Zipf’s law and log-normal distributions in measures of scientific output across fields and institutions: 40 years of Slovenia’s research as an example

A Rough Hybrid Multicriteria Decision-Making Model for Improving the Quality of a Research Information System

Improving the quality of research information systems is an important goal in the process of improving the performance of research management in Chinese universities. Since the evaluation of information system (IS) quality is a multicriteria decision problem, it is critical to identify the interrelationships among the dimensions and criteria, and decide on the important criteria for proposed improvement strategies. This paper suggests a hybrid multicriteria decision-making (MCDM) model for improving the quality of a research information system. First, a rough method combined with the decision-making trial and evaluation laboratory and analytical network process (rough DANP) model is used to improve the objectivity of expert judgements. Additionally, the rough DANP can be used to construct an influential network relationship map (INRM) between research information system components to derive the criterion weights. The complex proportional assessment of alternatives with rough numbers (COPRAS-R) is applied to evaluate the performance of the research information system. A Chinese university research information system is chosen to illustrate the usefulness of the proposed model. The results show that efficiency, effectiveness, and user frequency have the highest priorities for improvement. Selected management implications based on the actual case study are supplied.

Power Law Distributions in Information Retrieval

Several properties of information retrieval (IR) data, such as query frequency or document length, are widely considered to be approximately distributed as a power law. This common assumption aims to focus on specific characteristics of the empirical probability distribution of such data (e.g., its scale-free nature or its long/fat tail). This assumption, however, may not be always true. Motivated by recent work in the statistical treatment of power law claims, we investigate two research questions: (i) To what extent do power law approximations hold for term frequency, document length, query frequency, query length, citation frequency, and syntactic unigram frequency? And (ii) what is the computational cost of replacing ad hoc power law approximations with more accurate distribution fitting? We study 23 TREC and 5 non-TREC datasets and compare the fit of power laws to 15 other standard probability distributions. We find that query frequency and 5 out of 24 term frequency distributions are best approximated by a power law. All remaining properties are better approximated by the Inverse Gaussian, Generalized Extreme Value, Negative Binomial, or Yule distribution. We also find the overhead of replacing power law approximations by more informed distribution fitting to be negligible, with potential gains to IR tasks like index compression or test collection generation for IR evaluation.

Fragmented Romanian sociology: growth and structure of the collaboration network

Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common.

Community structure and the evolution of interdisciplinarity in Slovenia's scientific collaboration network

Interaction among the scientific disciplines is of vital importance in modern science. Focusing on the case of Slovenia, we study the dynamics of interdisciplinary sciences from to . Our approach relies on quantifying the interdisciplinarity of research communities detected in the coauthorship network of Slovenian scientists over time. Examining the evolution of the community structure, we find that the frequency of interdisciplinary research is only proportional with the overall growth of the network. Although marginal improvements in favor of interdisciplinarity are inferable during the 70s and 80s, the overall trends during the past 20 years are constant and indicative of stalemate. We conclude that the flow of knowledge between different fields of research in Slovenia is in need of further stimulation.

A common construction pattern of English words and Chinese characters

Rankings are ubiquitous around the world. Here I investigate spatial ranking patterns of English Words and Chinese Characters, and reveal a common construction pattern related to phase separation. In detail, I analyze a list of different words in the English language, and find that the frequency of the number of letters per word linearly or nonlinearly decays over its rank in the frequency table. I interpret the linearly decaying area as a linear phase that covers 96.4% words, which is in sharp contrast to a nonlinear phase (representing the nonlinearly decaying area) that covers the remaining 3.6% words. Amazingly, the phase separation phenomenon with the same two percentages of 96.4% and 3.6% holds also for the relation between strokes and characters in the Chinese language although English and Chinese are two distinctly different language systems. The common construction pattern originates from the log-normal distributions of frequencies of words or characters, which can be understood by the joint effect of both the Weber-Fechner law in psychophysics and the principle of maximum entropy in information theory.

Characterizing and modeling citation dynamics

Citation distributions are crucial for the analysis and modeling of the activity of scientists. We investigated bibliometric data of papers published in journals of the American Physical Society, searching for the type of function which best describes the observed citation distributions. We used the goodness of fit with Kolmogorov-Smirnov statistics for three classes of functions: log-normal, simple power law and shifted power law. The shifted power law turns out to be the most reliable hypothesis for all citation networks we derived, which correspond to different time spans. We find that citation dynamics is characterized by bursts, usually occurring within a few years since publication of a paper, and the burst size spans several orders of magnitude. We also investigated the microscopic mechanisms for the evolution of citation networks, by proposing a linear preferential attachment with time dependent initial attractiveness. The model successfully reproduces the empirical citation distributions and accounts for the presence of citation bursts as well.

Emergence of scale-free leadership structure in social recommender systems

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.

A simple index for the high-citation tail of citation distribution to quantify research performance in countries and institutions

BACKGROUND

Conventional scientometric predictors of research performance such as the number of papers, citations, and papers in the top 1% of highly cited papers cannot be validated in terms of the number of Nobel Prize achievements across countries and institutions. The purpose of this paper is to find a bibliometric indicator that correlates with the number of Nobel Prize achievements.

METHODOLOGY/PRINCIPAL FINDINGS

This study assumes that the high-citation tail of citation distribution holds most of the information about high scientific performance. Here I propose the x-index, which is calculated from the number of national articles in the top 1% and 0.1% of highly cited papers and has a subtractive term to discount highly cited papers that are not scientific breakthroughs. The x-index, the number of Nobel Prize achievements, and the number of national articles in Nature or Science are highly correlated. The high correlations among these independent parameters demonstrate that they are good measures of high scientific performance because scientific excellence is their only common characteristic. However, the x-index has superior features as compared to the other two parameters. Nobel Prize achievements are low frequency events and their number is an imprecise indicator, which in addition is zero in most institutions; the evaluation of research making use of the number of publications in prestigious journals is not advised.

CONCLUSION

The x-index is a simple and precise indicator for high research performance.