pagerank paper citation

Even if the number of citations received by the citing document is used to weigh the citation in a dynamic manner, it is possible that these weights could be manipulated again by citing the citing document with less impactful documents: thus the influence of such documents only becomes indirect and secondary, and does not altogether diminish. We first present some qualitative observations to illustrate the utility of pagerank-index in this data set. Timesteps have no meaning in terms of physical time but simply denote an instance where a new paper is spawned. Particularly we used both a simulated system of authors and real-world databases to establish its comparative merits. <> It is concluded that a Web page benefits from links inside its Web community and on the other hand irrelevant links penalize the Web pages and their Web communities. 0000001899 00000 n Everything You Need to Know about Google PageRank - Semrush Blog The paper offers a reflexive and nuanced analysis of the "patent-paper citation" as a theoretical and historical construct, and it calls for a broader and contextualised understanding of patent references, including their social, legal and rhetorical function. It would be ideal if we can trace the history of this field, from its inception. Pennysaver apartments for rent westchester ny Importantly, we showed that the correlation between pagerank-index and h-index is about 50% generally (0.49 for Quantum Game Theory and 0.59 for HEP-theory). For authors of a similar level of seniority (as indicated by their IDs), the collaborative authors have a clear advantage. We decided the margin-of-error to be 0.0001 which gives us the required consistency over multiple executions of the page-rank algorithm. The rest of the paper is organised as follows: in the Methods section, we describe how the pagerank-index is defined and could be computed, and provide detailed justifications for our definitions. The results of this experiment are given as a plot in S1 Appendix. The process has three stages: (i) computing the page-rank value of each paper in the system (each node in the citation network) (ii) assigning weighted proportions of such values to each author in the system (each node in the collaboration network) (iii) computing the author pagerank-index as a percentile. Using impact factors for this purpose again has a number of well-documented shortfalls. PageRank for Ranking Authors in Co-citation Networks Measuring Centrality in Legal Citation Networks - SSRN Fig 13 shows the variation of the average h-index and average pagerank-index over time of for both groups of authors. The PageRank Citation Ranking: Bringing Order to the Web In each case or scenario, two groups of authors are contrasted based on an inherent publication habit in which they are different. The top papers by D. Abbott were published in highly reputable journals, such as Nature, Physical Review Letters, and Statistical Science. PageRank was influenced by citation analysis, early developed by Eugene Garfield in the 1950s at the University of . In simple terms, PageRank measures the relative value of each and every page that Googlebot finds while crawling the web. tynch.a-przydatek.de Improving Graph Neural Networks at Scale: Combining Approximate PageRank and CoreRank. First, let us make some qualitative observations which illustrate the utility of pagerank-index and how it can help rectify biased perceptions of the scientific contributions of scientists in a field. However, even in the present era, it could be argued that a scientist maintaining an optimal balance between quality and quantity is most likely to be favoured by h-index, though maintaining such a balance does not necessarily make him a better scientist. The Google PageRank Algorithm | SoftwarePundit For example, Zhang et al. These are chosen by weighted preferential attachment, as described below. In the remainder of this paper, we first present a detailed account of our method in the section of PR-index. It could be demonstrated, by individual analysis as has been done in the previous paragraphs, that those authors who have low pagerank-index value either have typically co-authored with a larger pool of authors, or have received most of their citations from low-impact papers, while those authors who score highly have relatively worked with a smaller pool of authors and received their citations from more reputable sources, thus justifying their high pagerank-index. This subnetwork has co-authored together some highly cited papers, so it is not surprising that members of this subgroup, and particularly the authors mentioned above, have their ranks within the field change considerably when pagerank-index is used (X. Xu: h-Rank = 7, -Rank = 46, R. Han, h-Rank = 7, -Rank = 46, M. Shi, h-Rank = 27, -Rank = 147). Therefore, it is possible to choose one of these profiles as a field for the anaylsis, though such profiles typically do not contain more than a thousand papers and many papers belonging to the relevant field may be missing from these profiles. It is our hope that the pagerank-index will be extensively used by scientific community in quantifying the scientific output of researchers. These authors have published in high impact journals like Science which pagerank-index implicitly takes in to consideration (because many papers citing their papers also would have appeared in Science, cited by many others) while h-index does not. 0000024293 00000 n 0000033411 00000 n PDF PageRank for Ranking Authors in Co-citation Networks - Texas A&M University Note that the collaborative authors, by