Krista Rizman Žalik

Multi-objective evolutionary algorithm using problem-specific genetic operators for community detection in networks

Automatic network clustering is an important method for mining the meaningful communities of complex networks. Uncovered communities help to understand the potential system structure and functionality. Many algorithms that use multiple optimization criteria and optimize a population of solutions are difficult to apply to real systems because they suffer a long optimization process. In this paper, in order to accelerate the optimization process and to uncover multiple significant community structures more effectively, a multi-objective evolutionary algorithm is proposed and evaluated using problem-specific genetic mutation and group crossover, and problem-specific initialization. Since crossover operators mainly contribute to performance of genetic algorithms, more problem-specific group crossover operators are introduced and evaluated for intelligent evolution of population. The experiments on both artificial and real-world networks demonstrate that the proposed evolutionary algorithm with problem-specific genetic operations has effective performance on discovering the community structure of networks.

Efficient chain code compression with interpolative coding

Abstract This paper considers the use of interpolative coding for lossless chain code compression. The most popular chain codes are used, including Freeman chain code in eight (F8) and four directions (F4), Vertex Chain Code (VCC), and three-orthogonal chain code (3OT). The whole compression pipeline consists of the Burrows–Wheeler transform, Move-To-Front transform and the interpolative coding, which was improved by FELICS and new Ψ-coding. The approach was compared with the state-of-the-art chain code compression algorithms. For VCC, 3OT and F4, the obtained results are slightly better than the existing approaches. However, an important improvement was achieved with F8 chain code, where the presented approach is considerably better.

Community detection in networks using new update rules for label propagation

Detecting community structure clarifies the link between structure and function in complex networks and is used for applications in many disciplines. The Label Propagation Algorithm (LPA) has the benefits of nearly-linear running time and easy implementation, but it returns multiple resulting partitions over multiple runs. Following LPA, some new updating rules are proposed to detect communities in networks, which are based mainly on the almost strong definition of communities and the topological similarity. Experiments on more artificial and real social networks have demonstrated better performance of the proposed method compared with that of the community detection algorithms CNM, Cfinder and MEP on the quality of communities.

Evolution Algorithm for Community Detection in Social Networks Using Node Centrality

Community structure identification has received a great effort among computer scientists who are focusing on the properties of complex networks like the internet, social networks, food networks, e-mail networks and biochemical networks. Automatic network clustering can uncover natural groups of nodes called communities in real networks that reveals its underlying structure and functions. In this paper, we use a multiobjective evolution community detection algorithm, which forms center-based communities in a network exploiting node centrality. Node centrality is easy to use for better partitions and for increasing the convergence of evolution algorithm. The proposed algorithm reveals the center-based natural communities with high quality. Experiments on real-world networks demonstrate the efficiency of the proposed approach.

Memetic algorithm using node entropy and partition entropy for community detection in networks

Abstract Community detection is a key to understanding the structure of complex networks. Many community detection approaches have been proposed based on the modularity optimization. Algorithms that optimize one initial solution often get into local optima, but algorithms that simultaneously optimize a population of solutions have high computational complexity. To solve these problems, genetic algorithms improved by a local learning procedure known as memetic algorithms can be applied. We propose a memetic algorithm for community detection in networks, that exploits node entropy for local learning. Node entropy is easy to use to speed up the convergence of an evolutionary algorithm and to increase the quality of partitions, while it uses only the node’s neighborhood and does not require any threshold value. Moreover, this algorithm is slightly modified in order to avoid modularity function which suffers a resolution limit and, therefore, it may fail to detect small communities. We propose and use an entropy function as an optimization function and as criteria in grouping crossover operator. Experiments on real-world and synthetic networks illustrate that the proposed method can find natural partitions effectively.

Boolean operations on rasterized shapes represented by chain codes using space filling curves

Abstract This paper introduces a new algorithm for Boolean operations on rasterized geometric shapes that are represented with chain codes. The algorithm works in three steps. Firstly, the chain code symbols are transformed in the Hilbert space, where the overlaid chain code symbols are recognised. After that, a suitable starting cell is determined. Finally, the walk-about through the sequence of the initial chain code symbols is performed to obtain the sequence of chain code symbols representing the shape of the required Boolean operation. The algorithm is demonstrated on Freeman chain code in four directions. The time and space complexity of the proposed algorithm is linear, which was proven theoretically and confirmed by experiments.

Visualization and Clustering of DNA Sequences

Visual inspection can discover patterns that are quite difficult to discover with computers. We introduce a novel encoding algorithm providing graphical representation of DNA in order to characterize and compare long DNA sequences. The algorithm transforms a DNA sequence into four encoded sequences of natural numbers describing all the distances between each two the same and nearest nucleotides in a DNA sequence. Encoded DNA sequences of natural numbers are simpler for computer handling and numerical analysis, and can be employed for 2D graphical representations that offer visual analysis. This novel representation for processing DNA sequences is demonstrated using the coding sequences of the β-globin gene’s exon I for eleven different species. Similarity analysis was done by means of 1D and 2D representation and numerical analysis with similarity/dissimilarity measures used for clustering.