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Course Program

Topic 1: Network Vizualization⚓︎

Theory:⚓︎

  • Graph tools
  • Graph formats

Practice:⚓︎

  • iGraph
  • Gephi
  • NetworkX
  • ReGraph
  • Cytoscape
  • Graphistry

Topic 2: Network Properties and Centralities⚓︎

Theory:⚓︎

  • betweennees
  • closensess
  • pagerank
  • degree
  • cores
  • Graph Motiffs
  • bipartite networks

Practice:⚓︎

  • betweennees
  • closensess
  • pagerank
  • degree
  • cores
  • graph motiffs
  • bipartite networks
  • HITS

Topic 3: Network Models⚓︎

Theory:⚓︎

  • Erdős–Rényi model
  • Preferential attachment
  • Small World model

Practice:⚓︎

  • generate random graphs with nx

Topic 4: Community Detection⚓︎

Theory:⚓︎

  • Modularity
  • Label propagation
  • Newman alg.
  • Louvain alg.
  • Leiden alg.
  • Spectral clustering
  • Hierarchhical and aglomerative clustering

Practice:⚓︎

  • Label propagation
  • Newman alg.
  • Louvain alg.
  • Leiden alg.
  • Blockmodelling guest talk

Topic 5: Epidemic Models and Information Propagation⚓︎

Theory:⚓︎

  • Treshold Models
  • Independent Cascades Models
    • SI
    • SIS
    • SIR

Practice:⚓︎

  • ndlib

Topic 6: Graph Embeddings (part 1)⚓︎

Theory:⚓︎

  • DeepWalk
  • Node2vec
  • LINE
  • HOPE
  • Graph Rewiring

Practice:⚓︎

  • link prediction with network embeddings

Topic 7: Graph Embeddings (part 2)⚓︎

Theory:⚓︎

  • Message Passing Neural Networks
    • pagerank as message parsing
  • GraphSage
  • Graph Attention Networks
  • Weisfeile-Lehman test

Practice:⚓︎

  • Community detection with GAT

Topic 8: Temporal and Knowledge Graphs⚓︎

Theory:⚓︎

  • Message Parsing with recurrency
  • Temporal Graph Networks
  • Knowledge graphs
  • JODIE
  • TGAT

Practice:⚓︎

  • DGL,
  • TransE

Topic 9: Graph Based Search, Graph Databases⚓︎

Theory:⚓︎

  • Information Retrieval
  • fais
  • gensim
  • elasticsearch

Extra⚓︎

Extra 1: Web scraping⚓︎

Extra 2: Graphs of words⚓︎