215 research outputs found
Emergence of good conduct, scaling and Zipf laws in human behavioral sequences in an online world
We study behavioral action sequences of players in a massive multiplayer
online game. In their virtual life players use eight basic actions which allow
them to interact with each other. These actions are communication, trade,
establishing or breaking friendships and enmities, attack, and punishment. We
measure the probabilities for these actions conditional on previous taken and
received actions and find a dramatic increase of negative behavior immediately
after receiving negative actions. Similarly, positive behavior is intensified
by receiving positive actions. We observe a tendency towards anti-persistence
in communication sequences. Classifying actions as positive (good) and negative
(bad) allows us to define binary 'world lines' of lives of individuals.
Positive and negative actions are persistent and occur in clusters, indicated
by large scaling exponents alpha~0.87 of the mean square displacement of the
world lines. For all eight action types we find strong signs for high levels of
repetitiveness, especially for negative actions. We partition behavioral
sequences into segments of length n (behavioral `words' and 'motifs') and study
their statistical properties. We find two approximate power laws in the word
ranking distribution, one with an exponent of kappa-1 for the ranks up to 100,
and another with a lower exponent for higher ranks. The Shannon n-tuple
redundancy yields large values and increases in terms of word length, further
underscoring the non-trivial statistical properties of behavioral sequences. On
the collective, societal level the timeseries of particular actions per day can
be understood by a simple mean-reverting log-normal model.Comment: 6 pages, 5 figure
Lightweight Interactions for Reciprocal Cooperation in a Social Network Game
The construction of reciprocal relationships requires cooperative
interactions during the initial meetings. However, cooperative behavior with
strangers is risky because the strangers may be exploiters. In this study, we
show that people increase the likelihood of cooperativeness of strangers by
using lightweight non-risky interactions in risky situations based on the
analysis of a social network game (SNG). They can construct reciprocal
relationships in this manner. The interactions involve low-cost signaling
because they are not generated at any cost to the senders and recipients.
Theoretical studies show that low-cost signals are not guaranteed to be
reliable because the low-cost signals from senders can lie at any time.
However, people used low-cost signals to construct reciprocal relationships in
an SNG, which suggests the existence of mechanisms for generating reliable,
low-cost signals in human evolution.Comment: 13 pages, 2 figure
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
Long-Term Evolution of Massive Black Hole Binaries. III. Binary Evolution in Collisional Nuclei
[Abridged] In galactic nuclei with sufficiently short relaxation times,
binary supermassive black holes can evolve beyond their stalling radii via
continued interaction with stars. We study this "collisional" evolutionary
regime using both fully self-consistent N-body integrations and approximate
Fokker-Planck models. The N-body integrations employ particle numbers up to
0.26M and a direct-summation potential solver; close interactions involving the
binary are treated using a new implementation of the Mikkola-Aarseth chain
regularization algorithm. Even at these large values of N, two-body scattering
occurs at high enough rates in the simulations that they can not be simply
scaled to the large-N regime of real galaxies. The Fokker-Planck model is used
to bridge this gap; it includes, for the first time, binary-induced changes in
the stellar density and potential. The Fokker-Planck model is shown to
accurately reproduce the results of the N-body integrations, and is then
extended to the much larger N regime of real galaxies. Analytic expressions are
derived that accurately reproduce the time dependence of the binary semi-major
axis as predicted by the Fokker-Planck model. Gravitational wave coalescence is
shown to occur in <10 Gyr in nuclei with velocity dispersions below about 80
km/s. Formation of a core results from a competition between ejection of stars
by the binary and re-supply of depleted orbits via two-body scattering. Mass
deficits as large as ~4 times the binary mass are produced before coalescence.
After the two black holes coalesce, a Bahcall-Wolf cusp appears around the
single hole in one relaxation time, resulting in a nuclear density profile
consisting of a flat core with an inner, compact cluster, similar to what is
observed at the centers of low-luminosity spheroids.Comment: 21 page
Understanding mobility in a social petri dish
Despite the recent availability of large data sets on human movements, a full understanding of the rules governing motion within social systems is still missing, due to incomplete information on the socio-economic factors and to often limited spatio-temporal resolutions. Here we study an entire society of individuals, the players of an online-game, with complete information on their movements in a network-shaped universe and on their social and economic interactions. Such a "socio-economic laboratory" allows to unveil the intricate interplay of spatial constraints, social and economic factors, and patterns of mobility. We find that the motion of individuals is not only constrained by physical distances, but also strongly shaped by the presence of socio-economic areas. These regions can be recovered perfectly by community detection methods solely based on the measured human dynamics. Moreover, we uncover that long-term memory in the time-order of visited locations is the essential ingredient for modeling the trajectories
79/81Br nuclear quadrupole resonance spectroscopic characterization of halogen bonds in supramolecular assemblies
Vertex importance extension of betweenness centrality algorithm
Variety of real-life structures can be simplified by a graph. Such simplification emphasizes the structure represented by vertices connected via edges. A common method for the analysis of the vertices importance in a network is betweenness centrality. The centrality is computed using the information about the shortest paths that exist in a graph. This approach puts the importance on the edges that connect the vertices. However, not all vertices are equal. Some of them might be more important than others or have more significant influence on the behavior of the network. Therefore, we introduce the modification of the betweenness centrality algorithm that takes into account the vertex importance. This approach allows the further refinement of the betweenness centrality score to fulfill the needs of the network better. We show this idea on an example of the real traffic network. We test the performance of the algorithm on the traffic network data from the city of Bratislava, Slovakia to prove that the inclusion of the modification does not hinder the original algorithm much. We also provide a visualization of the traffic network of the city of Ostrava, the Czech Republic to show the effect of the vertex importance adjustment. The algorithm was parallelized by MPI (http://www.mpi-forum.org/) and was tested on the supercomputer Salomon (https://docs.it4i.cz/) at IT4Innovations National Supercomputing Center, the Czech Republic.808726
A curious case of dynamic disorder in pyrrolidine rings elucidated by NMR crystallography
A pharmaceutical exhibits differing dynamics in crystallographically distinct pyrrolidine rings despite being nearly related by symmetry, with one performing ring inversions while the other is constrained to torsional librations. Using 13C solid-state magic-angle spinning (MAS) NMR and DFT calculations, we show that this contrast originates from C-H···H-C close contacts and less efficient C-H···π intermolecular interactions observed in the transition state of the constrained pyrrolidine ring, highlighting the influence of the crystallographic environment on the molecular motion
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
Core Collapse via Coarse Dynamic Renormalization
In the context of the recently developed "equation-free" approach to
computer-assisted analysis of complex systems, we extract the self-similar
solution describing core collapse of a stellar system from numerical
experiments. The technique allows us to side-step the core "bounce" that occurs
in direct N-body simulations due to the small-N correlations that develop in
the late stages of collapse, and hence to follow the evolution well into the
self-similar regime.Comment: 5 pages, 3 figure
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