Connections aren't everything.

Who are the best spreaders of information in a social network? Best connected individuals may not be the most influential spreaders. Instead, location in the network, as defined by the k-shell, determines influence. Paper: pdf or cond-mat. Press releases: Technology review, Science Daily, Fast company, Science for SEO, Emedia, NSF, India Times. Collaboration between Bar-Ilan University, Boston University, Stockholm University, NYU, and CCNY. Nature Physics 2010. (High resolution image and cover, created with the lanet-vi tool).

Recent analysis based on real diffusion data from extensive datasets including LiveJournal, Facebook, Twitter and publications in APS journals suggests that k-shell is a better predictor of nodes' influence in practice, outperforming degree and PageRank. [For datasets see here.

In reality, k-shell index can identify more influential spreaders of influence, even for networks without complete information of network structure. In the cases when the global structure is unavailable, we find that the sum of the nearest neighbors' degrees is a practical proxy for user's influence. This provides a practical topological marker for super spreaders of in formation in real applications.

The k-shell structure of LiveJournal social network. Four hubs located in the periphery are highlighted by black squares.

The influence of the spreading process cannot be predicted by degree reliably. For the LiveJournal network, we compare the influence area of single nodes with the same degree k = 6902 (nodes A and B) or the same k-shell index 230 (nodes A and C). In the lower level of the corresponding plots, nodes' k-shell indexes are marked with different colors. In the upper level, nodes with green color constitute the influence area, while the grey nodes are not influenced by the source node.