On Fractional Approach to Analysis of Linked Networks

In this paper, we present the outer product decomposition of a product of compatible linked networks. It provides a foundation for the fractional approach in network analysis. We discuss the standard and Newman's normalization of networks. We propose some alternatives for fractional bibliographic coupling measures

An O(m) Algorithm for Cores Decomposition of Networks

The structure of large networks can be revealed by partitioning them to smaller parts, which are easier to handle. One of such decompositions is based on $k$--cores, proposed in 1983 by Seidman. In the paper an efficient, $O(m)$, $m$ is the number of lines, algorithm for determining the cores decomposition of a given network is presented

Short Cycles Connectivity

Short cycles connectivity is a generalization of ordinary connectivity. Instead by a path (sequence of edges), two vertices have to be connected by a sequence of short cycles, in which two adjacent cycles have at least one common vertex. If all adjacent cycles in the sequence share at least one edge, we talk about edge short cycles connectivity. It is shown that the short cycles connectivity is an equivalence relation on the set of vertices, while the edge short cycles connectivity components determine an equivalence relation on the set of edges. Efficient algorithms for determining equivalence classes are presented. Short cycles connectivity can be extended to directed graphs (cyclic and transitive connectivity). For further generalization we can also consider connectivity by small cliques or other families of graphs

Face recognition in different subspaces - A comparative study

Face recognition is one of the most successful applications of image analysis and understanding and has gained much attention in recent years. Among many approaches to the problem of face recognition, appearance-based subspace analysis still gives the most promising results. In this paper we study the three most popular appearance-based face recognition projection methods (PCA, LDA and ICA). All methods are tested in equal working conditions regarding preprocessing and algorithm implementation on the FERET data set with its standard tests. We also compare the ICA method with its whitening preprocess and find out that there is no significant difference between them. When we compare different projection with different metrics we found out that the LDA+COS combination is the most promising for all tasks. The L1 metric gives the best results in combination with PCA and ICA1, and COS is superior to any other metric when used with LDA and ICA2. Our results are compared to other studies and some discrepancies are pointed ou