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References: Graph Algorithms, Centrality, and Similarity Measures

  1. Centrality - Wikipedia - Surveys degree, betweenness, closeness, and eigenvector centrality with their formal definitions, directly matching the four-provider comparison this chapter uses to show that "most important" has no single definition.

  2. PageRank - Wikipedia - Explains the damping-factor recursive formula originally built to rank web pages, the same algorithm this chapter repurposes to rank providers by referral influence.

  3. Cosine Similarity - Wikipedia - Defines the angle-based vector similarity measure this chapter computes by hand on patient symptom-severity vectors and contrasts against Euclidean distance.

  4. Mining of Massive Datasets (3rd Edition) - Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman - Cambridge University Press - Credited for its widely used random-surfer derivation of PageRank and its treatment of Jaccard similarity and link prediction, the exact formulas this chapter works out by hand on a provider referral network.

  5. Networks, Crowds, and Markets: Reasoning About a Highly Connected World - David Easley and Jon Kleinberg - Cambridge University Press - A standard network-science text credited for its intuitive, case-study-driven explanations of centrality and structural bridges, echoing this chapter's bridge-versus-hub referral example.

  6. Centrality Algorithms - Neo4j Graph Data Science Documentation - Documents production implementations of degree, betweenness, closeness, PageRank, and eigenvector centrality, the same five measures this chapter defines and compares on a referral network.

  7. PageRank Algorithm - GeeksforGeeks - Walks through PageRank's formula and a worked multi-page example with code, reinforcing the recursive damping-factor calculation this chapter applies to a provider network.

  8. Similarity Functions - Neo4j Graph Data Science Documentation - Documents Jaccard, cosine, and other similarity functions with Cypher examples, the production-grade implementation of the two similarity measures this chapter derives by hand.

  9. Link Prediction Algorithms - Neo4j Graph Data Science Documentation - Documents Adamic-Adar, common neighbors, and other topological link-prediction functions, the production versions of the formula this chapter computes by hand to predict a future referral edge.

  10. Dijkstra's Shortest Path Algorithm - GeeksforGeeks - Explains the priority-queue-based algorithm this chapter traces step by step over a weighted referral network to find the minimum-cost path between two providers.