virtue of extensive collaboration, have a massive advantage in terms of average paper and citation counts(The topological characteristics of the evolved citation network and the collaboration network at the end of simulation are included in S1 Supplementary Material. (PDF) PageRank Algorithm - ResearchGate In any case, compared to the time it often takes for a document to be picked up by a database such as Google scholar after it becomes available in the web, the time it may take for it to influence the pagerank-index will be minimal. As described below, the evolution process realistically imitates the growth of a research field and corresponding citation and collaboration networks. xref While this clearly gives them all high h-index values, the pagerank-index considers the fact that the co-author pool is relatively large and thus does not give them very high scores. In fact, the authors J.Du (4.7), X. Xu (5.5), X. Zhou (5.3), and R. Han (5.5) all appear to have co-authored several papers together, as part of a relatively large group of authors, as the average number of authors in their papers given in brackets above indicate. A general theory of epidemics can explain the growth of symbolic logic from 1847 to 1962 and an epidemic model predicts the rise and fall of particular research areas within symbolic logic. For example, we show that the h-index is heavily influenced by the number of authors of a paper, so that scientists could benefit by working in larger groups, whereby the pagerank-index neutralises this effect and highlights the contributions of smaller groups of authors. Likewise, E. Elizalde is ranked 229th using h-index but leaps to the 6th position when pagerank-index is used. a simple pagerank for citation network. If this does not happen, locally famous authors whose research does not have global impact but gets cited by their colleagues in their country or research circle can get rewarded. We avoid showing the h-index percentile range below 95%, since h-index is a discrete quantity and there are only two data points below 95%, corresponding to h-index = 1 (88.9%) and h-index = 0 (55.4%), and showing these would reduce clarity of the plot. The process has three stages: (i) computing the page-rank value of each paper in the system (each node in the citation network) (ii) assigning weighted proportions of such values to each author in the system (each node in the collaboration network) (iii) computing the author pagerank-index as a percentile. However, to demonstrate the utility of the new index, we need to be able to define a particular author community in some way, since the index is not yet implemented in commercial databases. We undertake a detailed analysis to demonstrate the utility of the pagerank-index. Therefore, in our simulation system we spawn two groups of authors, with one group having an inherent tendency to massage their h-index this way, while the other group does not. These entities use well known metrics to compare the scientific output of researchers. For example, it is well known that it is more difficult to be cited in some fields compared to others, and researchers from these fields complain that they as a result get lower h-indices. A manipulative document, once spawned, will be assigned authors only from the manipulative authors pool. This is mainly because both of these authors have a relatively high average authors per paper value and they have been second / third authors more often, which factors the pagerank-index takes into consideration but the h-index does not. A novel citation recommendation system called DiSCern, which finds relevant and diversified citations in response to a search query in terms of keyword(s) to describe the query topic, while using only the citation graph and the keywords associated with the articles, and no latent information. The system would spawn two types of papers accordingly: collaborative papers and non-collaborative papers, which would be assigned authors only from the respective relevant pool of authors. How To Add Citations A Paper ? - edocs.utsa Similarly, Hu et al. Comparing to the original graph, we add an extra edge (node6, node1) to form a cycle. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages. We have undertaken extensive studies to validate the utility of the new measure. For authors of similar seniority, the manipulative author group has a clear advantage. PageRank: Link Analysis Explanation and Python Implementation from PageRank is calculated based on a mathematical formula, which the original Google paper defines as follows: PR (A) = (1d) + d (PR (T1)/C (T1) + + PR (Tn)/C (Tn)) In this equation, T1 to Tn are all pages linking to page A. The manipulative authors had 70.6 papers per author on average and 256.2 citations per author on average, while the corresponding numbers for non-manipulative authors were 35.4 and 130.2. We show how to efficiently compute PageRank for large numbers of pages. This work proposes paper recommendation algorithms by using the citation information among papers to find relevant papers and shows that this slight guidance helps the user to reach a desired paper in a more efficient way. Even though the contrast is less sharp in this third scenario, all of these results indicate that the pagerank-index can reduce the perceived disadvantage quality oriented authors may face when their performance is measured by the h-index. A citation network of documents with differing impacts. Note that though the collaboration network is not an input in computing the pagerank-index, the pagerank-index is able to recognize and reward