268,240 research outputs found
Co-evolution of Selection and Influence in Social Networks
Many networks are complex dynamical systems, where both attributes of nodes
and topology of the network (link structure) can change with time. We propose a
model of co-evolving networks where both node at- tributes and network
structure evolve under mutual influence. Specifically, we consider a mixed
membership stochastic blockmodel, where the probability of observing a link
between two nodes depends on their current membership vectors, while those
membership vectors themselves evolve in the presence of a link between the
nodes. Thus, the network is shaped by the interaction of stochastic processes
describing the nodes, while the processes themselves are influenced by the
changing network structure. We derive an efficient variational inference
procedure for our model, and validate the model on both synthetic and
real-world data.Comment: In Proc. of the Twenty-Fifth Conference on Artificial Intelligence
(AAAI-11
Identifying collaboration dynamics of bipartite author-topic networks with the influences of interest changes
Knowing driving factors and understanding researcher behaviors from the dynamics of collaborations over time offer some insights, i.e. help funding agencies in designing research grant policies. We present longitudinal network analysis on the observed collaborations through co-authorship over 15 years. Since co-authors possibly influence researchers to have interest changes, by focusing on researchers who could become the influencer, we propose a stochastic actor-oriented model of bipartite (two-mode) author-topic networks from article metadata. Information of scientific fields or topics of article contents, which could represent the interests of researchers, are often unavailable in the metadata. Topic absence issue differentiates this work with other studies on collaboration dynamics from article metadata of title-abstract and author properties. Therefore, our works also include procedures to extract and map clustered keywords as topic substitution of research interests. Then, the next step is to generate panel-waves of co-author networks and bipartite author-topic networks for the longitudinal analysis. The proposed model is used to find the driving factors of co-authoring collaboration with the focus on researcher behaviors in interest changes. This paper investigates the dynamics in an academic social network setting using selected metadata of publicly-available crawled articles in interrelated domains of "natural language processing" and "information extraction". Based on the evidence of network evolution, researchers have a conformed tendency to co-author behaviors in publishing articles and exploring topics. Our results indicate the processes of selection and influence in forming co-author ties contribute some levels of social pressure to researchers. Our findings also discussed on how the co-author pressure accelerates the changes of interests and behaviors of the researchers
CAN OPINION BE STABLE IN AN OPEN NETWORK WITH HIERARCHY?AN AGENT-BASED MODEL OF THE COMMERCIAL COURT OF PARIS
The co-evolution of social networks and opinion formation has received increasing attention in recent years. As a contribution to the growing literature on this topic, we explore connections between empirical data representing the advice network of judges at the Commercial Court in Paris and an agent-based simulation protocol testing various hypotheses on the motives that drive agent behaviors. A previous work (Rouchier et al. 2007) had already modeled the dynamics of advice-seeking among judges and studied the implications of different rationality assumptions on the shape of the emerging network. Here, we add an influence model to the previously examined advice-seeking relationships in order to explore the possibility that there is a form of “culture” at the Court that harmonizes the opinions of members over time; we identify a set of relevant stylized facts, and we use new indicators to evaluate how agents choose with whom to interact within this framework. The basic assumptions we analyze are that they seek advice from senior judges who are higher up in the hierarchy, who enjoy high reputation, or who are similar to them. Our simulations test which criterion –or which combination of criteria– is most credible, by comparing both the properties of the emerging network and the dynamics of opinion at the Court to the stylized facts. Our results single out the combination of criteria that most likely guide individuals' selection of advisors and provide insight into their effects on opinion formation.Advice network ; Agent-Based Simulation ; Influence Model ; Opinion Dynamics ; Hierarchy ; Reputation
The co-evolution of emotional well-being with weak and strong friendship ties
Social ties are strongly related to well-being. But what characterizes this
relationship? This study investigates social mechanisms explaining how social
ties affect well-being through social integration and social influence, and how
well-being affects social ties through social selection. We hypothesize that
highly integrated individuals - those with more extensive and dense friendship
networks - report higher emotional well-being than others. Moreover, emotional
well-being should be influenced by the well-being of close friends. Finally,
well-being should affect friendship selection when individuals prefer others
with higher levels of well-being, and others whose well-being is similar to
theirs. We test our hypotheses using longitudinal social network and well-being
data of 117 individuals living in a graduate housing community. The application
of a novel extension of Stochastic Actor-Oriented Models for ordered networks
(ordered SAOMs) allows us to detail and test our hypotheses for weak- and
strong-tied friendship networks simultaneously. Results do not support our
social integration and social influence hypotheses but provide evidence for
selection: individuals with higher emotional well-being tend to have more
strong-tied friends, and there are homophily processes regarding emotional
well-being in strong-tied networks. Our study highlights the two-directional
relationship between social ties and well-being, and demonstrates the
importance of considering different tie strengths for various social processes
Open Dynamic Interaction Network: a cell-phone based platform for responsive EMA
The study of social networks is central to advancing our understanding of a wide range of phenomena in human societies. Social networks co-evolve concurrently alongside the individuals within them. Selection processes cause network structure to change in response to emerging similarities/differences between individuals. At the same time, diffusion processes occur as individuals influence one another when they interact across network links. Indeed, each network link is a logical abstraction that aggregates many short-lived pairwise interactions of interest that are being studied. Traditionally, network co-evolution is studied by periodically taking static snapshots of social networks using surveys. Unfortunately, participation incentives make surveys costly to deliver, which makes it impractical to collect snapshots at fine temporal resolution. On the other hand, collecting data at wider time intervals requires participants to perform error-prone recall about long periods of time. This creates a difficult research tradeoff between data cost and data quality. More recently, techniques of Ecological Momentary Assessment (EMA) have been developed, involving repeated sampling of subjects\u27 current behaviors and experiences in real time, in subjects\u27 natural environments. This thesis project describes the design, implementation, and validation of a new platform for responsive EMA. The Open Dynamic Interaction Network (ODIN) platform is a cost-effective and flexible cell-phone based platform to collect continuous time sensor data and deliver contextual surveys to a study population. ODIN allows social and behavioral health researchers to instrument study protocols by specifying both the questions to be asked and the rules governing when questions should be asked over the duration of the study. Researcher-specified rules can reference sensor data (e.g. time, GPS, accelerometer-based activity, Bluetooth-based proximity to other participants, etc), as well as the subject\u27s previous answers. ODIN is composed of four backend services, two web user interfaces, and an Android application. A pilot study was conducted over the course of 30 days with 16 participants to evaluate the system. The results obtained from the pilot show that the system successfully collects relevant data for the study as well as triggering questions according to the study needs.
