The course focuses on advanced algorithms in the field of network analysis. Lectures will be devoted to the analysis and nature of each algorithm in order to assess the suitability of the method in its application. Experiments with algorithms, tools and selected datasets are carried out in exercises and homework.

Network Analysis Methods 2 - full-time study

doc. Mgr. Miloš Kudělka, Ph.D.

Points for tasks and activities

Participation in seminars and continuous activity: 19-36 marks; credit paper: 10-20 points; Implementation of selected algorithm(s) with a simple user interface to set up experiments: 12-24 marks; Analysis of a larger (real-world) network and report with results: 10-20 marks.

Organization of teaching

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Organizational guidelines for the semester, topics, literature, tools.

Project assignment

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Network analysis and interpretation of results

Data Structures for Network Representation

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Different representations, storage, tasks and their complexity. Large networks and computational issues.

The seminar implements DoK and works with large networks.

Large and dynamic networks

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Different types of networks, properties, tasks.

The seminar works with two temporal data sets.

Link prediction

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Link prediction in networks. Different approaches, methods based on local similarity.

Use and compare different methods based on similarity and common neighbor analysis.

Evolving networks, preferential attachment

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Models of networks with preferential attachment, evolution and dynamics of networks. Preferential attachment as a principle and as a consequence of another principle.

Implementation of two models generating preferential attachment, experiments, visualization.

Multilayer networkss - basic information

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Multilayer networks as a unifying model for different types of networks. Overview, basic measures (centralities).

Experiments with a multilayer network, computing degree-based centralities.

Multilayer networks - measures and projections

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Distance-based measures, random walk applications. Flattening and projection of multilayer networks.

Implementing a random walk in a multilayer network and using it to compute occupation centrality and more.

Multilayer networks - visualization, communities

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Visualization of multilayer networks. Three approaches to community structure detection.

Visualization using one of the recommended libraries. Implementation of community detection using network flattening.

Multilayer networks - link prediction

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Prediction of links in multilayer network environments.

Implementation of known methods and associations rules between layers.

Influence Propagation in Social Networks

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Simple propagation and simulation model.

Simulation of influence propagation in small and large networks.