authors who perform such an important role in the development of the field, as indicated by the relatively high pagerank-index of this author. Mahendra Piraveenan, endobj Normally citing two sources in one sentence needs to consider the same parenthesis format as in single source formatting. Several modifications of the classical PageRank formula adapted for bibliographic networks take into account not only the citation but also the co-authorship graph and turn out to be better than the standard PageRank ranking. Stage III involves summing all pagerank value shares an author has obtained, comparing it with other authors in the community and assigning a percentile value to that author accordingly. Yet, it is perfectly clear that a citation by a paper from a highly regarded journal, such as Nature, should be treated differently from a citation by a workshop paper or a technical report. . In this run, the system was simulated until 25000 papers were spawned consisting of 5012 non-collaborative papers. Page, Lawrence and Brin, Sergey and Motwani, Rajeev and Winograd, Terry (1999) The PageRank Citation Ranking: Bringing Order to the Web. It is clear that this author plays an important role in the field by being the bridge between two sets of authors who work perhaps in two sub-fields. Each page is given a PageRank score between 1 and 10. However, since we have no evidence to suggest that some authors in this particular field deliberately publish documents to self-cite, and to avoid belabouring the point unnecessarily, we avoid showing results for such groupings in this paper. We do not want to use the overall h-index since that purportedly measures the scientific contributions of authors to all fields, not just quantum game theory. Furthermore, this author appears as the last author in their most highly cited papers. For instance, quality or the impact of the papers is not taken in to account if number of papers is used while a few papers with a high number of citations co-authored by many authors can inflate the number of citations measure. We compare PageRank to an idealized random Web surfer. The PageRank Citation Ranking.pdf - Course Hero Therefore it is of importance to continually analyse and further develop methods and metrics which are used for this purpose. For example, the underlying citation network could be updated and page-rank algorithm could be rerun every hour, every day, or every week. networkx.pagerank pagerank(G, alpha=0.84999999999999998, max_iter=100, tol=1e-08, nstart=None) Return the PageRank of the nodes in the graph. This is done because in real world also, already highly cited papers are more likely to be noticed and cited by new papers. Even though it could be argued that high-quality publications are likely to result in higher number of citations, ultimately the h-index is also bound by the number of papers a scientist has produced. The higher their score, the more yours will increase. While Researchgate has not revealed the exact algorithm behind the RG score, it seems that those scientists who do more online activity within Researchgate are rewarded: for posting, for answering questions, for following and being followed, for sharing data etc. 0000029973 00000 n We have stated that the pagerank-index is fairer to the scientists compared to the existing metrics, and that it can help minimise the impact of massaging and potential for questionable practises. We note here, however that we do not necessarily claim one group of authors are somehow unethical in all these scenarios. PDF Time-Aware Weighted PageRank for Paper Ranking in Academic Graphs A scientist working on her own should be able to produce three papers, which should fetch her 150 citations after one year. We may again note that the citation network displays scale-free characteristics, while the collaboration network does not). However the usual practice is to order the authors by the contributions they made to a certain publication. A more nuanced approach is therefore necessary. It essentially indicates whether an author/paper belongs to a certain group or the other group in that scenario. In the previous subsection, we considered individual authors to show how some authors whose definitive contributions are obfuscated by h-index come to prominence when pagerank-index is used for comparison. The authors are assumed to be otherwise similar, on average, between the two groups. Therefore we first generated citation networks from this dataset, considering each instance after a new paper has been added as a timestep. After each timestep, the pagerank algorithm was run on the citation network until steady state, and the pagerank score, and by extension the pagerank-index, of authors were updated. PageRank centrality & EigenCentrality - Cambridge Intelligence We consider three independent case studies using this synthesized system. We have focused primarily on comparing pagerank-index with h-index, since h-index is itself an improvement on other previous measures, as Hirsch explains [2]. Flitney, who have scored well both in terms of h-index and pagerank-index as discussed above. The pagerank-index is no silver bullet though and has some shortcomings. Purpose The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. It is a sophisticated and elegant measure since its method of computation ensures the position of authors within their communities, their relative standing, and their historic contribution in developing a field will be rewarded, without assigning or using crude weights to represent any of these factors. The implication is that using h-index, a non-manipulative author will be penalized in average about ten times more than using pagerank-index (Since pagerank-index is a percentile, we also tested whether the percentile ranking of pagerank-index makes the difference smaller (not shown). We then used the provided meta data in order to build the collaboration network for supplementary analysis, though, as mentioned, the pagerank-index only uses the citation network in its computation. 