NETWORK SCIENCE (TOMASO ERSEGHE)
Corso
A Padova
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Descrizione
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Tipologia
Corso
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Luogo
Padova
The course has the following methods of examination:
INTERNET module:
The final exam will be the same for both ATTENDING and NON-ATTENDING students since it does not rely on in-class activities. The exam consists of two parts, namely: 1. a WRITTEN EXAM at the computer, 2. a LAB TEST. Students will be offered four attempts to pass the written and the lab tests. During in-class lectures, the students may be offered to participate to some (in class or at home) activities, such as peer-reviewing of other students' reports, participating in-class discussion and taking part to problem-solving competitions. The active participation to such initiatives may bring a few extra points (up to 3) to the students.
NETWORK SCIENCE module:
The verification of the expected knowledge and skills is carried out with the DEVELOPMENT OF A PROJECT aimed at verifying the ability to apply theory in interdisciplinary contexts, and which requires: the choice, the collection of data, and the analysis of a different network for each student; computer implementation (in any programming language known to the student) of the algorithms required for the analysis; the drafting of an essay. The project is foreseen in two ways: 1. for ATTENDING students in which the students are guided towards intermediate project objectives (HOMEWORKS) coherently with the development of the lessons, and complete the project at the end of the course; 2. for NON-ATTENDING students, in which the development of the project takes place in a single solution and is discussed in an oral exam in one of the four institutional dates. A bonus of up to 3 points is available for attending students that take part to an INTERDISCIPLINARY PROJECT with social science students attending the twin course on SOCIAL NETWORK ANALYSIS.
The final grade is expressed as a combination of the judgments in the two modules (50%+50%).
Sedi e date
Luogo
Inizio del corso
Inizio del corso
Opinioni
Programma
1. Network models - Basic network properties: graphs, adjacency matrix, degree distribution, connectivity; Erdos-Renyi model; Random graphs with general degree distribution; Power laws and scale free networks; Small world phenomena; Hubs; Network generation and expansion; Barabasi-Albert model; Preferential attachment; Evolving networks; Assortativity; Robustness.
2. Ranking - Hubs and authorities; PageRank: teleportation, topic specific ranking, proximity measures, trust rank; Speeding up by quadratic interpolation.
3. Community detection - Dendrograms; Girvan Newman method and betweenness; Modularity optimization; Spectral clustering; Other clustering algorithms; Core-periphery model for overlapping communities; Clique percolation method; Cluster affiliation model and BigCLAM.
4. Miscellaneous aspects - Link prediction; Applications scenarios
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NETWORK SCIENCE (TOMASO ERSEGHE)
