6,425 research outputs found
Multiplex Communities and the Emergence of International Conflict
Advances in community detection reveal new insights into multiplex and
multilayer networks. Less work, however, investigates the relationship between
these communities and outcomes in social systems. We leverage these advances to
shed light on the relationship between the cooperative mesostructure of the
international system and the onset of interstate conflict. We detect
communities based upon weaker signals of affinity expressed in United Nations
votes and speeches, as well as stronger signals observed across multiple layers
of bilateral cooperation. Communities of diplomatic affinity display an
expected negative relationship with conflict onset. Ties in communities based
upon observed cooperation, however, display no effect under a standard model
specification and a positive relationship with conflict under an alternative
specification. These results align with some extant hypotheses but also point
to a paucity in our understanding of the relationship between community
structure and behavioral outcomes in networks.Comment: arXiv admin note: text overlap with arXiv:1802.0039
Multirelational Organization of Large-scale Social Networks in an Online World
The capacity to collect fingerprints of individuals in online media has
revolutionized the way researchers explore human society. Social systems can be
seen as a non-linear superposition of a multitude of complex social networks,
where nodes represent individuals and links capture a variety of different
social relations. Much emphasis has been put on the network topology of social
interactions, however, the multi-dimensional nature of these interactions has
largely been ignored in empirical studies, mostly because of lack of data.
Here, for the first time, we analyze a complete, multi-relational, large social
network of a society consisting of the 300,000 odd players of a massive
multiplayer online game. We extract networks of six different types of
one-to-one interactions between the players. Three of them carry a positive
connotation (friendship, communication, trade), three a negative (enmity, armed
aggression, punishment). We first analyze these types of networks as separate
entities and find that negative interactions differ from positive interactions
by their lower reciprocity, weaker clustering and fatter-tail degree
distribution. We then proceed to explore how the inter-dependence of different
network types determines the organization of the social system. In particular
we study correlations and overlap between different types of links and
demonstrate the tendency of individuals to play different roles in different
networks. As a demonstration of the power of the approach we present the first
empirical large-scale verification of the long-standing structural balance
theory, by focusing on the specific multiplex network of friendship and enmity
relations.Comment: 7 pages, 5 figures, accepted for publication in PNA
Metric projection for dynamic multiplex networks
Evolving multiplex networks are a powerful model for representing the
dynamics along time of different phenomena, such as social networks, power
grids, biological pathways. However, exploring the structure of the multiplex
network time series is still an open problem. Here we propose a two-steps
strategy to tackle this problem based on the concept of distance (metric)
between networks. Given a multiplex graph, first a network of networks is built
for each time steps, and then a real valued time series is obtained by the
sequence of (simple) networks by evaluating the distance from the first element
of the series. The effectiveness of this approach in detecting the occurring
changes along the original time series is shown on a synthetic example first,
and then on the Gulf dataset of political events
Revisiting Interval Graphs for Network Science
The vertices of an interval graph represent intervals over a real line where
overlapping intervals denote that their corresponding vertices are adjacent.
This implies that the vertices are measurable by a metric and there exists a
linear structure in the system. The generalization is an embedding of a graph
onto a multi-dimensional Euclidean space and it was used by scientists to study
the multi-relational complexity of ecology. However the research went out of
fashion in the 1980s and was not revisited when Network Science recently
expressed interests with multi-relational networks known as multiplexes. This
paper studies interval graphs from the perspective of Network Science
Quantifying dynamical spillover in co-evolving multiplex networks
Multiplex networks (a system of multiple networks that have different types
of links but share a common set of nodes) arise naturally in a wide spectrum of
fields. Theoretical studies show that in such multiplex networks, correlated
edge dynamics between the layers can have a profound effect on dynamical
processes. However, how to extract the correlations from real-world systems is
an outstanding challenge. Here we provide a null model based on Markov chains
to quantify correlations in edge dynamics found in longitudinal data of
multiplex networks. We use this approach on two different data sets: the
network of trade and alliances between nation states, and the email and
co-commit networks between developers of open source software. We establish the
existence of "dynamical spillover" showing the correlated formation (or
deletion) of edges of different types as the system evolves. The details of the
dynamics over time provide insight into potential causal pathways
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
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