103,545 research outputs found
Non-parametric Bayesian modeling of complex networks
Modeling structure in complex networks using Bayesian non-parametrics makes
it possible to specify flexible model structures and infer the adequate model
complexity from the observed data. This paper provides a gentle introduction to
non-parametric Bayesian modeling of complex networks: Using an infinite mixture
model as running example we go through the steps of deriving the model as an
infinite limit of a finite parametric model, inferring the model parameters by
Markov chain Monte Carlo, and checking the model's fit and predictive
performance. We explain how advanced non-parametric models for complex networks
can be derived and point out relevant literature
A Latent Parameter Node-Centric Model for Spatial Networks
Spatial networks, in which nodes and edges are embedded in space, play a
vital role in the study of complex systems. For example, many social networks
attach geo-location information to each user, allowing the study of not only
topological interactions between users, but spatial interactions as well. The
defining property of spatial networks is that edge distances are associated
with a cost, which may subtly influence the topology of the network. However,
the cost function over distance is rarely known, thus developing a model of
connections in spatial networks is a difficult task.
In this paper, we introduce a novel model for capturing the interaction
between spatial effects and network structure. Our approach represents a unique
combination of ideas from latent variable statistical models and spatial
network modeling. In contrast to previous work, we view the ability to form
long/short-distance connections to be dependent on the individual nodes
involved. For example, a node's specific surroundings (e.g. network structure
and node density) may make it more likely to form a long distance link than
other nodes with the same degree. To capture this information, we attach a
latent variable to each node which represents a node's spatial reach. These
variables are inferred from the network structure using a Markov Chain Monte
Carlo algorithm.
We experimentally evaluate our proposed model on 4 different types of
real-world spatial networks (e.g. transportation, biological, infrastructure,
and social). We apply our model to the task of link prediction and achieve up
to a 35% improvement over previous approaches in terms of the area under the
ROC curve. Additionally, we show that our model is particularly helpful for
predicting links between nodes with low degrees. In these cases, we see much
larger improvements over previous models
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