79,411 research outputs found
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
Dynamical origins of the community structure of multi-layer societies
Social structures emerge as a result of individuals managing a variety of
different of social relationships. Societies can be represented as highly
structured dynamic multiplex networks. Here we study the dynamical origins of
the specific community structures of a large-scale social multiplex network of
a human society that interacts in a virtual world of a massive multiplayer
online game. There we find substantial differences in the community structures
of different social actions, represented by the various network layers in the
multiplex. Community size distributions are either similar to a power-law or
appear to be centered around a size of 50 individuals. To understand these
observations we propose a voter model that is built around the principle of
triadic closure. It explicitly models the co-evolution of node- and
link-dynamics across different layers of the multiplex. Depending on link- and
node fluctuation rates, the model exhibits an anomalous shattered fragmentation
transition, where one layer fragments from one large component into many small
components. The observed community size distributions are in good agreement
with the predicted fragmentation in the model. We show that the empirical
pairwise similarities of network layers, in terms of link overlap and degree
correlations, practically coincide with the model. This suggests that several
detailed features of the fragmentation in societies can be traced back to the
triadic closure processes.Comment: 8 pages, 6 figure
Detection of the elite structure in a virtual multiplex social system by means of a generalized -core
Elites are subgroups of individuals within a society that have the ability
and means to influence, lead, govern, and shape societies. Members of elites
are often well connected individuals, which enables them to impose their
influence to many and to quickly gather, process, and spread information. Here
we argue that elites are not only composed of highly connected individuals, but
also of intermediaries connecting hubs to form a cohesive and structured
elite-subgroup at the core of a social network. For this purpose we present a
generalization of the -core algorithm that allows to identify a social core
that is composed of well-connected hubs together with their `connectors'. We
show the validity of the idea in the framework of a virtual world defined by a
massive multiplayer online game, on which we have complete information of
various social networks. Exploiting this multiplex structure, we find that the
hubs of the generalized -core identify those individuals that are high
social performers in terms of a series of indicators that are available in the
game. In addition, using a combined strategy which involves the generalized
-core and the recently introduced -core, the elites of the different
'nations' present in the game are perfectly identified as modules of the
generalized -core. Interesting sudden shifts in the composition of the elite
cores are observed at deep levels. We show that elite detection with the
traditional -core is not possible in a reliable way. The proposed method
might be useful in a series of more general applications, such as community
detection.Comment: 13 figures, 3 tables, 19 pages. Accepted for publication in PLoS ON
Analyses of a Virtual World
We present an overview of a series of results obtained from the analysis of
human behavior in a virtual environment. We focus on the massive multiplayer
online game (MMOG) Pardus which has a worldwide participant base of more than
400,000 registered players. We provide evidence for striking statistical
similarities between social structures and human-action dynamics in the real
and virtual worlds. In this sense MMOGs provide an extraordinary way for
accurate and falsifiable studies of social phenomena. We further discuss
possibilities to apply methods and concepts developed in the course of these
studies to analyse oral and written narratives.Comment: 16 pages, 7 figures. To appear in: "Maths Meets Myths:
Complexity-science approaches to folktales, myths, sagas, and histories."
Editors: R. Kenna, M. Mac Carron, P. Mac Carron. (Springer, 2016
Undermining and Strengthening Social Networks through Network Modification
Social networks have well documented effects at the individual and aggregate
level. Consequently it is often useful to understand how an attempt to
influence a network will change its structure and consequently achieve other
goals. We develop a framework for network modification that allows for
arbitrary objective functions, types of modification (e.g. edge weight
addition, edge weight removal, node removal, and covariate value change), and
recovery mechanisms (i.e. how a network responds to interventions). The
framework outlined in this paper helps both to situate the existing work on
network interventions but also opens up many new possibilities for intervening
in networks. In particular use two case studies to highlight the potential
impact of empirically calibrating the objective function and network recovery
mechanisms as well as showing how interventions beyond node removal can be
optimised. First, we simulate an optimal removal of nodes from the Noordin
terrorist network in order to reduce the expected number of attacks (based on
empirically predicting the terrorist collaboration network from multiple types
of network ties). Second, we simulate optimally strengthening ties within
entrepreneurial ecosystems in six developing countries. In both cases we
estimate ERGM models to simulate how a network will endogenously evolve after
intervention
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