In this post, I would like to introduce my latest paper published in Mathematics (ISSN: 2227-7390), a top-quality journal in the field of mathematics.
About our paper: Distributed Mechanism for Detecting Average Consensus with Maximum-Degree Weights in Bipartite Regular Graphs
In this article, we focus our attention on the average consensus algorithm (AC) with the maximum-degree (MD) weights, which is a distributed iterative consensus scheme for estimating various aggregate functions (namely, the arithmetic mean, the sum, and the network size). More specifically, we deal with AC with MD weights for distributed averaging over bipartite regular graphs, where this algorithm diverges, as identified in . In the first part of our contribution, we provide a spectral analysis of AC with MD weights in bipartite regular graphs. Then, we propose a distributed mechanism for detecting whether or not the algorithm is executed in graphs that are bipartite regular and identify how to properly reconfigure the algorithm in these graphs. This allows ensuring the convergence of AC with MD weights in these critical topologies whereby AC is able to estimate the desired aggregate function after reconfiguration. The proposed distributed mechanism is tested in three scenarios (i.e., the inner states are rounded to either decimals, hundreds, or thousands) over bipartite regular graphs with various degrees as well as in random graphs (RGs) and random geometric graphs (RGGs) with various parameters. In the experimental part, very high detection precision of our mechanism is proven in comprehensive experiments, and it is identified that our proposed mechanism can ensure that the inner states converge to the arithmetic mean although AC with MD weights is executed in bipartite regular graphs. As the literature review shows, our contribution poses a significant novelty compared to other manuscripts from the field, where no similar approach for the mentioned purpose is presented.
Click here to see the paper
Mathematics (ISSN: 2227-7390) is another top-quality journal issued by MDPI. It is an open-access journal with a rapid and quality review process. Besides, the journal is highly ranked in Web of Science (JCR - Q1 (Mathematics) / CiteScore - Q1 (General Mathematics)), where it is even ranked in the top ten percents (i.e., the first decile). I have only positive experience with this journal, thereby strongly recommending publishing in it. Also, I can recommend reading papers published in this journal, where many top quality manuscripts appropriate for being cited can be found.