79,021 research outputs found
Topological properties and organizing principles of semantic networks
Interpreting natural language is an increasingly important task in computer
algorithms due to the growing availability of unstructured textual data.
Natural Language Processing (NLP) applications rely on semantic networks for
structured knowledge representation. The fundamental properties of semantic
networks must be taken into account when designing NLP algorithms, yet they
remain to be structurally investigated. We study the properties of semantic
networks from ConceptNet, defined by 7 semantic relations from 11 different
languages. We find that semantic networks have universal basic properties: they
are sparse, highly clustered, and many exhibit power-law degree distributions.
Our findings show that the majority of the considered networks are scale-free.
Some networks exhibit language-specific properties determined by grammatical
rules, for example networks from highly inflected languages, such as e.g.
Latin, German, French and Spanish, show peaks in the degree distribution that
deviate from a power law. We find that depending on the semantic relation type
and the language, the link formation in semantic networks is guided by
different principles. In some networks the connections are similarity-based,
while in others the connections are more complementarity-based. Finally, we
demonstrate how knowledge of similarity and complementarity in semantic
networks can improve NLP algorithms in missing link inference
Collaborative Training in Sensor Networks: A graphical model approach
Graphical models have been widely applied in solving distributed inference
problems in sensor networks. In this paper, the problem of coordinating a
network of sensors to train a unique ensemble estimator under communication
constraints is discussed. The information structure of graphical models with
specific potential functions is employed, and this thus converts the
collaborative training task into a problem of local training plus global
inference. Two important classes of algorithms of graphical model inference,
message-passing algorithm and sampling algorithm, are employed to tackle
low-dimensional, parametrized and high-dimensional, non-parametrized problems
respectively. The efficacy of this approach is demonstrated by concrete
examples
Statistical inference framework for source detection of contagion processes on arbitrary network structures
In this paper we introduce a statistical inference framework for estimating
the contagion source from a partially observed contagion spreading process on
an arbitrary network structure. The framework is based on a maximum likelihood
estimation of a partial epidemic realization and involves large scale
simulation of contagion spreading processes from the set of potential source
locations. We present a number of different likelihood estimators that are used
to determine the conditional probabilities associated to observing partial
epidemic realization with particular source location candidates. This
statistical inference framework is also applicable for arbitrary compartment
contagion spreading processes on networks. We compare estimation accuracy of
these approaches in a number of computational experiments performed with the
SIR (susceptible-infected-recovered), SI (susceptible-infected) and ISS
(ignorant-spreading-stifler) contagion spreading models on synthetic and
real-world complex networks
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