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This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research.

Aim of study: A common procedure when evaluating scientists is considering the journal’s quartile of impact factors (within a category), many times considering the quartile in the year of publication instead of the last available ranking. We tested whether the extra work involved in considering the quartiles of each particular year is justified Area of study: Europe Material and methods: we retrieved information from all papers published in 2008-2012 by researchers of AGROTECNIO, a centre focused in a range of agri-food subjects. Then, we validated the results observed for AGROTECNIO against five other European independent research centres: Technical University of Madrid (UPM) and the Universities of Nottingham (UK), Copenhagen (Denmark), Helsinki (Finland), and Bologna (Italy). Main results: The relationship between the actual impact of the papers and the impact factor quartile of a journal within its category was not clear, although for evaluations based on recently published papers there might not be much better indicators. We found unnecessary to determine the rank of the journal for the year of publication as the outcome of the evaluation using the last available rank was virtually the same. Research highlights: We confirmed that the journal quality reflects only vaguely the quality of the papers, and reported for the first time evidences that using the journal rank from the particular year that papers were published represents an unnecessary effort and therefore evaluation should be done simply considering the last available rank.

The Peer Reputation (PR) metric was recently proposed in the literature, in order to judge a researcher’s contribution through the quality of the venue in which the researcher’s work is published. PR, proposed by Nelakuditi et al., ties the selectivity of a publication venue with the reputation of the first author’s institution. By computing PR for a percentage of the papers accepted in a conference or journal, a more solid indicator of a venue’s selectivity than the paper Acceptance Ratio (AR) can be derived. In recent work we explained the reasons for which we agree that PR offers substantial information that is missing from AR, however we also pointed out several limitations of the metric. These limitations make PR inadequate, if used only on its own, to give a solid evaluation of a researcher’s contribution. In this work, we present our own approach for judging the quality of a Computer Science/Computer Engineering conference venue, and thus, implicitly, the potential quality of a paper accepted in that conference. Driven by our previous findings on the adequacy of PR, as well as our belief that an institution does not necessarily “make” a researcher, we propose a Conference Classification Approach (CCA) that takes into account a number of metrics and factors, in addition to PR. These are the paper’s impact and the authors’ h-indexes. We present and discuss our results, based on data gathered from close to 3000 papers from 12 top-tier Computer Science/Computer Engineering conferences belonging to different research fields. In order to evaluate CCA, we compare our conference rankings against multiple publicly available rankings based on evaluations from the Computer Science/Computer Engineering community, and we show that our approach achieves a very comparable classification. [ABSTRACT FROM AUTHOR]

Structured measurements have been widely used to measure the impact of scholarly entities based on scholarly networks. Existing methods use heterogeneous scholarly networks and the PageRank algorithm to quantify the impact of scientific collaboration. However, by ignoring important higher-order relationships in citation networks, the impact of scientific collaboration is quantified by relying on first-order relationships, which leads to indistinguishable self-citations. In this paper, to address these shortcomings, we propose a Motif-based Scientific Collaboration Impact Rank framework, named as MSCIRank, which leverages the triangular motifs from the reconstructed collaboration-citation networks and integrates the first-order and higher-order relationships in the PageRank algorithm to quantify the impact of scientific collaboration and scholarly papers. MSCIRank consists of two models, i,e, linear and non-linear. Extensive experiments have demonstrated the effectiveness of MSCIRank. The experimental results show that MSCIRank is better than SCIRank in identifying Nobel Prize papers in terms of Recall. The MSCIRank model can weaken or strengthen the impact of self-citation. Linear MSCIRank is consistent with Pareto's principle, while non-linear MSCIRank is inconsistent. In addition, the average impact of pairs of co-authors with high impact in the linear MSCIRank is much higher than that in the non-linear MSCIRank. [ABSTRACT FROM AUTHOR]

Abstract: To publish papers that have impact is indispensable for good scientists. It is frequently accepted that the impact factor (IF) of a journal reflects the likelihood of a paper to have impact. Many scientists may then attempt publishing their papers in journals with the highest possible IF. This is relevant for crop scientists as in many cases our hypotheses may be relevant to agronomy/agriculture but the product of our work may be published in the more basic plant science journals, which in general have higher IF than agronomic journals. To test whether agronomic journals may be better vehicles than plant science journals for crop physiological work, I did an analysis of my own publications (only “standard” papers, disregarding reviews) relating their annual citation rates against the IF of the journal in which they were published. The relationship was virtually inexistent, and my most frequently quoted papers were published in agronomic rather than in plant sciences journals. Conversely, restricting the analysis to the papers published within agronomic journals there was a relationship between the record of citations of the papers and the IF of the journals, particularly if the analysis is restricted to the most quoted papers in each journal. I then analyzed the cases of three other well-recognized crop scientists, to check whether the findings with my results were exceptional, and in the three cases results were remarkably similar. Therefore, it seems that the journal IF might be a valuable criterion for choosing the best journal in which to publish our best contributions only when comparing within agronomic journals, but not within combinations of agronomy and plant science journals. This should be also taken into account in evaluation processes when comparing scientists working in related but not identical fields. [Copyright &y& Elsevier]