手法を比較

	重み付き知識グラフ分析	加重モジュラリティ分析
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Machine learning
提唱年	2010s–present	2004
提唱者	Hogan et al. and the broader knowledge graph community	Newman, M. E. J.
種類	Network analysis variant	Community structure optimization on weighted graphs
原典	Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1–37.	Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131.
別名	WKGA, weighted KG analysis, confidence-weighted knowledge graph, weighted semantic network analysis	weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity
関連	6	5
概要	Weighted Knowledge Graph Analysis extends standard knowledge graph methods by assigning numerical weights — such as confidence scores, co-occurrence frequencies, or relation strengths — to edges between entities. These weights allow analysts to prioritise high-confidence triples, find the most influential paths, and compute weight-aware centrality and community structure in large structured knowledge bases.	Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully.