This diploma thesis deals with various types of relations observable in social networks. First part contains survey of existing papers focused on social media data analysis and visualisations. This survey is followed by research of existing visualisation applications. The contribution of this thesis is a new look at relations on social networks, from the side of relationship between users who write posts on any given topic. To visualize these relations was chosen network graph. On the basis of established criteria existing instruments were evaluated and since any of them did not meet all requirements, I created new application for visualising relations. Thesis also includes a description of selection the appropriate libraries for its implementation, explanation of user interface and a summary of configuration options and customization. The end of the work contains analysis and visualisations made with newly-created tool.

Analysis of Inferred Social Networks While the social network analysis (SNA) is not a new science branch, thanks to the boom of social media platforms in recent years new methods and approaches appear with increasing frequency. However, not all datasets have network structure visible at first glance. We believe that every reasonable interconnected system of data hides a social network, which can be inferred using specific methods. In this thesis we examine such social network, inferred from the real-world data of a smaller bank. We also review some of the most commonly used methods in SNA and then apply them on our complex network, expecting to find structures typical for traditional social networks.

Social networks - a description of principles and theoretical background, dynamics and possibilities of their use, particularly in management and marketing; methods for detection of social networks and work with them, the available software tools, the practical application of theoretical knowledge - analysis of specific data structures and social networks subsequent processing with regard to potential applications in management

This diploma thesis describes and implements the design of a graph neural network usedfor 2D segmentation of neural structure. The first chapter of the thesis briefly introduces the problem of segmentation. In this chapter, segmentation techniques are divided according to the principles of the methods they use. Each type of technique contains the essence of this category as well as a description of one representative. The second chapter of the diploma thesis explains graph neural networks (GNN for short). Here, the thesis divides graph neural networks in general and describes recurrent graph neural networks(RGNN for short) and graph autoencoders, that can be used for image segmentation, in more detail. The specific image segmentation solution is based on the message passing method in RGNN, which can replace convolution masks in convolutional neural networks.RGNN also provides a simpler multilayer perceptron topology. The second type of graph neural networks characterised in the thesis are graph autoencoders, which use various methods for better encoding of graph vertices into Euclidean space. The last part ofthe diploma thesis deals with the analysis of the problem, the proposal of its specific solution and the evaluation of results. The purpose of the practical part of the work was the implementation of GNN for image data segmentation. The advantage of using neural networks is the ability to solve different types of segmentation by changing training data. RGNN with messaging passing and node2vec were used as implementation GNNf or segmentation problem. RGNN training was performed on graphics cards provided bythe school and Google Colaboratory. Learning RGNN using node2vec was very memory intensive and therefore it was necessary to train on a processor with an operating memory larger than 12GB. As part of the RGNN optimization, learning was tested using various loss functions, changing topology and learning parameters. A tree structure method was developed to use node2vec to improve segmentation, but the results did not confirman improvement for a small number of iterations. The best outcomes of the practical implementation were evaluated by comparing the tested data with the convolutional neural network U-Net. It is possible to state comparable results to the U-Net network, but further testing is needed to compare these neural networks. The result of the thesisis the use of RGNN as a modern solution to the problem of image segmentation and providing a foundation for further research.

Master´s thesis focuses on the utilisation of project management methodology within the design of a project in a selected company. It specifies theoretical sources which are the key for the project plan proposal. It applies the theory to the project of introducing electronic commerce in the field of building material sale. The thesis containts the plan proposal and optimisation of the project in terms of time, costs and use of available resources, together with the analysis of a company environment.

Network visualizations have been used for quit long time. Different disciplines use this visualization to compare a given dataset. Identifying better comparison approach that is used for information visualization is indispensable both for the people who are using it and for developers who are looking for a better way of visualizing huge data. In this thesis a task based approach has been used to analyze two different network comparison approaches namely Juxtaposition (showing different objects compared in separate space or time) and Superposition (overlaying objects in the same space). Thirty students at Linnaeus University have participated in the questionnaire to evaluate the usability of the two approaches. SPSS tool is used to analyze the data collected from the participants and the result explicitly indicates that there is no significant variation between Juxtaposition and Superposition comparison approaches. The result can be used as a recommendation for domain specific professionals and developers in their quest for better network comparison for their audience.

