25,629 research outputs found
Communities in Networks
We survey some of the concepts, methods, and applications of community
detection, which has become an increasingly important area of network science.
To help ease newcomers into the field, we provide a guide to available
methodology and open problems, and discuss why scientists from diverse
backgrounds are interested in these problems. As a running theme, we emphasize
the connections of community detection to problems in statistical physics and
computational optimization.Comment: survey/review article on community structure in networks; published
version is available at
http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd
Spectral Attention-Driven Intelligent Target Signal Identification on a Wideband Spectrum
This paper presents a spectral attention-driven reinforcement learning based
intelligent method for effective and efficient detection of important signals
in a wideband spectrum. In the work presented in this paper, it is assumed that
the modulation technique used is available as a priori knowledge of the
targeted important signal. The proposed spectral attention-driven intelligent
method is consists of two main components, a spectral correlation function
(SCF) based spectral visualization scheme and a spectral attention-driven
reinforcement learning mechanism that adaptively selects the spectrum range and
implements the intelligent signal detection. Simulations illustrate that the
proposed method can achieve high accuracy of signal detection while observation
of spectrum is limited to few ranges via effectively selecting the spectrum
ranges to be observed. Furthermore, the proposed spectral attention-driven
machine learning method can lead to an efficient adaptive intelligent spectrum
sensor designs in cognitive radio (CR) receivers.Comment: 6 pages, 11 figure
The stability of a graph partition: A dynamics-based framework for community detection
Recent years have seen a surge of interest in the analysis of complex
networks, facilitated by the availability of relational data and the
increasingly powerful computational resources that can be employed for their
analysis. Naturally, the study of real-world systems leads to highly complex
networks and a current challenge is to extract intelligible, simplified
descriptions from the network in terms of relevant subgraphs, which can provide
insight into the structure and function of the overall system.
Sparked by seminal work by Newman and Girvan, an interesting line of research
has been devoted to investigating modular community structure in networks,
revitalising the classic problem of graph partitioning.
However, modular or community structure in networks has notoriously evaded
rigorous definition. The most accepted notion of community is perhaps that of a
group of elements which exhibit a stronger level of interaction within
themselves than with the elements outside the community. This concept has
resulted in a plethora of computational methods and heuristics for community
detection. Nevertheless a firm theoretical understanding of most of these
methods, in terms of how they operate and what they are supposed to detect, is
still lacking to date.
Here, we will develop a dynamical perspective towards community detection
enabling us to define a measure named the stability of a graph partition. It
will be shown that a number of previously ad-hoc defined heuristics for
community detection can be seen as particular cases of our method providing us
with a dynamic reinterpretation of those measures. Our dynamics-based approach
thus serves as a unifying framework to gain a deeper understanding of different
aspects and problems associated with community detection and allows us to
propose new dynamically-inspired criteria for community structure.Comment: 3 figures; published as book chapte
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