0000032608 00000 n It is a static snapshot of the HEP-TH citation network as at April, 2003 featuring 29555 publications over the duration of January, 1993 to April, 2003. We ran pagerank on these citation networks until steady state to compute the pagerank-index for all authors at each timestep, and we also computed the h-index by considering the total number of citations each author had at a given timestep. It is a principled and nuanced measure, because it makes no assumptions about the status of the journals, conferences or authors other than using the actual citation data, yet considers the infinite levels of feedback a paper receives rather than the simple citation count. Again, the collaborative tendencies we have considered are extreme, however this experiment highlights how pagerank-index would negate the unfair advantage some authors can gain by using extensive collaboration. However, not all of these references are necessarily to existing papers in the simulated system. The simulation system was set up to reflect the realistic process of evolution of a field where new authors and papers are continually spawned, however extreme scenarios were sometimes used to demonstrate our points clearly, without loss of generality. 0000034118 00000 n Besides, such metrics are now accepted as indicators of the prestige and standing of a researcher in the scientific community. Low productivity is rewarded by the number of citations per paper measure. Despite this, the field of quantum game theory as defined by this profile is a suitable dataset to test the utility of the pagerank-index, particularly since pagerank-index of all participating authors could be computed historically from the moment the first paper was published. Nonetheless, to propagate information GNNs rely on a . While this is an extreme scenario which rarely happens in real world, it is suitable to demonstrate the effect of such documents in the h-index and pagerank-index easily. 0000025633 00000 n 0000030679 00000 n Looking at the data available in the Google Scholar Profile, it is very easy to see how and why this happens. Therefore, we demonstrate that the pagerank-index is a much fairer metric for ranking scientists, and captures a broader interpretation of meaningful contribution to the respective field than simply cultivating citations from whatever source. That was a fantastic breakthrough, but something started happening over the years. The details of the highlighted authors are listed in Table 2. But there is still much that can be said objectively about the relative importance of Web pages. The h-index was defined by Hirsch [2] as the number of papers with h number of citations. As web page networks and citation networks have similar structures, many scholars have introduced this algorithm for the identification of important nodes [ 13 - 16 ]. This is quite remarkable. (2), The second stage of computation involves distributing this page-rank value among the respective authors of each publication. The gain is similarly expressed in percentages throughout the paper, even when percentiles are used.) The PageRank computations require several passes, called "iterations", through the collection to adjust approximate PageRank values to more closely reflect the theoretical true . To avoid anomalies, we only consider those authors who published more than 5 papers and been cited more than 5 times. Since the pagerank-index is a percentile, percentile values were used for the h-index as well, rather than actual h-index values. We convert the h-index also to percentile, since it makes it easier to compare the two metrics this way, and also because otherwise it might be argued that the variation is due to a percentile being compared with a direct score. We emphasise that the pagerank-index is a better metric because it is able to go beyond mere citation counts and rewards definitive contributions of authors in many ways, as described above. For example, if an author had written two papers and in each of these there were two co-authors, this author has in total 2/3 paper-shares: it is as if this author wrote 2/3 of a paper by himself. Indeed, [20] suggests that more than 80% of papers published today confirm with this practise and the scientific world is converging towards it. It is asserted that citation data are not merely flawed in one or another respect but that they are so incomplete and so biased, in principle, that they should not be used in empirical studies of intellectual influence. In order to increase the PageRank, the intuitive approach is to increase its parent node to pass the rank in it. This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. Technical Report. Let us take researcher D. Abbott, for example. Importantly, a web page designer cannot increase the pagerank of her page by providing links to it from many empty or purpose-less websites, which exist only to boost the pagerank of other pages.

How Did The Dhow Affect Trade, Best Time To Eat Sauerkraut For Gut Health, Ga State Vs Charlotte Football Score, 2019 Santa Cruz Bronson Geometry, Carabao Cup Final 2022 On Tv Usa, Utsa Fall 2023 Calendar, Ramstein Commissary Early Bird Hours,