Building and Analyzing a Global Co-Authorship Network Using Google Scholar Data

Yang, Chen; Ding, Cong; Hu, Jiawen; Chen, Ruichuan; Hui, Pan; Fu, Xiaoming

doi:10.1145/3041021.3053056

Cited by 25 publications

(15 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Much research has been done in various fields using Google Scholar data. For instance, Chen et al (2017) collected more than 400,000 Google Scholar profiles across various disciplines and analyzed the demography of these scholars. A co-authorship network was built to study the collaboration among authors and its resulting link to citation metrics.…”

Section: Working With Google Scholar Datamentioning

confidence: 99%

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

Yang¹,

Kleissl

Gueymard³

et al. 2018

Solar Energy

393

164

View full text Add to dashboard Cite

Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as "solar forecasting" for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and organizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineering topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.

show abstract

Section: Working With Google Scholar Datamentioning

confidence: 99%

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

Yang¹,

Kleissl

Gueymard³

et al. 2018

Solar Energy

393

164

View full text Add to dashboard Cite

show abstract

“…Two scholars are connected by an edge if they have co-authored a paper together. The data were originally collected in May of 2015 by Chen et al [2017]. 4 To gather the data from Google Scholar, their team employed a web crawler to search each letter in the English alphabet using the Google Scholar "search authors" feature, and then worked through each search result and parsed the HTML to get all of the scholars' metadata and publication records.…”

Section: Network Datasetsmentioning

confidence: 99%

Clustering via Information Access in a Network

Beilinson,

Ulzii-Orshikh,

Bashardoust

et al. 2020

Preprint

View full text Add to dashboard Cite

Information flow in a graph (say, a social network) has typically been modeled using standard influence propagation methods, with the goal of determining the most effective ways to spread information widely. More recently, researchers have begun to study the differing access to information of individuals within a network. This previous work suggests that information access is itself a potential aspect of privilege based on network position. While concerns about fairness usually focus on differences between demographic groups, characterizing network position may itself give rise to new groups for study. But how do we characterize position? Rather than using standard grouping methods for graph clustering, we design and explore a clustering that explicitly incorporates models of how information flows on a network. Our goal is to identify clusters of nodes that are similar based on their access to information across the network. We show, both formally and experimentally, that the resulting clustering method is a new approach to network clustering. Using a wide variety of datasets, our experiments show that the introduced clustering technique clusters individuals together who are similar based on an external information access measure.

show abstract

“…The giant (i.e., largest) component of the network contains 13520 author nodes, 16604 publication nodes, and 38169 edges. Often, in studies of collaboration networks, one "projects" the network to a single type of node (e.g., by keeping the author nodes and replacing the publication nodes with cliques of edges between those authors) (Araújo et al 2017;Karimi et al 2018;Liu et al 2005;Abbasi et al 2012;Chen et al 2017;Jadidi et al 2018). However, we kept the original bipartite structure in all of our analysis, as projecting the network loses structural information (Kitsak 2017) (e.g, a publication with three authors would be identical to three publications between all pairs of those authors).…”

Section: Data Acquisition Data Cleaning and Gender Assignmentsmentioning

confidence: 99%

Gender and collaboration patterns in a temporal scientific authorship network

et al. 2019

View full text Add to dashboard Cite

One can point to a variety of historical milestones for gender equality in STEM (science, technology, engineering, and mathematics), however, the practical effects are gradual and ongoing. It is important to quantify gender differences in subdomains of scientific work in order to detect potential biases and to monitor progress. In this work, we studied the relevance of gender in scientific collaboration patterns in the Institute for Operations Research and the Management Sciences (INFORMS), a professional organization with sixteen peer-reviewed journals. We constructed a large temporal bipartite network between authors and publications, using the organization's publication data from 1952 to 2016, and augmented the author nodes with gender labels. We characterized differences in several basic statistics of this network over time, highlighting how they change with respect to relevant historical events. We found a steady increase in participation by women (e.g., fraction of authorships by women and of new women authors) starting ∼1980. However, women still comprise less than 25% of the INFORMS society, and are additionally underrepresented among authors with many publications. Finally, we describe a methodology for quantifying differences in the role that authorships by women and men play in the overall connectivity of the network. Specifically, we propose a degree-preserving temporal and geometric null model with emergent communities. We use two measures of edge importance related to diffusion throughout the network, namely effective resistance and edge contraction importance to quantify gender differences in collaboration patterns that go beyond differences in local statistics.

show abstract

Building and Analyzing a Global Co-Authorship Network Using Google Scholar Data

Cited by 25 publications

References 16 publications

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

Clustering via Information Access in a Network

Gender and collaboration patterns in a temporal scientific authorship network

Contact Info

Product

Resources

About