124,798 research outputs found
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
Decoding Information from noisy, redundant, and intentionally-distorted sources
Advances in information technology reduce barriers to information
propagation, but at the same time they also induce the information overload
problem. For the making of various decisions, mere digestion of the relevant
information has become a daunting task due to the massive amount of information
available. This information, such as that generated by evaluation systems
developed by various web sites, is in general useful but may be noisy and may
also contain biased entries. In this study, we establish a framework to
systematically tackle the challenging problem of information decoding in the
presence of massive and redundant data. When applied to a voting system, our
method simultaneously ranks the raters and the ratees using only the evaluation
data, consisting of an array of scores each of which represents the rating of a
ratee by a rater. Not only is our appraoch effective in decoding information,
it is also shown to be robust against various hypothetical types of noise as
well as intentional abuses.Comment: 19 pages, 5 figures, accepted for publication in Physica
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