A noninformetric analysis of the relationship between citation age and journal productivity

Egghe, Leo

doi:10.1002/1532-2890(2001)9999:9999<::aid-asi1079>3.0.co;2-l

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…In the second class, the nodes in the citation graph represent collections. Collections may be journals (e.g., [16], [4], [28], [15], [27], [18], [3]), conferences (e.g., [5]), or even authors. The weighted edges in the citation graph represent the total number of citations made from one collection to another.…”

Section: Related Workmentioning

confidence: 99%

Measuring credibility of users in an E-learning social network

Wei

King

2010

2010 5th International Conference on Computer Science &Amp; Education

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Learning Villages (LV) is an E-learning platform for people's online discussions and frequently citing postings of one another. It will greatly improve learning efficiency if credible users can be accurately identified in the E-learnning community. In this paper, we propose a novel method to rank credibility of users in the LV system. We first propose a k-EACM graph to describe the article citation structure in the LV system. The k-EACM graph not only describes the citation attitude between articles but also takes into account indirect citation links. We further build a weighted graph model k-UCM graph to reveal the implicit relationships between users hidden behind the citations among their articles. Finally, we design a graph based ranking algorithm, called Credible Author Ranking (CAR) algorithm, which can be applied to rank nodes in a graph with negative edges. We perform experiments on three simulated data sets. The experimental results show that our proposed method works well to rank credibility of users in the LV system. The comparison of results on average credibility of users in each set and composition of users in top N of three cases demonstrates that the CAR algorithm ranks users credibility in consistent with the predefined ground truth.1

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Section: Related Workmentioning

confidence: 99%

Measuring credibility of users in an E-learning social network

Wei

King

2010

2010 5th International Conference on Computer Science &Amp; Education

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“…Temporal locality has been studied for many years in citation analysis (see e.g., [9,11])-it is well-known that a scientific paper gets the majority of its citations soon after it is published and as time passes, receives fewer citations. Amitay et al [1] have also identified aspects of temporal locality in Web domains, showing that incorporating hyperlink timestamps into link-based page-ranking algorithms can improve retrieval accuracy.…”

Section: Background and Related Workmentioning

confidence: 99%

Temporal-Relational Classifiers for Prediction in Evolving Domains

Sharan¹,

Neville²

2008

2008 Eighth IEEE International Conference on Data Mining

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Many relational domains contain temporal information and dynamics that are important to model (e.g., social networks, protein networks). However, past work in relational learning has focused primarily on modeling static "snapshots" of the data and has largely ignored the temporal dimension of these data. In this work, we extend relational techniques to temporally-evolving domains and outline a representational framework that is capable of modeling both temporal and relational dependencies in the data. We develop efficient learning and inference techniques within the framework by considering a restricted set of temporalrelational dependencies and using parameter-tying methods to generalize across relationships and entities. More specifically, we model dynamic relational data with a twophase process, first summarizing the temporal-relational information with kernel smoothing, and then moderating attribute dependencies with the summarized relational information. We develop a number of novel temporal-relational models using the framework and then show that the current approaches to modeling static relational data are special cases within the framework. We compare the new models to the competing static relational methods on three real-world datasets and show that the temporal-relational models consistently outperform the relational models that ignore temporal information-achieving significant reductions in error ranging from 15% to 70%.

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“…It has been shown that citations of journal articles behave in a consistent manner. In general, the more time passes, the less citatihons a paper receives (Egghe, 2001; Garfield, 1998). In fact, a journal will be considered more prominent the higher its citation half‐life is (i.e., how old in years most of the papers currently cited are in the literature that were previously published in this journal).…”

Section: Introductionmentioning

confidence: 99%

Trend detection through temporal link analysis

Amitay¹,

Carmel²,

Herscovici³

et al. 2004

J. Am. Soc. Inf. Sci.

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Although time has been recognized as an important dimension in the co-citation literature, to date it has not been incorporated into the analogous process of link analysis on the Web. In this paper, we discuss several aspects and uses of the time dimension in the context of Web information retrieval. We describe the ideal casewhere search engines track and store temporal data for each of the pages in their repository, assigning timestamps to the hyperlinks embedded within the pages. We introduce several applications which benefit from the availability of such timestamps. To demonstrate our claims, we use a somewhat simplistic approach, which dates links by approximating the age of the page's content. We show that by using this crude measure alone it is possible to detect and expose significant events and trends. We predict that by using more robust methods for tracking modifications in the content of pages, search engines will be able to provide results that are more timely and better reflect current real-life trends than those they provide today.

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A noninformetric analysis of the relationship between citation age and journal productivity

Cited by 7 publications

References 22 publications

Measuring credibility of users in an E-learning social network

Measuring credibility of users in an E-learning social network

Temporal-Relational Classifiers for Prediction in Evolving Domains

Trend detection through temporal link analysis

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