Sergio Peignier

Graph Theory & Network Analysis

Foundations of graph theory and network analysis, with applications to algorithms, complex systems, and biological networks such as gene regulatory systems.

Focus: Algorithms · Network Science · Biological Networks

Level: Undergraduate / Graduate

Introduction to Graph Theory

Core concepts including graphs, nodes, edges, and fundamental properties of network structures.

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Graph Features & Metrics

Key properties of graphs such as degree distribution, centrality, and structural measures.

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Graph Traversal Algorithms

Exploration techniques including depth-first search (DFS) and breadth-first search (BFS).

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Dijkstra’s Algorithm

Shortest path computation in weighted graphs and applications in network optimization.

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Random Graphs

Probabilistic models of networks and their applications in complex systems and data science.

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Project: Connectome Analysis (C. elegans)

Study of neural connectivity using graph theory applied to biological networks.

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Project: Gene Regulatory Network Analysis (S. cerevisiae)

Hands-on project applying graph theory to biological networks, focusing on gene regulatory interactions.

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Final Exams Collection

Past exams for practice and self-evaluation.

Exam 2019
Exam 2020