Adviser: Jitender Deogun and Bilal Kha
Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure
Large-scale transitions in societies are associated with both individual
behavioural change and restructuring of the social network. These two factors
have often been considered independently, yet recent advances in social network
research challenge this view. Here we show that common features of societal
marginalization and clustering emerge naturally during transitions in a
co-evolutionary adaptive network model. This is achieved by explicitly
considering the interplay between individual interaction and a dynamic network
structure in behavioural selection. We exemplify this mechanism by simulating
how smoking behaviour and the network structure get reconfigured by changing
social norms. Our results are consistent with empirical findings: The
prevalence of smoking was reduced, remaining smokers were preferentially
connected among each other and formed increasingly marginalised clusters. We
propose that self-amplifying feedbacks between individual behaviour and dynamic
restructuring of the network are main drivers of the transition. This
generative mechanism for co-evolution of individual behaviour and social
network structure may apply to a wide range of examples beyond smoking.Comment: 16 pages, 5 figure
Coevolutionary games - a mini review
Prevalence of cooperation within groups of selfish individuals is puzzling in
that it contradicts with the basic premise of natural selection. Favoring
players with higher fitness, the latter is key for understanding the challenges
faced by cooperators when competing with defectors. Evolutionary game theory
provides a competent theoretical framework for addressing the subtleties of
cooperation in such situations, which are known as social dilemmas. Recent
advances point towards the fact that the evolution of strategies alone may be
insufficient to fully exploit the benefits offered by cooperative behavior.
Indeed, while spatial structure and heterogeneity, for example, have been
recognized as potent promoters of cooperation, coevolutionary rules can extend
the potentials of such entities further, and even more importantly, lead to the
understanding of their emergence. The introduction of coevolutionary rules to
evolutionary games implies, that besides the evolution of strategies, another
property may simultaneously be subject to evolution as well. Coevolutionary
rules may affect the interaction network, the reproduction capability of
players, their reputation, mobility or age. Here we review recent works on
evolutionary games incorporating coevolutionary rules, as well as give a
didactic description of potential pitfalls and misconceptions associated with
the subject. In addition, we briefly outline directions for future research
that we feel are promising, thereby particularly focusing on dynamical effects
of coevolutionary rules on the evolution of cooperation, which are still widely
open to research and thus hold promise of exciting new discoveries.Comment: 24 two-column pages, 10 figures; accepted for publication in
BioSystem
Leaders should not be conformists in evolutionary social dilemmas
The most common assumption in evolutionary game theory is that players should
adopt a strategy that warrants the highest payoff. However, recent studies
indicate that the spatial selection for cooperation is enhanced if an
appropriate fraction of the population chooses the most common rather than the
most profitable strategy within the interaction range. Such conformity might be
due to herding instincts or crowd behavior in humans and social animals. In a
heterogeneous population where individuals differ in their degree, collective
influence, or other traits, an unanswered question remains who should conform.
Selecting conformists randomly is the simplest choice, but it is neither a
realistic nor the optimal one. We show that, regardless of the source of
heterogeneity and game parametrization, socially the most favorable outcomes
emerge if the masses conform. On the other hand, forcing leaders to conform
significantly hinders the constructive interplay between heterogeneity and
coordination, leading to evolutionary outcomes that are worse still than if
conformists were chosen randomly. We conclude that leaders must be able to
create a following for network reciprocity to be optimally augmented by
conformity. In the opposite case, when leaders are castrated and made to
follow, the failure of coordination impairs the evolution of cooperation.Comment: 7 two-column pages, 4 figures; accepted for publication in Scientific
Reports [related work available at arXiv:1412.4113
Evolutionary game theory: Temporal and spatial effects beyond replicator dynamics
Evolutionary game dynamics is one of the most fruitful frameworks for
studying evolution in different disciplines, from Biology to Economics. Within
this context, the approach of choice for many researchers is the so-called
replicator equation, that describes mathematically the idea that those
individuals performing better have more offspring and thus their frequency in
the population grows. While very many interesting results have been obtained
with this equation in the three decades elapsed since it was first proposed, it
is important to realize the limits of its applicability. One particularly
relevant issue in this respect is that of non-mean-field effects, that may
arise from temporal fluctuations or from spatial correlations, both neglected
in the replicator equation. This review discusses these temporal and spatial
effects focusing on the non-trivial modifications they induce when compared to
the outcome of replicator dynamics. Alongside this question, the hypothesis of
linearity and its relation to the choice of the rule for strategy update is
also analyzed. The discussion is presented in terms of the emergence of
cooperation, as one of the current key problems in Biology and in other
disciplines.Comment: Review, 48 pages, 26 figure
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