1,469 research outputs found
Guaranteeing Convergence of Iterative Skewed Voting Algorithms for Image Segmentation
In this paper we provide rigorous proof for the convergence of an iterative
voting-based image segmentation algorithm called Active Masks. Active Masks
(AM) was proposed to solve the challenging task of delineating punctate
patterns of cells from fluorescence microscope images. Each iteration of AM
consists of a linear convolution composed with a nonlinear thresholding; what
makes this process special in our case is the presence of additive terms whose
role is to "skew" the voting when prior information is available. In real-world
implementation, the AM algorithm always converges to a fixed point. We study
the behavior of AM rigorously and present a proof of this convergence. The key
idea is to formulate AM as a generalized (parallel) majority cellular
automaton, adapting proof techniques from discrete dynamical systems
On the Analysis of a Label Propagation Algorithm for Community Detection
This paper initiates formal analysis of a simple, distributed algorithm for
community detection on networks. We analyze an algorithm that we call
\textsc{Max-LPA}, both in terms of its convergence time and in terms of the
"quality" of the communities detected. \textsc{Max-LPA} is an instance of a
class of community detection algorithms called \textit{label propagation}
algorithms. As far as we know, most analysis of label propagation algorithms
thus far has been empirical in nature and in this paper we seek a theoretical
understanding of label propagation algorithms. In our main result, we define a
clustered version of \er random graphs with clusters where
the probability , of an edge connecting nodes within a cluster is
higher than , the probability of an edge connecting nodes in distinct
clusters. We show that even with fairly general restrictions on and
( for any , , where is the number of nodes), \textsc{Max-LPA} detects the
clusters in just two rounds. Based on this and on empirical
results, we conjecture that \textsc{Max-LPA} can correctly and quickly identify
communities on clustered \er graphs even when the clusters are much sparser,
i.e., with for some .Comment: 17 pages. Submitted to ICDCN 201
Community Detection via Semi-Synchronous Label Propagation Algorithms
A recently introduced novel community detection strategy is based on a label
propagation algorithm (LPA) which uses the diffusion of information in the
network to identify communities. Studies of LPAs showed that the strategy is
effective in finding a good community structure. Label propagation step can be
performed in parallel on all nodes (synchronous model) or sequentially
(asynchronous model); both models present some drawback, e.g., algorithm
termination is nor granted in the first case, performances can be worst in the
second case. In this paper, we present a semi-synchronous version of LPA which
aims to combine the advantages of both synchronous and asynchronous models. We
prove that our models always converge to a stable labeling. Moreover, we
experimentally investigate the effectiveness of the proposed strategy comparing
its performance with the asynchronous model both in terms of quality,
efficiency and stability. Tests show that the proposed protocol does not harm
the quality of the partitioning. Moreover it is quite efficient; each
propagation step is extremely parallelizable and it is more stable than the
asynchronous model, thanks to the fact that only a small amount of
randomization is used by our proposal.Comment: In Proc. of The International Workshop on Business Applications of
Social Network Analysis (BASNA '10
Social Interactions and Subjective Well-Being: Evidence from Latin America
In this paper, we seek to examine the effect of comparisons and social capital on subjective well-being. Furthermore, we test if, through social influence and exposure, social capital is either an enhancer or appeaser of the comparison effect. Using the LatinobarĂÂłmetro Survey (2007) we find that in contrast to most previous studies, the comparison effect on well-being is positive; that is, the better others perform, the happier the individual is. We also find that social capital is among the strongest correlates of individualsâ subjective well-being in Latin American countries. Furthermore, our findings suggest that social contacts may enhance the comparison effect on individualâs happiness, which is more intense for those who perform worse in their reference group.comparison effect, social capital, subjective well-being, social interactions
On the Voting Time of the Deterministic Majority Process
In the deterministic binary majority process we are given a simple graph
where each node has one out of two initial opinions. In every round, every node
adopts the majority opinion among its neighbors. By using a potential argument
first discovered by Goles and Olivos (1980), it is known that this process
always converges in rounds to a two-periodic state in which every node
either keeps its opinion or changes it in every round.
It has been shown by Frischknecht, Keller, and Wattenhofer (2013) that the
bound on the convergence time of the deterministic binary majority
process is indeed tight even for dense graphs. However, in many graphs such as
the complete graph, from any initial opinion assignment, the process converges
in just a constant number of rounds.
By carefully exploiting the structure of the potential function by Goles and
Olivos (1980), we derive a new upper bound on the convergence time of the
deterministic binary majority process that accounts for such exceptional cases.
We show that it is possible to identify certain modules of a graph in order
to obtain a new graph with the property that the worst-case
convergence time of is an upper bound on that of . Moreover, even
though our upper bound can be computed in linear time, we show that, given an
integer , it is NP-hard to decide whether there exists an initial opinion
assignment for which it takes more than rounds to converge to the
two-periodic state.Comment: full version of brief announcement accepted at DISC'1
Social Interactions and Subjective Well-Being: Evidence from Latin America
In this paper, we seek to examine the effect of comparisons and social capital on subjective well-being. Furthermore, we test if, through social influence and exposure, social capital is either an enhancer or appeaser of the comparison effect. Using the LatinobarĂłmetro Survey (2007) we find that in contrast to most previous studies, the comparison effect on well-being is positive; that is, the better others perform, the happier the individual is. We also find that social capital is among the strongest correlates of individualsâ subjective well-being in Latin American countries. Furthermore, our findings suggest that social contacts may enhance the comparison effect on individualâs happiness, which is more intense for those who perform worse in their reference group.The financial support from the Spanish Womenâs Institute and Ministry of Science and Technology ECO2009-10818 and the Basque Government IT241-07 granted to Victoria Ateca-Amestoy and from the Spanish Ministry of Education through grant SEJ2009-11117 to Ana I. Moro-Egido is gratefully acknowledged
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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