Characterization of clusterability of signed graph in terms of Newcomb's balance of sentiments
Applied Mathematics and Computation
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
EigenSpokes: surprising patterns and scalable community chipping in large graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
| Hi-index | 0.00 |
Previous studies on social networks are often focused on networks with only positive relations between individual nodes. As a significant extension, we conduct the spectral analysis on graphs with both positive and negative edges. Specifically, we investigate the impacts of introducing negative edges and examine patterns in the spectral space of the graph's adjacency matrix. Our theoretical results show that communities in a k-balanced signed graph are distinguishable in the spectral space of its signed adjacency matrix even if connections between communities are dense. This is quite different from recent findings on unsigned graphs, where communities tend to mix together in the spectral space when connections between communities increase. We further conduct theoretical studies based on graph perturbation to examine spectral patterns of general unbalanced signed graphs. We illustrate our theoretical findings with various empirical evaluations.