Linganisha mbinu

	Uchambuzi wa Milipuko ya Maarifa yenye Uzito	Uchambuzi wa Modularityi yenye Uzito
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2010s–present	2004
Mwanzilishi	Hogan et al. and the broader knowledge graph community	Newman, M. E. J.
Aina	Network analysis variant	Community structure optimization on weighted graphs
Chanzo asilia	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.
Majina mbadala	WKGA, weighted KG analysis, confidence-weighted knowledge graph, weighted semantic network analysis	weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity
Zinazohusiana	6	5
Muhtasari	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.