Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Social Network Analysis

bibliometrix: An R-tool for comprehensive science mapping analysis

How to conduct a bibliometric analysis: An overview and guidelines

Clarivate Analytics’s Web of Science (WoS) is the world’s leading scientific citation search and analytical information platform. It is used as both a research tool supporting a broad array of scientific tasks across diverse knowledge domains as well as a dataset for large-scale data-intensive studies. WoS has been used in thousands of published academic studies over the past 20 years. It is also the most enduring commercial legacy of Eugene Garfield. Despite the central position WoS holds in contemporary research, the quantitative impact of WoS has not been previously examined by rigorous scientific studies. To better understand how this key piece of Eugene Garfield’s heritage has contributed to science, we investigated the ways in which WoS (and associated products and features) is mentioned in a sample of 19,478 English-language research and review papers published between 1997 and 2017, as indexed in WoS databases. We offered descriptive analyses of the distribution of the papers across countries, institutions and knowledge domains. We also used natural language processingtechniques to identify the verbs and nouns in the abstracts of these papers that are grammatically connected to WoS-related phrases. This is the first study to empirically investigate the documentation of the use of the WoS platform in published academic papers in both scientometric and linguistic terms.

/pdf/web-of-science-use-in-published-research-and-review-papers-4922paih0k.pdf

Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis

This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors. © 2009 Wiley Periodicals, Inc.

PageRank for ranking authors in co-citation networks

Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988–2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis. © 2009 Wiley Periodicals, Inc.

http://info.slis.indiana.edu/~dingying/Publication/ApplyingCentralityPreliminary.pdf

Applying centrality measures to impact analysis: A coauthorship network analysis

Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties, with the aim to apply centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of twenty years (1988-2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness, betweenness, degree and PageRank) for authors in this network. We find out that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking, and suggest that centrality measures can be useful indicators for impact analysis.

Google's PageRank has created a new synergy to information retrieval for a better ranking of Web pages. It ranks documents depending on the topology of the graphs and the weights of the nodes. PageRank has significantly advanced the field of information retrieval and keeps Google ahead of competitors in the search engine market. It has been deployed in bibliometrics to evaluate research impact, yet few of these studies focus on the important impact of the damping factor (d) for ranking purposes. This paper studies how varied damping factors in the PageRank algorithm can provide additional insight into the ranking of authors in an author co-citation network. Furthermore, we propose weighted PageRank algorithms. We select 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculate the ranks of these 108 authors based on PageRank with damping factor ranging from 0.05 to 0.95. In order to test the relationship between these different measures, we compare PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank's with different damping factors and also with different PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index is not significantly correlated with centrality measures.

Erjia Yan

Papers

Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis

PageRank for ranking authors in co-citation networks

Applying centrality measures to impact analysis: A coauthorship network analysis

Applying centrality measures to impact analysis: A coauthorship network analysis

PageRank for ranking authors in co-citation networks