Arboricity and subgraph listing algorithms
SIAM Journal on Computing
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Edge Metrics for Visual Graph Analytics: A Comparative Study
IV '08 Proceedings of the 2008 12th International Conference Information Visualisation
The h-Index of a Graph and Its Application to Dynamic Subgraph Statistics
WADS '09 Proceedings of the 11th International Symposium on Algorithms and Data Structures
Heider vs simmel: emergent features in dynamic structures
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Link prediction with social vector clocks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Empirical social networks are often aggregate proxies for several heterogeneous relations. In online social networks, for instance, interactions related to friendship, kinship, business, interests, and other relationships may all be represented as catchall "friendships." Because several relations are mingled into one, the resulting networks exhibit relatively high and uniform density. As a consequence, the variation in positional differences and local cohesion may be too small for reliable analysis. We introduce a method to identify the essential relationships in networks representing social interactions. Our method is based on a novel concept of triadic cohesion that is motivated by Simmel's concept of membership in social groups. We demonstrate that our Simmelian backbones are capable of extracting structure from Facebook interaction networks that makes them easy to visualize and analyze. Since all computations are local, the method can be restricted to partial networks such as ego networks, and scales to big data.