Traffic flow predictions are an important part of an Intelligent Transportation System as the ability to forecast accurately the traffic conditions in a transportation system allows for proactive rather than reactive traffic control. Providing accurate real-time traffic predictions is a challenging problem because of the nonlinear and stochastic features of traffic flow. An increasingly widespread deployment of traffic sensors in a growing transportation system produces greater volume of traffic flow data. This results in problems concerning fast, reliable and scalable traffic predictions.The thesis explores the feasibility of increasing the scalability of real-time traffic predictions by partitioning the transportation system into smaller subsections. This is done by using data collected by Trafikverket from traffic sensors in Stockholm and Gothenburg to construct a traffic sensor graph of the transportation system. In addition, three graph partitioning algorithms are designed to divide the traffic sensor graph according to vehicle travel time. Finally, the produced transportation system partitions are used to train multi-layered long shortterm memory recurrent neural networks for traffic density predictions. Four different types of models are produced and evaluated based on root mean squared error, training time and prediction time, i.e. transportation system model, partitioned transportation models, single sensor models, and overlapping partition models.Results of the thesis show that partitioning a transportation system is a viable solution to produce traffic prediction models as the average prediction accuracy for each traffic sensor across the different types of prediction models are comparable. This solution tackles scalability issues that are caused by increased deployment of traffic sensors to the transportation system. This is done by reducing the number of traffic sensors each prediction model is responsible for which results in less complex models with less amount of input data. A more decentralized and effective solution can be achieved since the models can be distributed to the edge of the transportation system, i.e. near the physical location of the traffic sensors, reducing prediction and response time of the models. / Prognoser för trafikflödet är en viktig del av ett intelligent transportsystem, eftersom möjligheten att prognostisera exakt trafiken i ett transportsystem möjliggör proaktiv snarare än reaktiv trafikstyrning. Att tillhandahålla noggrann trafikprognosen i realtid är ett utmanande problem på grund av de olinjära och stokastiska egenskaperna hos trafikflödet. En alltmer utbredd använding av trafiksensorer i ett växande transportsystem ger större volym av trafikflödesdata. Detta leder till problem med snabba, pålitliga och skalbara trafikprognoser.Avhandlingen undersöker möjligheten att öka skalbarheten hos realtidsprognoser genom att dela transportsystemet i mindre underavsnitt. Detta görs genom att använda data som samlats in av Trafikverket från trafiksensorer i Stockholm och Göteborg för att konstruera en trafiksensor graf för transportsystemet. Dessutom är tre grafpartitioneringsalgoritmer utformade för att dela upp trafiksensor grafen enligt fordonets körtid. Slutligen används de producerade transportsystempartitionerna för att träna multi-layered long short memory neurala nät för förspänning av trafiktäthet. Fyra olika typer av modeller producerades och utvärderades baserat på rotvärdes kvadratfel, träningstid och prediktionstid, d.v.s. transportsystemmodell, partitionerade transportmodeller, enkla sensormodeller och överlappande partitionsmodeller.Resultat av avhandlingen visar att partitionering av ett transportsystem är en genomförbar lösning för att producera trafikprognosmodeller, eftersom den genomsnittliga prognoser noggrannheten för varje trafiksensor över de olika typerna av prediktionsmodeller är jämförbar. Denna lösning tar itu med skalbarhetsproblem som orsakas av ökad användning av trafiksensorer till transportsystemet. Detta görs genom att minska antal trafiksensorer varje trafikprognosmodell är ansvarig för. Det resulterar i mindre komplexa modeller med mindre mängd inmatningsdata. En mer decentraliserad och effektiv lösning kan uppnås eftersom modellerna kan distribueras till transportsystemets kant, d.v.s. nära trafiksensorns fysiska läge, vilket minskar prognosoch responstid för modellerna.

Outlier detection is an important data mining task due to its applicability in many contemporary applications such as fraud detection and anomaly detection in networks, etc. It assumes significance due to the general perception that outliers represent evolving novel patterns in data that are critical to many discovery tasks. Extensive use of various data mining techniques in different application domains gave rise to the rapid proliferation of research work on outlier detection problem. This has lead to the development of numerous methods for detecting outliers in various problem settings. However, most of these methods deal primarily with numeric data. Therefore, the problem of outlier detection in categorical data has been considered in this work for developing some novel methods addressing various research issues. Firstly, a ranking based algorithm for detecting a likely set of outliers in a given categorical data has been developed employing two independent ranking schemes. Subsequently, the issue of data dimensionality has been addressed by proposing a novel unsupervised feature selection algorithm on categorical data. Similarly, the uncertainty associated with the outlier detection task has also been suitably dealt with by developing a novel rough sets based categorical clustering algorithm. Due to the networked nature of the data pertaining to many real life applications such as computer communication networks, social networks of friends, the citation networks of documents, hyper-linked networks of web pages, etc., outlier detection(also known as anomaly detection) in graph representation of network data turns out to be an important pattern discovery activity. Accordingly, a novel graph mining method has been envisaged in this thesis based on the concept of community detection in graphs. In addition to finding anomalous nodes and anomalous edges, this method is capable of detecting various higher level anomalies that are arbitrary sub-graphs of the input graph. Subsequently, these ideas have been further extended in this thesis to characterize the time varying behavior of outliers(anomalies) in dynamic network data by defining various categories of temporal outliers (anomalies). Characterizing the behavior of such outliers during the evolution of the network over time is critical for discovering different anomalous connectivity patterns with potential adverse effects such as intrusions into a computer network, etc. In order to deal with temporal outlier detection in single instance network/graph data, the link prediction task has been leveraged in this thesis to produce multiple instances of the input graph. Thus, various outlier detection principles have been successfully applied for mining various categories of temporal outliers(anomalies) in the graph representation of network data.

The master’s thesis considers a project proposal for development of application software for customer’s needs. The thesis specifies the basic concepts and methods of project management. It contains analysis of the current state of company and its environment. The thesis focuses on the project proposal.

The diploma thesis deals with a problematics of project management. The major point is to create a project that is aiming on expanding field of aktivity in selected company with the use of theoretical knowledge of project management. This knowledge is desribed in first chapter of the diploma thesis. The second chapter contains basic informations about company and the analysis of company surroundings. The last part of the thesis solves proposals and desribes benefits created by these solutions in monitored company.