In this thesis, we primarily introduce two ranking methods in terms
of scientific networks and markets, respectively. First, considering
the exponential growth in the number of academic researchers,
identifying the highest quality papers is a very demanding task for
editors of scientific journals. While several measures exist to
evaluate a paper's impact post-publication, the challenge of
determining the potential impact of a manuscript during the
review process remains an understudied issue. In Section
\ref{sec:4}, we propose a reviewer-reputation ranking algorithm to
identify high-quality papers based on paper citations, where a
reviewer’s reputation is computed from the correlation between
their past ratings and the current number of citations received by
the papers they have evaluated. During the review process,
reviewers with high reputation scores are given more weight to
determine the quality of papers. We test the algorithm on an
artificial network with 200 reviewers and 600 papers, as well as on
the American Physical Society (APS) data set, including in the
analysis 308,243 papers and 274,154 mutual citations. We compare
our approach with two existing methods, demonstrating that our
algorithm significantly outperforms the others in identifying
manuscripts with the highest quality. Our findings have the
potential to enhance the impact of scientific journals, thereby
contributing to academic and scientific progress.

Second, We focus on a centralized platform in online markets that
help buyers and sellers find each other and reduce information
asymmetries. To better understand the role of an intermediary on
market outcomes, we propose a new platform design model
whose foundation rests on the tools developed by physicists
working on complex systems. In this model, the platform can
decide whether to rank the visibility of products based on the
criteria of higher-quality products or higher fees paid by
companies. Our framework allows us to study the influence of
different platform strategies on player payoffs in a market with
partially informed consumers. We find a fundamental market
failure: the optimal platform strategy minimizes social welfare.
Therefore, consumer search within the platform must be driven by
a sub-optimal algorithm that solves the trade-off between the cost
of fees charged by the platform and a high transaction volume.