Graph Neural Networks: Day 1- Introduction to GNNs

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Introduction to GNNs and Graph Theory: This lecture introduces foundational graph theory concepts—such as nodes, edges, adjacency matrices—and highlights the unique characteristics of graph data. Key applications of GNNs are explored, including their roles in social networks, molecular research, transportation, and knowledge graphs.

Course Structure and Practical Work: The course balances theory with hands-on coding in Python, Java, and C++, starting with NetworkX for small graphs. Students engage in exploratory data analysis, graph embeddings, and GNN models through assignments and lab sessions.

Network Analysis and Visualization: Assignments focus on visualizing large datasets, including social networks and biological systems. Students are encouraged to explore datasets like Facebook ego networks and Bitcoin transactions, using NetworkX to uncover complex patterns and insights.
  1. SupportVectors
  2. GNNS
  3. AI
  4. machinelearning
  5. supportvectors
  6. edutech
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