28,194 research outputs found
Causal inference for social network data
We describe semiparametric estimation and inference for causal effects using
observational data from a single social network. Our asymptotic result is the
first to allow for dependence of each observation on a growing number of other
units as sample size increases. While previous methods have generally
implicitly focused on one of two possible sources of dependence among social
network observations, we allow for both dependence due to transmission of
information across network ties, and for dependence due to latent similarities
among nodes sharing ties. We describe estimation and inference for new causal
effects that are specifically of interest in social network settings, such as
interventions on network ties and network structure. Using our methods to
reanalyze the Framingham Heart Study data used in one of the most influential
and controversial causal analyses of social network data, we find that after
accounting for network structure there is no evidence for the causal effects
claimed in the original paper
Revealing networks from dynamics: an introduction
What can we learn from the collective dynamics of a complex network about its
interaction topology? Taking the perspective from nonlinear dynamics, we
briefly review recent progress on how to infer structural connectivity (direct
interactions) from accessing the dynamics of the units. Potential applications
range from interaction networks in physics, to chemical and metabolic
reactions, protein and gene regulatory networks as well as neural circuits in
biology and electric power grids or wireless sensor networks in engineering.
Moreover, we briefly mention some standard ways of inferring effective or
functional connectivity.Comment: Topical review, 48 pages, 7 figure
Reconstructing directed and weighted topologies of phase-locked oscillator networks
The formalism of complex networks is extensively employed to describe the
dynamics of interacting agents in several applications. The features of the
connections among the nodes in a network are not always provided beforehand,
hence the problem of appropriately inferring them often arises. Here, we
present a method to reconstruct directed and weighted topologies (REDRAW) of
networks of heterogeneous phase-locked nonlinear oscillators. We ultimately
plan on using REDRAW to infer the interaction structure in human ensembles
engaged in coordination tasks, and give insights into the overall behavior
Unsupervised robust nonparametric learning of hidden community properties
We consider learning of fundamental properties of communities in large noisy
networks, in the prototypical situation where the nodes or users are split into
two classes according to a binary property, e.g., according to their opinions
or preferences on a topic. For learning these properties, we propose a
nonparametric, unsupervised, and scalable graph scan procedure that is, in
addition, robust against a class of powerful adversaries. In our setup, one of
the communities can fall under the influence of a knowledgeable adversarial
leader, who knows the full network structure, has unlimited computational
resources and can completely foresee our planned actions on the network. We
prove strong consistency of our results in this setup with minimal assumptions.
In particular, the learning procedure estimates the baseline activity of normal
users asymptotically correctly with probability 1; the only assumption being
the existence of a single implicit community of asymptotically negligible
logarithmic size. We provide experiments on real and synthetic data to
illustrate the performance of our method, including examples with adversaries.Comment: Experiments with new types of adversaries adde
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