Fiedler-vector

Spectral Decomposition of Conflict Clusters: Extracting Opposition Factions via Laplacian Eigenvectors

Using graph Laplacian analysis and Fiedler vectors to reveal hidden factional structure in multi-agent conflict networks

Repeated agent conflicts can form factional structures that are hard to detect from pairwise analysis alone. This paper applies spectral graph theory by constructing conflict-graph Laplacians, analyzing eigenspectra, and using the Fiedler vector to partition opposition groups. We extend to k-faction decomposition via higher eigenvectors and present visualization methods that translate spectral patterns into operational governance signals.