7,983 research outputs found
On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs
Modern networks are of huge sizes as well as high dynamics, which challenges
the efficiency of community detection algorithms. In this paper, we study the
problem of overlapping community detection on distributed and dynamic graphs.
Given a distributed, undirected and unweighted graph, the goal is to detect
overlapping communities incrementally as the graph is dynamically changing. We
propose an efficient algorithm, called \textit{randomized Speaker-Listener
Label Propagation Algorithm} (rSLPA), based on the \textit{Speaker-Listener
Label Propagation Algorithm} (SLPA) by relaxing the probability distribution of
label propagation. Besides detecting high-quality communities, rSLPA can
incrementally update the detected communities after a batch of edge insertion
and deletion operations. To the best of our knowledge, rSLPA is the first
algorithm that can incrementally capture the same communities as those obtained
by applying the detection algorithm from the scratch on the updated graph.
Extensive experiments are conducted on both synthetic and real-world datasets,
and the results show that our algorithm can achieve high accuracy and
efficiency at the same time.Comment: A short version of this paper will be published as ICDE'2018 poste
Efficient Data Gathering in Wireless Sensor Networks Based on Matrix Completion and Compressive Sensing
Gathering data in an energy efficient manner in wireless sensor networks is
an important design challenge. In wireless sensor networks, the readings of
sensors always exhibit intra-temporal and inter-spatial correlations.
Therefore, in this letter, we use low rank matrix completion theory to explore
the inter-spatial correlation and use compressive sensing theory to take
advantage of intra-temporal correlation. Our method, dubbed MCCS, can
significantly reduce the amount of data that each sensor must send through
network and to the sink, thus prolong the lifetime of the whole networks.
Experiments using real datasets demonstrate the feasibility and efficacy of our
MCCS method
Fractional stochastic differential equations satisfying fluctuation-dissipation theorem
We propose in this work a fractional stochastic differential equation (FSDE)
model consistent with the over-damped limit of the generalized Langevin
equation model. As a result of the `fluctuation-dissipation theorem', the
differential equations driven by fractional Brownian noise to model memory
effects should be paired with Caputo derivatives, and this FSDE model should be
understood in an integral form. We establish the existence of strong solutions
for such equations and discuss the ergodicity and convergence to Gibbs measure.
In the linear forcing regime, we show rigorously the algebraic convergence to
Gibbs measure when the `fluctuation-dissipation theorem' is satisfied, and this
verifies that satisfying `fluctuation-dissipation theorem' indeed leads to the
correct physical behavior. We further discuss possible approaches to analyze
the ergodicity and convergence to Gibbs measure in the nonlinear forcing
regime, while leave the rigorous analysis for future works. The FSDE model
proposed is suitable for systems in contact with heat bath with power-law
kernel and subdiffusion behaviors
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