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References: Centrality and Pathfinding

  1. Centrality - Wikipedia - Comprehensive treatment of centrality measures including degree, betweenness, closeness, eigenvector, and PageRank. Covers mathematical definitions, computation, and interpretation for network analysis.

  2. Shortest Path Problem - Wikipedia - Overview of pathfinding algorithms including Dijkstra's algorithm, Bellman-Ford, and A* search. Explains how shortest paths reveal communication efficiency and organizational distance.

  3. PageRank - Wikipedia - Explains the iterative algorithm originally designed for web page ranking that measures node importance based on the quality and quantity of incoming links. Applicable to identifying influential employees in communication networks.

  4. The Hidden Power of Social Networks - Rob Cross and Andrew Parker - Harvard Business Review Press (2004) - Chapters 2-3 demonstrate how centrality metrics identify the four network roles (central connectors, boundary spanners, information brokers, peripheral specialists) in real organizational networks.

  5. Networks, Crowds, and Markets: Reasoning About a Highly Connected World - David Easley and Jon Kleinberg - Cambridge University Press (2010) - Rigorous yet accessible treatment of network analysis including centrality, graph structure, and information cascades. Free online version available from authors.

  6. Betweenness Centrality - Wikipedia - Detailed explanation of betweenness centrality measuring how often a node lies on shortest paths between other nodes. Key metric for identifying communication bottlenecks and brokers in organizations.

  7. Dijkstra's Algorithm - Wikipedia - Step-by-step explanation of the classic shortest-path algorithm with worked examples. Foundation for weighted pathfinding queries on organizational communication graphs.

  8. Breadth-First Search - Wikipedia - Explains BFS traversal for exploring graph neighborhoods level by level. Essential algorithm for computing shortest unweighted paths and discovering an employee's extended network.

  9. Neo4j Graph Data Science Library - Neo4j - Documentation for Neo4j's built-in graph algorithm library including centrality, community detection, pathfinding, and similarity algorithms used throughout this course.

  10. Network Science - Chapter 7: Degree Correlation - Barabási Lab - Free online chapter explaining how degree distributions and correlations create hubs and hierarchies in networks, directly applicable to understanding organizational power structures.