1,733 research outputs found
Applying weighted PageRank to author citation networks
This paper aims to identify whether different weighted PageRank algorithms
can be applied to author citation networks to measure the popularity and
prestige of a scholar from a citation perspective. Information Retrieval (IR)
was selected as a test field and data from 1956-2008 were collected from Web of
Science (WOS). Weighted PageRank with citation and publication as weighted
vectors were calculated on author citation networks. The results indicate that
both popularity rank and prestige rank were highly correlated with the weighted
PageRank. Principal Component Analysis (PCA) was conducted to detect
relationships among these different measures. For capturing prize winners
within the IR field, prestige rank outperformed all the other measures.Comment: 19 pages, 4 figures, 5 table
Applying weighted PageRank to author citation networks
Abstract This paper aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information Retrieval (IR) was selected as a test field and data from were collected from Web of Science (WOS). Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal Component Analysis (PCA) was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures
Network-based ranking in social systems: three challenges
Ranking algorithms are pervasive in our increasingly digitized societies,
with important real-world applications including recommender systems, search
engines, and influencer marketing practices. From a network science
perspective, network-based ranking algorithms solve fundamental problems
related to the identification of vital nodes for the stability and dynamics of
a complex system. Despite the ubiquitous and successful applications of these
algorithms, we argue that our understanding of their performance and their
applications to real-world problems face three fundamental challenges: (i)
Rankings might be biased by various factors; (2) their effectiveness might be
limited to specific problems; and (3) agents' decisions driven by rankings
might result in potentially vicious feedback mechanisms and unhealthy systemic
consequences. Methods rooted in network science and agent-based modeling can
help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure
Popular and/or Prestigious? Measures of Scholarly Esteem
Citation analysis does not generally take the quality of citations into
account: all citations are weighted equally irrespective of source. However, a
scholar may be highly cited but not highly regarded: popularity and prestige
are not identical measures of esteem. In this study we define popularity as the
number of times an author is cited and prestige as the number of times an
author is cited by highly cited papers. Information Retrieval (IR) is the test
field. We compare the 40 leading researchers in terms of their popularity and
prestige over time. Some authors are ranked high on prestige but not on
popularity, while others are ranked high on popularity but not on prestige. We
also relate measures of popularity and prestige to date of Ph.D. award, number
of key publications, organizational affiliation, receipt of prizes/honors, and
gender.Comment: 26 pages, 5 figure
Grammar-Based Random Walkers in Semantic Networks
Semantic networks qualify the meaning of an edge relating any two vertices.
Determining which vertices are most "central" in a semantic network is
difficult because one relationship type may be deemed subjectively more
important than another. For this reason, research into semantic network metrics
has focused primarily on context-based rankings (i.e. user prescribed
contexts). Moreover, many of the current semantic network metrics rank semantic
associations (i.e. directed paths between two vertices) and not the vertices
themselves. This article presents a framework for calculating semantically
meaningful primary eigenvector-based metrics such as eigenvector centrality and
PageRank in semantic networks using a modified version of the random walker
model of Markov chain analysis. Random walkers, in the context of this article,
are constrained by a grammar, where the grammar is a user defined data
structure that determines the meaning of the final vertex ranking. The ideas in
this article are presented within the context of the Resource Description
Framework (RDF) of the Semantic Web initiative.Comment: First draft of manuscript originally written in November 200
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