Sciweavers

Share
SDM
2010
SIAM

Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization

9 years 19 days ago
Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization
We study the application of spectral clustering, prediction and visualization methods to graphs with negatively weighted edges. We show that several characteristic matrices of graphs can be extended to graphs with positively and negatively weighted edges, giving signed spectral clustering methods, signed graph kernels and network visualization methods that apply to signed graphs. In particular, we review a signed variant of the graph Laplacian. We derive our results by considering random walks, graph clustering, graph drawing and electrical networks, showing that they all result in the same formalism for handling negatively weighted edges. We illustrate our methods using examples from social networks with negative edges and bipartite rating graphs.
Jérôme Kunegis, Stephan Schmidt, Andr
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2010
Where SDM
Authors Jérôme Kunegis, Stephan Schmidt, Andreas Lommatzsch, Jürgen Lerner, Ernesto William De Luca, Sahin Albayrak
Comments (0)
books