Related papers: Eigenfactor
Altmetrics are tools for measuring the impact of research beyond scientific communities. In general, they measure online mentions of scholarly outputs, such as on online social networks, blogs, and news sites. Some stakeholders in higher…
The PageRank algorithm enables to rank the nodes of a network through a specific eigenvector of the Google matrix, using a damping parameter $\alpha \in ]0,1[$. Using extensive numerical simulations of large web networks, with a special…
The remarkable success of Deep Neural Networks(DNN) is driven by gradient-based optimization, yet this process is often undermined by its tendency to produce disordered weight structures, which harms feature clarity and degrades learning…
We study the statistics of citations made to the top ranked indexed journals for Science and Social Science databases in the Journal Citation Reports using different measures. Total annual citation and impact factor, as well as a third…
Ageing of publications, percentage of self-citations, and impact vary from journal to journal within fields of science. The assumption that citation and publication practices are homogenous within specialties and fields of science is…
Social media metrics - commonly coined as "altmetrics" - have been heralded as great democratizers of science, providing broader and timelier indicators of impact than citations. These metrics come from a range of sources, including…
The Impact Factor (IF), despite its widespread use, suffers from well-known biases that remain incompletely addressed in practice -- most notably its sensitivity to journal size and its lack of field normalization. Because of size…
Today, there exist many centrality measures for assessing the importance of nodes in a network as a function of their position and the underlying topology. One class of such measures builds on eigenvector centrality, where the importance of…
Eigenvalues of a graph are of high interest in graph analytics for Big Data due to their relevance to many important properties of the graph including network resilience, community detection and the speed of viral propagation. Accurate…
The $\alpha$ person is the dominant person in a group. We define the $\alpha$-author of a paper as the author of the paper with the highest $h$-index among all the coauthors, and an $\alpha$-paper of a scientist as a paper authored or…
Two concepts of centrality have been defined in complex networks. The first considers the centrality of a node and many different metrics for it has been defined (e.g. eigenvector centrality, PageRank, non-backtracking centrality, etc). The…
This chapter discusses altmetrics (short for "alternative metrics"), an approach to uncovering previously-invisible traces of scholarly impact by observing activity in online tools and systems. I argue that citations, while useful, miss…
Citation metrics serve as the cornerstone of scholarly impact evaluation despite their well-documented vulnerability to inflation through self-citation practices. This paper introduces the Self-Citation Adjusted Index (SCAI), a…
The h index is a widely recognized metric for assessing the research impact of scholars, defined as the maximum value h such that the scholar has published h papers each cited at least h times. While it has proven useful measuring…
An accurate and fair assessment of the efficiency and impact of scientific work is, despite a lot of recent research effort, still an open problem. The measurement of quality and success of individual scientists and research groups can be…
Scholarly impact may be metricized using an author's total number of citations as a stand-in for real worth, but this measure varies in applicability between disciplines. The detail of the number of citations per publication is nowadays…
An expert ranking of forestry journals was compared with journal impact factors and h-indices computed from the ISI Web of Science and internet-based data. Citations reported by Google Scholar appear to offer the most efficient way to rank…
Quantitatively profiling a scholar's scientific impact is important to modern research society. Current practices with bibliometric indicators (e.g., h-index), lists, and networks perform well at scholar ranking, but do not provide…
Semantic networks qualify the meaning of an edge relating any two vertices. Determining which vertices are most "central" in a semantic network is difficult because one relationship type may be deemed subjectively more important than…
Over the recent years, there has been a growing interest in developing new research evaluation methods that could go beyond the traditional citation-based metrics. This interest is motivated on one side by the wider availability or even…