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 $n$, the computational cost of the algorithm scales as $O(n^3)$, 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