Graph Mining

During this course, students will learn selected methods and practical algorithms for mining and analyzing graphs with applications in several domains. The selected topics include centrality measures, degree correlations, clustering and community detection, graph embeddings, and tools to handle relational data. During the lectures students will learn the theoretical foundations of the network science which will be later complemented with practical experiments in Python and Julia programming languages. The course requires finishing the Statistical Learning Methods as a prerequisite.

The goal of the course is to provide the basic knowledge of contemporary network science and present the necessary tools used in graph mining. During this course, basic definitions of graph theory will be covered, with emphasis on the random graph theory. Students will learn the basic models, measures, and algorithms used in network science. The theoretical foundations will be complemented with practical experiments, where students will learn where and how to implement such models, analyze and visualize their results by themselves. The course requires finishing the Statistical Learning Methods as a prerequisite.

Literatura podstawowa:

Kamiński B., Prałat P. and Théberge F. (2022), Mining Complex Networks, first edition (https://www.torontomu.ca/mining-complex-networks/)

Barabási A.-L. (2018), Network science. Cambridge University Press (http://networksciencebook.com/)

Literatura uzupełniająca:

Latora V., Nicosia V. and Russo G. (2017), Complex Networks - Principles, Methods and Applications. Cambridge University Press

Newman M (2018), Complex Networks - Principles, Methods and Applications. Oxford University Press, 2nd ed.

Menczer F., Fortunato S. and Davis C. A. (2020), A First Course in Network Science. Cambridge University Press

Publikacje własne:

Bogumił Kamiński, Bartosz Pankratz, Paweł Prałat, Francois Theberge, Modularity of the ABCD random graph model with community structure, W: Journal of Complex Networks,2022;

Bogumił Kamiński, Tomasz Olczak, Bartosz Pankratz, Paweł Prałat , dr Francois Theberge, Properties and Performance of the ABCDe Random Graph Model with Community Structure, W: Big Data Research,2022;

Przemysław Szufel, Bartosz Pankratz, Anna Szczurek, Bogumił Kamiński, Paweł Prałat , Vehicle Routing Simulation for Prediction of Commuter?s Behaviour, W: JOURNAL OF ADVANCED TRANSPORTATION,2022;

Przemysław Szufel, Bogumił Kamiński, Bartosz Pankratz, Francois Theberge , dr Paweł Prałat , Valerie Poulin , Clustering via Hypergraph Modularity,W: red. Hocine Cherifi, José Fernando Mendes, Luis Mateus Rocha, Sabrina Gaito, Esteban Moro, Joana Gonçalves-Sá, Francisco Santos, Complex Networks 2019 : The 8th International Conference on Complex Networks & Their Applications : Book of Abstract ,2019

Kryteria oceniania:

egzamin tradycyjny-pisemny: 0.00%

egzamin testowy: 0.00%

egzamin ustny: 50.00%

kolokwium: 0.00%

inne: 0.00%

projekty: 50.00%

studia przypadków: 0.00%

prezentacje indywidualne lub grupowe: 0.00%

During this course, students will learn selected methods and practical algorithms for mining and analyzing graphs with applications in several domains. The selected topics include centrality measures, degree correlations, clustering and community detection, graph embeddings, and tools to handle relational data. During the lectures students will learn the theoretical foundations of the network science which will be later complemented with practical experiments in Python and Julia programming languages. The course requires finishing the Statistical Learning Methods as a prerequisite.

The goal of the course is to provide the basic knowledge of contemporary network science and present the necessary tools used in graph mining. During this course, basic definitions of graph theory will be covered, with emphasis on the random graph theory. Students will learn the basic models, measures, and algorithms used in network science. The theoretical foundations will be complemented with practical experiments, where students will learn where and how to implement such models, analyze and visualize their results by themselves. The course requires finishing the Statistical Learning Methods as a prerequisite.

Literatura podstawowa:

Kamiński B., Prałat P. and Théberge F. (2022), Mining Complex Networks, first edition (https://www.torontomu.ca/mining-complex-networks/)

Barabási A.-L. (2018), Network science. Cambridge University Press (http://networksciencebook.com/)

Literatura uzupełniająca:

Latora V., Nicosia V. and Russo G. (2017), Complex Networks - Principles, Methods and Applications. Cambridge University Press

Newman M (2018), Complex Networks - Principles, Methods and Applications. Oxford University Press, 2nd ed.

Menczer F., Fortunato S. and Davis C. A. (2020), A First Course in Network Science. Cambridge University Press

Publikacje własne:

Bogumił Kamiński, Bartosz Pankratz, Paweł Prałat, Francois Theberge, Modularity of the ABCD random graph model with community structure, W: Journal of Complex Networks,2022;

Bogumił Kamiński, Tomasz Olczak, Bartosz Pankratz, Paweł Prałat , dr Francois Theberge, Properties and Performance of the ABCDe Random Graph Model with Community Structure, W: Big Data Research,2022;

Przemysław Szufel, Bartosz Pankratz, Anna Szczurek, Bogumił Kamiński, Paweł Prałat , Vehicle Routing Simulation for Prediction of Commuter?s Behaviour, W: JOURNAL OF ADVANCED TRANSPORTATION,2022;

Przemysław Szufel, Bogumił Kamiński, Bartosz Pankratz, Francois Theberge , dr Paweł Prałat , Valerie Poulin , Clustering via Hypergraph Modularity,W: red. Hocine Cherifi, José Fernando Mendes, Luis Mateus Rocha, Sabrina Gaito, Esteban Moro, Joana Gonçalves-Sá, Francisco Santos, Complex Networks 2019 : The 8th International Conference on Complex Networks & Their Applications : Book of Abstract ,2019

Kryteria oceniania:

egzamin tradycyjny-pisemny: 0.00%

egzamin testowy: 0.00%

egzamin ustny: 50.00%

kolokwium: 0.00%

inne: 0.00%

projekty: 50.00%

studia przypadków: 0.00%

prezentacje indywidualne lub grupowe: 0.00%