343 research outputs found
Maximum Weight Matching via Max-Product Belief Propagation
Max-product "belief propagation" is an iterative, local, message-passing
algorithm for finding the maximum a posteriori (MAP) assignment of a discrete
probability distribution specified by a graphical model. Despite the
spectacular success of the algorithm in many application areas such as
iterative decoding, computer vision and combinatorial optimization which
involve graphs with many cycles, theoretical results about both correctness and
convergence of the algorithm are known in few cases (Weiss-Freeman Wainwright,
Yeddidia-Weiss-Freeman, Richardson-Urbanke}.
In this paper we consider the problem of finding the Maximum Weight Matching
(MWM) in a weighted complete bipartite graph. We define a probability
distribution on the bipartite graph whose MAP assignment corresponds to the
MWM. We use the max-product algorithm for finding the MAP of this distribution
or equivalently, the MWM on the bipartite graph. Even though the underlying
bipartite graph has many short cycles, we find that surprisingly, the
max-product algorithm always converges to the correct MAP assignment as long as
the MAP assignment is unique. We provide a bound on the number of iterations
required by the algorithm and evaluate the computational cost of the algorithm.
We find that for a graph of size , the computational cost of the algorithm
scales as , which is the same as the computational cost of the best
known algorithm. Finally, we establish the precise relation between the
max-product algorithm and the celebrated {\em auction} algorithm proposed by
Bertsekas. This suggests possible connections between dual algorithm and
max-product algorithm for discrete optimization problems.Comment: In the proceedings of the 2005 IEEE International Symposium on
Information Theor
DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection
Fake News on social media platforms has attracted a lot of attention in
recent times, primarily for events related to politics (2016 US Presidential
elections), healthcare (infodemic during COVID-19), to name a few. Various
methods have been proposed for detecting Fake News. The approaches span from
exploiting techniques related to network analysis, Natural Language Processing
(NLP), and the usage of Graph Neural Networks (GNNs). In this work, we propose
DEAP-FAKED, a knowleDgE grAPh FAKe nEws Detection framework for identifying
Fake News. Our approach is a combination of the NLP -- where we encode the news
content, and the GNN technique -- where we encode the Knowledge Graph (KG). A
variety of these encodings provides a complementary advantage to our detector.
We evaluate our framework using two publicly available datasets containing
articles from domains such as politics, business, technology, and healthcare.
As part of dataset pre-processing, we also remove the bias, such as the source
of the articles, which could impact the performance of the models. DEAP-FAKED
obtains an F1-score of 88% and 78% for the two datasets, which is an
improvement of 21%, and 3% respectively, which shows the effectiveness of the
approach.Comment: Accepted at IEEE/ACM International Conference on Advances in Social
Networks Analysis and Mining (ASONAM) 202
Thermal fluctuation driven collective directed-motion due to coordinate-dependent damping
In the present paper, we show thermal fluctuations driven directed transport
of a collection of dimer in various configurations. The directed motion arises
as a result of the existence of broken symmetry caused by coordinate or state
dependence of the diffusivity and damping. In all the simulations, the
diffusivity of particles are related to the damping coefficient in accordance
with the Stokes-Einstein relation. We also show here that in the absence of the
broken symmetry induced by the coordinate/state dependent diffusion, similar
structures form, however they do not show directed transport on average. We
also give in this paper a detailed discussion on the interpretation of such
motions driven by bath degrees of freedom pertaining to conditions of
equilibrium and out of equilibrium situations.Comment: 7 page and 6 figure
Detection and Prevention of Vampire Attack in MANET
A mobile ad-hoc network is a temporary, infrastructure less network where nodes communicate without any centralized mechanism. This dynamic behaviour of MANET makes this network more potentially applicable in conference, battlefield environment and disaster relief, and has received significant attention in recent years. Attacker may use this weakness to disrupt the network. Subsequently, Power draining is the major thread; where attacker not only exhausts the network traffic but also degrades the life of node as well network. The objective of this study is to detect and prevent mobile ad- hoc networks from unwanted power draining due to Vampire attack. Here, Targeted Flooding through high battery capacity node has been used to deploy Vampire attack in mobile ad-hoc network. Subsequently, energy consumption and capacity observation technique has been used to detect malicious node(s). Furthermore, prevention method forcefully shutdown malicious nodes and transfer communication
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