132 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
Emergence of product differentiation from consumer heterogeneity and asymmetric information
We introduce a fully probabilistic framework of consumer product choice based
on quality assessment. It allows us to capture many aspects of marketing such
as partial information asymmetry, quality differentiation, and product
placement in a supermarket.Comment: 12 pages, 12 figure
Zipf's Law Leads to Heaps' Law: Analyzing Their Relation in Finite-Size Systems
Background: Zipf's law and Heaps' law are observed in disparate complex
systems. Of particular interests, these two laws often appear together. Many
theoretical models and analyses are performed to understand their co-occurrence
in real systems, but it still lacks a clear picture about their relation.
Methodology/Principal Findings: We show that the Heaps' law can be considered
as a derivative phenomenon if the system obeys the Zipf's law. Furthermore, we
refine the known approximate solution of the Heaps' exponent provided the
Zipf's exponent. We show that the approximate solution is indeed an asymptotic
solution for infinite systems, while in the finite-size system the Heaps'
exponent is sensitive to the system size. Extensive empirical analysis on tens
of disparate systems demonstrates that our refined results can better capture
the relation between the Zipf's and Heaps' exponents. Conclusions/Significance:
The present analysis provides a clear picture about the relation between the
Zipf's law and Heaps' law without the help of any specific stochastic model,
namely the Heaps' law is indeed a derivative phenomenon from Zipf's law. The
presented numerical method gives considerably better estimation of the Heaps'
exponent given the Zipf's exponent and the system size. Our analysis provides
some insights and implications of real complex systems, for example, one can
naturally obtained a better explanation of the accelerated growth of scale-free
networks.Comment: 15 pages, 6 figures, 1 Tabl
Empirical studies on the network of social groups: the case of Tencent QQ
Participation in social groups are important but the collective behaviors of
human as a group are difficult to analyze due to the difficulties to quantify
ordinary social relation, group membership, and to collect a comprehensive
dataset. Such difficulties can be circumvented by analyzing online social
networks. In this paper, we analyze a comprehensive dataset obtained from
Tencent QQ, an instant messenger with the highest market share in China.
Specifically, we analyze three derivative networks involving groups and their
members -- the hypergraph of groups, the network of groups and the user network
-- to reveal social interactions at microscopic and mesoscopic level. Our
results uncover interesting behaviors on the growth of user groups, the
interactions between groups, and their relationship with member age and gender.
These findings lead to insights which are difficult to obtain in ordinary
social networks.Comment: 18 pages, 9 figure
Network higher-order structure dismantling
Diverse higher-order structures, foundational for supporting a network's
"meta-functions", play a vital role in structure, functionality, and the
emergence of complex dynamics. Nevertheless, the problem of dismantling them
has been consistently overlooked. In this paper, we introduce the concept of
dismantling higher-order structures, with the objective of disrupting not only
network connectivity but also eradicating all higher-order structures in each
branch, thereby ensuring thorough functional paralysis. Given the diversity and
unknown specifics of higher-order structures, identifying and targeting them
individually is not practical or even feasible. Fortunately, their close
association with k-cores arises from their internal high connectivity. Thus, we
transform higher-order structure measurement into measurements on k-cores with
corresponding orders. Furthermore, we propose the Belief Propagation-guided
High-order Dismantling (BPDH) algorithm, minimizing dismantling costs while
achieving maximal disruption to connectivity and higher-order structures,
ultimately converting the network into a forest. BPDH exhibits the explosive
vulnerability of network higher-order structures, counterintuitively showcasing
decreasing dismantling costs with increasing structural complexity. Our
findings offer a novel approach for dismantling malignant networks, emphasizing
the substantial challenges inherent in safeguarding against such malicious
attacks.Comment: 14 pages, 5 figures, 2 table
The role of a matchmaker in buyer-vendor interactions
We consider a simple market where a vendor offers multiple variants of a certain product and preferences of both the vendor and potential buyers are heterogeneous and possibly even antagonistic. Optimization of the joint benefit of the vendor and the buyers turns the toy market into a combinatorial matching problem. We compare the optimal solutions found with and without a matchmaker, examine the resulting inequality between the market participants, and study the impact of correlations on the syste
Predicting missing links via local information
Missing link prediction in networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely Common Neighbours, has the best overall performance, and the Adamic-Adar index performs second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbours. It is found that many links are assigned the same scores if only the information of the nearest neighbours is used. We therefore design another new measure exploiting information on the next nearest neighbours, which can remarkably enhance the prediction accurac
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