‘Iterative conditional fitting’ is a recently proposed algorithm that can be used for maximization of the likelihood function in marginal independence models for categorical data. This paper describes a modification of this algorithm, which allows one to compute maximum likelihood estimates in a class of chain graph models for categorical data. The considered discrete chain graph models are def...

Models of Access Control Policies specified with graphs and graph transformation rules combine an intuitive visual representation with solid semantical foundations. While the expressive power of graph transformations leads in general to undecidable models, we prove that it is possible, with reasonable restrictions on the form of the rules, to obtain access control models where safety is decidab...

The weight of an edge xy of a graph is defined to be the sum of degrees of the vertices x and y. The weight of a graph G is the minimum of weights of edges of G. More than twenty years ago Erdős was interested in finding the maximum weight of a graph with n vertices and m edges. This paper presents a complete solution of a modification of the above problem in which a graph is required to be bip...

Reliability Wiener number is a modification of the original Wiener number in which probabilities are assigned to edges yielding a natural model in which there are some (or all) bonds in the molecule that are not static. Various probabilities naturally allow modelling different types of chemical bonds because chemical bonds are of different types and it is well-known that under certain condition...

Graph structures are utilized to represent a wide range of objects including naturally graph-like objects such as molecules and derived graph structures such as connectivity graphs for region-based image retrieval. This paper proposes to extend the applicability of the Earth Mover's Distance [RTG98] (EMD) to graph objects by deriving a similarity model with a representation of structural graph ...

This paper introduces a novel and efficient Graph Convolutional Network (GCN) Spatial Supporting Modification (SSM) method for classifying Hyperspectral Images (HSI), called First (SPA-F). The proposed can utilize spatial information fully based on the assumption that neighboring pixels are more likely to belong same category. uses from two levels consists of following steps. Firstly, graph is ...

This paper presents a reference data set along with a labeling for graph clustering algorithms, especially for those handling dynamic graph data. We implemented a modification of Iterative Conductance Cutting and a spectral clustering. As base data set we used a filtered part of the Enron corpus. Different cluster measurements, as intra-cluster density, inter-cluster sparseness, and Q-Modularit...

The most popular algorithms for generation of minimal spanning tree are Kruskal’s[2] and Prim’s[2] algorithm. Many algorithms have been proposed for generation of all spanning tree. This paper deals with generation of all possible spanning trees in increasing cost of a weighted graph. This approach uses one matrix called Difference Weighted Circuit Matrix (DWCM); it is little bit modification o...