Módszerek összehasonlítása

	Gráf-elemzési keretrendszer	Modularity Analysis
Tudományterület	Hálózatelemzés	Hálózatelemzés
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	2012–2016	2004
Megalkotó	Ehrlinger, L. & Wöß, W.; Google (popularized)	Newman, M. E. J. & Girvan, M.
Típus	Graph-based knowledge representation and analysis	Community detection / graph partitioning
Alapmű	Ehrlinger, L. & Wöß, W. (2016). Towards a Definition of Knowledge Graphs. In Proceedings of the SEMANTICS Posters and Demos Track (SEMANTiCS 2016). CEUR Workshop Proceedings, vol. 1695.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Alternatív nevek	KG analysis, semantic graph analysis, knowledge base graph analysis, entity-relation graph analysis	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Kapcsolódó	5	5
Összefoglaló	Knowledge Graph Analysis is a framework for representing, storing, and reasoning over structured factual knowledge as a directed graph of entities and typed relations. Entities (nodes) and relationships (edges) are expressed as subject–predicate–object triples, enabling rich querying, inference, and integration of heterogeneous data sources across domains such as biomedical research, e-commerce, and scientific literature.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.