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Graph Mining (24h, 3 ECTS, period 2)Course director: Mauro Sozio Teachers for 2020-2021
Mauro Sozio (MC1 Telecom Paris, IP Paris, LTCI) sozio at telecom dash paristech dot fr Scientific and pedagogical contentGraphs provide a powerful abstraction for representing a wide variety of real-world information, such as social networks, knowledge and information networks, biological networks, etc. The main objective of the course is to present the models and algorithms for discovering structures in large graphs, while covering the main theoretical and practical aspects of graph mining. In particular, algorithms with theoretical guarantees as well as algorithms that are proven to work well in practice will be discussed. We will cover the following main topics:
Content and schedule
Exercises and Research QuestionsPlease find below a list of exercises for the final exam. The same or very similar exercises will be asked at the exam. A few more exercises will be added shortly. Exercises on the on the 1st part and on the 2nd part of the course Default Projects:You can choose one project among the following ones. The deadline is 24/11 for both of them. Doing a project is not required to pass the course, however, in case no project is completed the maximum note is capped at 16. Project on 3-Hopsets (Update 14/11: after requests, Rust and Go are also supported) Project on Community Search News and Announcements
Teaching languageThe course will be in English. EvaluationDuring the course a few exercises related to the content of the course will be given. A subset of those exercises (or similar ones) will be asked during the final exam. Material presented during the lectures might also be asked at the final exam. The final exam contributes to 80% of the maximum grade, while a practical project contributes to 20% of the maximum grade. The project is not required to pass the course. It typically consists of implementing efficiently a graph algorithm or a data analysis task. Students should preferably use C++, Java, or Python, however, any programming language can be used. Students are not allowed to bring any notes at the final exam. PrerequisitesGood knowledge of algorithms and complexity theory. Good knowledge of Java, C++ or Python are preferable but not necessary. Related BooksAlgorithm Design par J. Kleinberg et E. Tardos, Addison Welsey 2005. D. Easley, J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 2010. Related courses
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