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Graph Algorithms Topics

  1. Basic Graph Theory Concepts: Vertex, Edge, Degree, Directed/Undirected Graphs, Weighted Graphs, etc.
  2. Graph Algorithms: Dijkstra's, Bellman-Ford, A*, BFS, DFS, Kruskal's, Prim's, Topological Sorting, etc.
  3. Large Graphs and Performance: Big-O Notation, Distributed Graph Processing, Graph Partitioning, Sparse/Dense Graph, Streaming Algorithms, etc.
  4. Graphs in Business and Analytics: Centrality Measures, Community Detection, Graph Analytics, Network Segmentation, Path Analysis, etc.
  5. Graphs in Fraud Detection and Healthcare: Anomaly Detection, Forensic Analysis, KYC Compliance, Money Laundering Detection, etc.
  6. Customer Analysis: Customer 360 View, CLV, Sentiment Analysis, Churn Prediction, Cohort Analysis, Social Listening, etc.

  7. Machine Learning and Graphs: Graph Neural Networks (GNNs), Graph Embeddings, Temporal Graph Analysis, Node2Vec, Graph Convolutional Networks (GCNs), etc.

  8. Graph Databases and Query Languages: Terms like Neo4j, Cypher Query Language, graph indexing, and graph-based data models.

  9. Graph Theory in Bioinformatics and Healthcare: Network pharmacology, protein-protein interaction networks, genetic networks.
  10. Graphs in Social Science and Epidemiology: Social influence models, diffusion networks, contact tracing graphs.
  11. Graph Theory in Infrastructure and Urban Planning: Transportation networks, utility networks, urban flow analysis.
  12. Graph Theory in Physics and Chemistry: Atomic and molecular structures, network thermodynamics, quantum graphs.