868 research outputs found
One-step Estimation of Networked Population Size: Respondent-Driven Capture-Recapture with Anonymity
Population size estimates for hidden and hard-to-reach populations are
particularly important when members are known to suffer from disproportion
health issues or to pose health risks to the larger ambient population in which
they are embedded. Efforts to derive size estimates are often frustrated by a
range of factors that preclude conventional survey strategies, including social
stigma associated with group membership or members' involvement in illegal
activities.
This paper extends prior research on the problem of network population size
estimation, building on established survey/sampling methodologies commonly used
with hard-to-reach groups. Three novel one-step, network-based population size
estimators are presented, to be used in the context of uniform random sampling,
respondent-driven sampling, and when networks exhibit significant clustering
effects. Provably sufficient conditions for the consistency of these estimators
(in large configuration networks) are given. Simulation experiments across a
wide range of synthetic network topologies validate the performance of the
estimators, which are seen to perform well on a real-world location-based
social networking data set with significant clustering. Finally, the proposed
schemes are extended to allow them to be used in settings where participant
anonymity is required. Systematic experiments show favorable tradeoffs between
anonymity guarantees and estimator performance.
Taken together, we demonstrate that reasonable population estimates can be
derived from anonymous respondent driven samples of 250-750 individuals, within
ambient populations of 5,000-40,000. The method thus represents a novel and
cost-effective means for health planners and those agencies concerned with
health and disease surveillance to estimate the size of hidden populations.
Limitations and future work are discussed in the concluding section
Graphlet and Orbit Computation on Heterogeneous Graphs
Many applications, ranging from natural to social sciences, rely on graphlet
analysis for the intuitive and meaningful characterization of networks
employing micro-level structures as building blocks. However, it has not been
thoroughly explored in heterogeneous graphs, which comprise various types of
nodes and edges. Finding graphlets and orbits for heterogeneous graphs is
difficult because of the heterogeneity and abundance of semantic information.
We consider heterogeneous graphs, which can be treated as colored graphs. By
applying the canonical label technique, we determine the graph isomorphism
problem with multiple states on nodes and edges. With minimal parameters, we
build all non-isomorphic graphs and associated orbits. We provide a Python
package that can be used to generate orbits for colored directed graphs and
determine the frequency of orbit occurrence. Finally, we provide four examples
to illustrate the use of the Python package.Comment: 13 pages, 7 figure
Group-size dependent synergy in heterogeneous populations
When people collaborate, they expect more in return than a simple sum of
their efforts. This observation is at the heart of the so-called public goods
game, where the participants' contributions are multiplied by an synergy
factor before they are distributed among group members. However, a larger group
could be more effective, which can be described by a larger synergy factor. To
elaborate on the possible consequences, in this study, we introduce a model
where the population has different sizes of groups, and the applied synergy
factor depends on the size of the group. We examine different options when the
increment of is linear, slow, or sudden, but in all cases, the cooperation
level is higher than that in a population where the homogeneous factor is
used. In the latter case, smaller groups perform better; however, this behavior
is reversed when synergy increases for larger groups. Hence, the entire
community benefits because larger groups are rewarded better. Notably, a
similar qualitative behavior can be observed for other heterogeneous
topologies, including scale-free interaction graphs.Comment: 9 pages, 6 figures, accepted by Chaos, Solitons and Fractals: the
Interdisciplinary Journal of Nonlinear Science, and Nonequilibrium and
Complex Phenomen
Dynamics and Social Clustering on Coevolving Networks
Complex networks offer a powerful conceptual framework for the description and analysis of many real world systems. Many processes have been formed into networks in the area of random graphs, and the dynamics of networks have been studied. These two mechanisms combined creates an adaptive or coevolving network -- a network whose edges change adaptively with respect to its states, bringing a dynamical interaction between the state of nodes and the topology of the network. We study three binary-state dynamics in the context of opinion formation, disease propagation and evolutionary games of networks. We try to understand how the network structure affects the status of individuals, and how the behavior of individuals, in turn, affects the overall network structure. We focus our investigation on social clustering, since this is one of the central properties of social networks, arising due to the ubiquitous tendency among individuals to connect to friends of a friend, and can significantly impact a coevolving network system. Introducing rewiring models with transitivity reinforcement, we investigate how the mechanism affects network dynamics and the clustering structure of the networks. We perform Monte Carlo simulations to explore the parameter space of each model. By applying improved compartmental formalism methods, including approximate master equations, our semi-analytical approximation generally provide accurate predictions of the final states of the networks, degree distributions, and evolution of fundamental quantities. Different levels of semi-analytical estimation are compared.Doctor of Philosoph
Restoring spatial cooperation with myopic agents in a three-strategy social dilemma
Introducing strategy complexity into the basic conflict of cooperation and
defection is a natural response to avoid the tragedy of the common state. As an
intermediate approach, quasi-cooperators were recently suggested to address the
original problem. In this study, we test its vitality in structured populations
where players have fixed partners. Naively, the latter condition should support
cooperation unambiguously via enhanced network reciprocity. However, the
opposite is true because the spatial structure may provide a humbler
cooperation level than a well-mixed population. This unexpected behavior can be
understood if we consider that at a certain parameter interval the original
prisoner's dilemma game is transformed into a snow-drift game. If we replace
the original imitating strategy protocol by assuming myopic players, the
spatial population becomes a friendly environment for cooperation. This
observation is valid in a huge region of parameter space. This study highlights
that spatial structure can reveal a new aspect of social dilemmas when strategy
complexity is introduced.Comment: 9 pages, 4 figures, accepted by Applied Mathematics and Computatio
A method for assessing the success and failure of community-level interventions in the presence of network diffusion, social reinforcement, and related social effects
Prevention and intervention work done within community settings often face
unique analytic challenges for rigorous evaluations. Since community prevention
work (often geographically isolated) cannot be controlled in the same way other
prevention programs and these communities have an increased level of
interpersonal interactions, rigorous evaluations are needed. Even when the
`gold standard' randomized control trials are implemented within community
intervention work, the threats to internal validity can be called into question
given informal social spread of information in closed network settings. A new
prevention evaluation method is presented here to disentangle the social
influences assumed to influence prevention effects within communities. We
formally introduce the method and it's utility for a suicide prevention program
implemented in several Alaska Native villages. The results show promise to
explore eight sociological measures of intervention effects in the face of
social diffusion, social reinforcement, and direct treatment. Policy and
research implication are discussed.Comment: 18 pages, 5 figure
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