Sentence examples for maximal matching algorithm from inspiring English sources

The maximal matching algorithm for maximizing the assortativity index (hereafter, referred to as the maximal assortative matching algorithm, MAM) prefers to include edges that have lower assortativity weight as part of the matching.

To the best of our knowledge, we have not come across a maximal matching algorithm that maximizes the assortativity index (for matching nodes that are similar to each other) or minimizes the assortativty index (for matching nodes that are very different from each other) in complex network graphs.

The rest of the paper is organized as follows: Sect. 2 presents the maximal assortative matching (MAM) algorithm for an arbitrary graph and discusses its flexibility to be used as a maximal matching algorithm for maximizing the number of nodes matched (hereafter referred to as the maximal node matching algorithm, MNM).

In this paper, we propose a maximal matching algorithm that can be used to maximize or minimize the assortativity index of the edges constituting the matching determined in complex network graphs where the nodes have weights (the smaller the difference in the node weights, the more similar are the nodes and vice-versa).

To the best of our knowledge, we have not come across a maximal matching algorithm that is aimed at simultaneously maximizing the a(di ssortativity of the matching as well as maximizing the cardinality of the matching for complex network graphs.

We present two randomized implicit algorithms: A randomized minimum spanning tree algorithm and a maximal matching algorithm.

Using a novel model for the dynamics of maximal matching algorithms, we show that modified maximal matching algorithms guarantee stability of the switch and establish bounds on the average delay experienced by a packet.

We primarily use Gephi for visualization; the implementations of the maximal matching algorithms proposed in this paper have been done from scratch through object-oriented programming in Java.

In this paper, we investigate how the class of maximal matching algorithms deployed in switches with a speedup of less than two can be modified to take into account the varying packet sizes.

As mentioned earlier, the results of our maximal matching algorithms can be ported to any network visualization tool and the edges chosen for matching could be visualized; the resolution of the visualization is limited only to the tool being used.

One could download this toolkit to their programming environment (say: NetBeans, https://netbeans.org/), transform the code for maximal matching algorithms to an API and integrate the API as a plug-in to Gephi.