Search (6 results, page 1 of 1)

Yan, E.: Finding knowledge paths among scientific disciplines (2014) 0.02
```
0.024277154 = product of:
  0.07283146 = sum of:
    0.07283146 = product of:
      0.109247185 = sum of:
        0.06750664 = weight(_text_:network in 1534) [ClassicSimilarity], result of:
          0.06750664 = score(doc=1534,freq=4.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.34791988 = fieldWeight in 1534, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1534)
        0.041740544 = weight(_text_:22 in 1534) [ClassicSimilarity], result of:
          0.041740544 = score(doc=1534,freq=4.0), product of:
            0.15257138 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043569047 = queryNorm
            0.27358043 = fieldWeight in 1534, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1534)
      0.6666667 = coord(2/3)
  0.33333334 = coord(1/3)
```
Abstract

This paper uncovers patterns of knowledge dissemination among scientific disciplines. Although the transfer of knowledge is largely unobservable, citations from one discipline to another have been proven to be an effective proxy to study disciplinary knowledge flow. This study constructs a knowledge-flow network in which a node represents a Journal Citation Reports subject category and a link denotes the citations from one subject category to another. Using the concept of shortest path, several quantitative measurements are proposed and applied to a knowledge-flow network. Based on an examination of subject categories in Journal Citation Reports, this study indicates that social science domains tend to be more self-contained, so it is more difficult for knowledge from other domains to flow into them; at the same time, knowledge from science domains, such as biomedicine-, chemistry-, and physics-related domains, can access and be accessed by other domains more easily. This study also shows that social science domains are more disunified than science domains, because three fifths of the knowledge paths from one social science domain to another require at least one science domain to serve as an intermediate. This work contributes to discussions on disciplinarity and interdisciplinarity by providing empirical analysis.

Date

26.10.2014 20:22:22
Yan, E.; Ding, Y.: Discovering author impact : a PageRank perspective (2011) 0.01
```
0.012001181 = product of:
  0.03600354 = sum of:
    0.03600354 = product of:
      0.10801062 = sum of:
        0.10801062 = weight(_text_:network in 2704) [ClassicSimilarity], result of:
          0.10801062 = score(doc=2704,freq=4.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.5566718 = fieldWeight in 2704, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0625 = fieldNorm(doc=2704)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
```
Abstract

This article provides an alternative perspective for measuring author impact by applying PageRank algorithm to a coauthorship network. A weighted PageRank algorithm considering citation and coauthorship network topology is proposed. We test this algorithm under different damping factors by evaluating author impact in the informetrics research community. In addition, we also compare this weighted PageRank with the h-index, citation, and program committee (PC) membership of the International Society for Scientometrics and Informetrics (ISSI) conferences. Findings show that this weighted PageRank algorithm provides reliable results in measuring author impact.

Ding, Y.; Yan, E.: Scholarly network similarities : how bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other (2012) 0.01

0.0063645868 = product of:
  0.01909376 = sum of:
    0.01909376 = product of:
      0.057281278 = sum of:
        0.057281278 = weight(_text_:network in 274) [ClassicSimilarity], result of:
          0.057281278 = score(doc=274,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.29521978 = fieldWeight in 274, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=274)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Yan, E.; Yu, Q.: Using path-based approaches to examine the dynamic structure of discipline-level citation networks (2016) 0.01
```
0.0063645868 = product of:
  0.01909376 = sum of:
    0.01909376 = product of:
      0.057281278 = sum of:
        0.057281278 = weight(_text_:network in 3053) [ClassicSimilarity], result of:
          0.057281278 = score(doc=3053,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.29521978 = fieldWeight in 3053, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=3053)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
```
Abstract

The objective of this paper is to identify the dynamic structure of several time-dependent, discipline-level citation networks through a path-based method. A network data set is prepared that comprises 27 subjects and their citations aggregated from more than 27,000 journals and proceedings indexed in the Scopus database. A maximum spanning tree method is employed to extract paths in the weighted, directed, and cyclic networks. This paper finds that subjects such as Medicine, Biochemistry, Chemistry, Materials Science, Physics, and Social Sciences are the ones with multiple branches in the spanning tree. This paper also finds that most paths connect science, technology, engineering, and mathematics (STEM) fields; 2 critical paths connecting STEM and non-STEM fields are the one from Mathematics to Decision Sciences and the one from Medicine to Social Sciences.
Li, D.; Ding, Y.; Sugimoto, C.; He, B.; Tang, J.; Yan, E.; Lin, N.; Qin, Z.; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model (2011) 0.01
```
0.0053038225 = product of:
  0.015911467 = sum of:
    0.015911467 = product of:
      0.047734402 = sum of:
        0.047734402 = weight(_text_:network in 4759) [ClassicSimilarity], result of:
          0.047734402 = score(doc=4759,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.2460165 = fieldWeight in 4759, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4759)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
```
Abstract

The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems.

Zhao, M.; Yan, E.; Li, K.: Data set mentions and citations : a content analysis of full-text publications (2018) 0.00

0.003971059 = product of:
  0.011913176 = sum of:
    0.011913176 = product of:
      0.035739526 = sum of:
        0.035739526 = weight(_text_:29 in 4008) [ClassicSimilarity], result of:
          0.035739526 = score(doc=4008,freq=2.0), product of:
            0.15326229 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.043569047 = queryNorm
            0.23319192 = fieldWeight in 4008, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.046875 = fieldNorm(doc=4008)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Date: 18.12.2017 16:29:01

Search (6 results, page 1 of 1)

Authors

Themes