variations, while the list of applied physics does.The work of the first author was supported by Ministerio de Economi, Industria y Competitividad, Spain, under Research Grant CSO2015-65594-C2-1R Y 2R (MINECO/FEDER, UE). The work of the third author was supported by Ministerio de Economi, Industria y Competitividad, Spain, under Research Grant MTM2016-77054-C2-1-P. We did not receive any funds for covering the costs to publish in open access.Ferrer Sapena, A.; Díaz Novillo, S.; Sánchez Pérez, EA. (2017). Measuring Time-Dynamics and Time-Stability of Journal Rankings in Mathematics and Physics by Means of Fractional p-Variations. Publications. 5(3):1-14. https://doi.org/10.3390/publications5030021S1145

Do PageRank-based author rankings outperform simple citation counts?

The basic indicators of a researcher's productivity and impact are still the number of publications and their citation counts. These metrics are clear, straightforward, and easy to obtain. When a ranking of scholars is needed, for instance in grant, award, or promotion procedures, their use is the fastest and cheapest way of prioritizing some scientists over others. However, due to their nature, there is a danger of oversimplifying scientific achievements. Therefore, many other indicators have been proposed including the usage of the PageRank algorithm known for the ranking of webpages and its modifications suited to citation networks. Nevertheless, this recursive method is computationally expensive and even if it has the advantage of favouring prestige over popularity, its application should be well justified, particularly when compared to the standard citation counts. In this study, we analyze three large datasets of computer science papers in the categories of artificial intelligence, software engineering, and theory and methods and apply 12 different ranking methods to the citation networks of authors. We compare the resulting rankings with self-compiled lists of outstanding researchers selected as frequent editorial board members of prestigious journals in the field and conclude that there is no evidence of PageRank-based methods outperforming simple citation counts.Comment: 28 pages, 5 figures, 6 table

Modeling Scholar Profile in Expert Recommendation based on Multi-Layered Bibliographic Graph

A recommendation system requires the profile of researchers which called here as Scholar Profile for suggestions based on expertise. This dissertation contributes on modeling unbiased scholar profile for more objective expertise evidence that consider interest changes and less focused on citations. Interest changes lead to diverse topics and make the expertise levels on topics differ. Scholar profile is expected to capture expertise in terms of productivity aspect which often signified from the volume of publications and citations. We include researcher behavior in publishing articles to avoid misleading citation. Therefore, the expertise levels of researchers on topics is influenced by interest evolution, productivity, dynamicity, and behavior extracted from bibliographic data of published scholarly articles. As this dissertation output, the scholar profile model employed within a recommendation system for recommending productive researchers who provide academic guidance. The scholar profile is generated from multi layers of bibliographic data, such as layers of author, topic, and relations between those layers to represent academic social network. There is no predefined information of topics in a cold-start situation, such that procedures of topic mapping are necessary. Then, features of productivity, dynamicity and behavior of researchers within those layers are taken from some observed years to accommodate the behavior aspect. We experimented with AMiner dataset often used in the following bibliographic data related studies to empirically investigate: (a) topic mapping strategies to obtain interest of researchers, (b) feature extraction model for productivity, dynamicity, and behavior aspects based on the mapped topics, and (c) expertise rank that considers interest changes and less focused on citations from the scholar profile. Ensuring the validity results, our experiments worked on standard expert list of AMiner researchers. We selected Natural Language Processing and Information Extraction (NLP-IE) domains because of their familiarity and interrelated context to make it easier for introducing cases of interest changes. Using the mapped topics, we also made minor contributions on transformation procedures for visualizing researchers on maps of Scopus subjects and investigating the possibilities of conflict of interest

Ranking in evolving complex networks

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google’s PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes

Detecting hierarchical relationships and roles from online interaction networks

In social networks, analysing the explicit interactions among users can help in inferring hierarchical relationships and roles that may be implicit. In this thesis, we focus on two objectives: detecting hierarchical relationships between users and inferring the hierarchical roles of users interacting via the same online communication medium. In both cases, we show that considering the temporal dimension of interaction substantially improves the detection of relationships and roles. The first focus of this thesis is on the problem of inferring implicit relationships from interactions between users. Based on promising results obtained by standard link-analysis methods such as PageRank and Rooted-PageRank (RPR), we introduce three novel time-based approaches, \Time-F" based on a defined time function, Filter and Refine (FiRe) which is a hybrid approach based on RPR and Time-F, and Time-sensitive Rooted-PageRank (T-RPR) which applies RPR in a way that takes into account the time-dimension of interactions in the process of detecting hierarchical ties. We experiment on two datasets, the Enron email dataset to infer managersubordinate relationships from email exchanges, and a scientific publication coauthorship dataset to detect PhD advisor-advisee relationships from paper co-authorships. Our experiments demonstrate that time-based methods perform better in terms of recall. In particular T-RPR turns out to be superior over most recent competitor methods as well as all other approaches we propose. The second focus of this thesis is examining the online communication behaviour of users working on the same activity in order to identify the different hierarchical roles played by the users. We propose two approaches. In the first approach, supervised learning is used to train different classification algorithms. In the second approach, we address the problem as a sequence classification problem. A novel sequence classification framework is defined that generates time-dependent features based on frequent patterns at multiple levels of time granularity. Our framework is a exible technique for sequence classification to be applied in different domains. We experiment on an educational dataset collected from an asynchronous communication tool used by students to accomplish an underlying group project. Our experimental findings show that the first supervised approach achieves the best mapping of students to their roles when the individual attributes of the students, information about the reply relationships among them as well as quantitative time-based features are considered. Similarly, our multi-granularity pattern-based framework shows competitive performance in detecting the students' roles. Both approaches are significantly better than the baselines considered