Zdeněk Horák

Social and swarm aspects of co-authorship network

The analysis of social networks is concentrated mainly on uncovering hidden relations and properties of network nodes (vertices). Most of the current approaches are focused on different network types and different network coefficients. This article introduces a social network analysis based on the so-called Forgetting Curve and Swarm Intelligence inspired by the Ant Colony Optimization. We analyse a co-authorship network and identify two types of ties among its nodes. The Forgetting Curve and Swarm Intelligence are used to model the dynamics of such a network.

Local Community Detection and Visualization: Experiment Based on Student Data

This paper is focused on the detection of communities in social networks. We propose and describe a novel method for detecting local communities. We have used this method in an experiment on student social networks in order to prove our hypothesis about the nature of student communities. The results of the experiment rationalized our hypothesis and confirmed the effectiveness of the described method of local community detection.

Two New Methods for Network Analysis: Ant Colony Optimization and Reduction by Forgetting

This paper presents two new methods for network analysis. Ant colony optimization is a nature inspired algorithm succesfull in graph traversal and network path finding whereas network reduction based on stability introduces two new properties of network vertices based on their long-term behavior, their role in the network and the understanding of how memory works. We illustrate the algorithms on applications in social network analysis and information retrieval using the DBLP dataset and a small network of hyperlinked documents.

Fuzzified Aho-Corasick search automata

In this paper, we discuss the need for efficient approximate string matching. We present the well-known Aho-Corasick automaton for locating multiple patterns and discuss an approach for fuzzification of this automaton. Along with some motivational examples, we propose and illustrate a novel algorithm for automaton construction.

On spectral partitioning of co-authorship networks

Spectral partitioning is a well known method in the area of graph and matrix analysis. Several approaches based on spectral partitioning and spectral clustering were used to detect structures in real world networks and databases. In this paper, we explore two community detection approaches based on the spectral partitioning to analyze a co-authorship network. The partitioning exploits the concepts of algebraic connectivity and characteristic valuation to form components useful for the analysis of relations and communities in real world social networks.

On Social Networks Reduction

Since the availability of social networks data and the range of these data have significantly grown in recent years, new aspects have to be considered. In this paper, we use combination of Formal Concept Analysis and well-known matrix factorization methods to address computational complexity of social networks analysis and clarity of their visualization. The goal is to reduce the dimension of social network data and to measure the amount of information, which has been lost during the reduction. Presented example containing real data proves the feasibility of our approach.

Comparison of Local and Global Ranking in Networks

Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. Latest trend in analyzing networks is to focus on local methods and parallelization. We introduce a method to find the ranking of the nodes. The approach extracts dependency relations among the network’s nodes. Key technical parameter of the approach is locality. Since only the surrounding of examined nodes is used in computations, there is no need to analyze the entire network. We compare this proposed local ranking to the global ranking of PageRank. We present experiment using large-scale artificial and real world networks. The results of experiment show high effectiveness due to the locality of our approach and also high quality of node ranking comparable to PageRank.

Image Search: A Story of One User Interface

With the rapid development of information technology, the emphasis on the quality of user interfaces has been increasing recently, also with regard to mobile platforms, accessibility etc. In this paper we focus on engaging more interactive ways to image search. While observing and discussing with users about how they wish to proceed during search of images we detected four typical scenarios. We present all of them on concrete examples. We also describe what kind of image features our system works with and how we detect them. We introduce our own user interface of Xingle testing system where are all the mentioned scenarios implemented. The Xingle system works with about half a million images that were collected for testing purposes.

Author Profile Identification Using Formal Concept Analysis

This paper presents results of the finding of the author’s profiles using formal concepts generated from DBLP database. Our main aim was to evaluate the use of formal concept analysis as a method for extracting the author’s profiles. There are several commonly used methods for clustering and for finding experts in a large database. These methods are mainly based on different kinds of clustering and metrics which are sometimes difficult to understand. Finding experts for particular and mainly special areas of research is not an easy task. Formal concept analysis (FCA) is a method with a very strong mathematical background, which makes it easy to understand. Properties of FCA can give us a very strong tool for finding author’s profiles.

Evolution of Author’s Profiles Based on Analysis of DBLP Data

In this paper we introduce a method for analysing the evolution of author’s profiles based on keywords extracted from titles of research papers contained in the DBLP database. Academic literature and research papers have been increasing rapidly in recent years. There are a lot of authors who focus each year on popular and widely used topics. Therefore finding experts for particular areas of research is not an easy task. Our solution presented in this paper uses formal concept analysis for finding profiles of author’s based on keywords used in titles of research papers. We try to analyse the evolution of these author’s profiles over time. Our presented approach is illustrated by using papers contained in the DBLP database from the last decade.