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References: Graph Theory, Algorithms, and Advanced Enterprise KG

  1. PageRank - Wikipedia - Explains the PageRank algorithm's mathematical basis, random-walk intuition, and applications beyond web search — directly supporting this chapter's treatment of decision-weighted PageRank for surfacing influential precedent decisions in context graphs.

  2. Community Detection - Wikipedia - Covers the Louvain algorithm and modularity optimization for finding natural clusters in graphs, supporting this chapter's section on using community structure to guide LLM context retrieval for related entities.

  3. Shortest Path Problem - Wikipedia - Defines shortest path algorithms including Dijkstra's algorithm and their complexity, directly relevant to this chapter's enterprise use cases: conflict-of-interest screening, logistics optimization, and audit trace navigation.

  4. Knowledge Graphs: Fundamentals, Techniques, and Applications - Aidan Hogan et al. - MIT Press - Chapters 6–9 cover knowledge graph embeddings, graph neural networks, entity resolution, information extraction, and knowledge graph quality — providing academic depth for all major ML-on-graphs topics introduced in this chapter.

  5. Building Knowledge Graphs - Mayank Kejriwal, Craig Knoblock, Pedro Szekely - MIT Press - Covers named entity recognition, relation extraction, entity disambiguation, and ontology mapping from heterogeneous enterprise sources — matching this chapter's information extraction pipeline and cross-system entity linking sections.

  6. Graph Neural Network - Wikipedia - Explains GNN architectures, neighborhood aggregation, and enterprise applications including fraud detection and link prediction — foundational reference for this chapter's GNN section on decision trace relevance prediction.

  7. JSON-LD - W3C JSON-LD Working Group - Official specification and primer for JSON for Linked Data, the interchange format this chapter identifies as the bridge between graph-native storage and JSON-centric web APIs and LLM tool interfaces.

  8. Master Data Management - Wikipedia - Covers MDM as the enterprise discipline for canonical entity records, explaining how entity resolution algorithms produce golden records — directly supporting this chapter's treatment of canonical entity models and cross-system entity linking.

  9. Embedding (machine learning) - Wikipedia - Explains vector embedding concepts, training objectives, and applications to knowledge graphs — providing the conceptual foundation for this chapter's knowledge graph embedding and semantic similarity retrieval sections.

  10. Transitive Closure - Wikipedia - Defines transitive closure as deriving all indirect relationships from chains of direct ones, supporting this chapter's section on how inference engines use forward chaining to expose full management chains from direct REPORTS_TO edges.