The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Growth of the flickr social network
Proceedings of the first workshop on Online social networks
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Graph OLAP: a multi-dimensional framework for graph data analysis
Knowledge and Information Systems
Overlapping Community Detection by Collective Friendship Group Inference
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Graph cube: on warehousing and OLAP multidimensional networks
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
FRINGE: a new approach to the detection of overlapping communities in graphs
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
W-entropy method to measure the influence of the members from social networks
International Journal of Web Engineering and Technology
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Nowadays, social networking services, such as Facebook, Google+ or Twitter, have been drawing increasing attention from everyday users to research and industrial communities worldwide. The success of these social networking services provides a wealth of information that, suitably managed, may offer very useful knowledge to decision making. This paper overviews the state of the art of this emerging field of study and, in particular, collects several social metrics that are useful for decision making. Furthermore, the FRINGE algorithm is proposed as a method to measure the degree of influence of a network node, i. e. to what extent a certain node is isolated or is inside a community in which it is the leader or is near him or her, as a consequence of its good performance in accuracy and computational cost. To study the advantages of our proposal, we present an in-depth analysis of the Flickr dataset. Our results show that there exists a strong correlation between the popularity of the set of photos uploaded by a user and his or her influence. This information may turn out to be very valuable for selective marketing campaigns, by taking advantage of the leadership structure of a social network, in order to spread a brand-new product widely throughout the network.