Finding Influential Nodes in a Social Network from Information Diffusion Data Export

@incollection{citeulike:5087132, abstract = {We address the problem of ranking influential nodes in complex social networks by estimating diffusion probabilities from observed information diffusion data using the popular independent cascade (IC) model. For this purpose we formulate the likelihood for information diffusion data which is a set of time sequence data of active nodes and propose an iterative method to search for the probabilities that maximizes this likelihood. We apply this to two real world social networks in the simplest setting where the probability is uniform for all the links, and show that the accuracy of the probability is outstandingly good, and further show that the proposed method can predict the high ranked influential nodes much more accurately than the well studied conventional four heuristic methods.}, author = {Kimura, Masahiro and Saito, Kazumi and Nakano, Ryohei and Motoda, Hiroshi}, citeulike-article-id = {5087132}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-1-4419-0056-2_18}, citeulike-linkout-1 = {http://www.springerlink.com/content/p54n387114265075}, doi = {10.1007/978-1-4419-0056-2_18}, journal = {Social Computing and Behavioral Modeling}, keywords = {diffusion, diffusion\_data, dynamics, influence, socialnetworks, toread}, pages = {1--8}, posted-at = {2009-10-15 01:55:08}, priority = {2}, title = {Finding Influential Nodes in a Social Network from Information Diffusion Data}, url = {http://dx.doi.org/10.1007/978-1-4419-0056-2_18}, year = {2009} }

TY - CHAP ID - citeulike:5087132 L3 - citeulike-article-id:5087132 N2 - We address the problem of ranking influential nodes in complex social networks by estimating diffusion probabilities from observed information diffusion data using the popular independent cascade (IC) model. For this purpose we formulate the likelihood for information diffusion data which is a set of time sequence data of active nodes and propose an iterative method to search for the probabilities that maximizes this likelihood. We apply this to two real world social networks in the simplest setting where the probability is uniform for all the links, and show that the accuracy of the probability is outstandingly good, and further show that the proposed method can predict the high ranked influential nodes much more accurately than the well studied conventional four heuristic methods. JF - Social Computing and Behavioral Modeling EP - 8 TI - Finding Influential Nodes in a Social Network from Information Diffusion Data SP - 1 KW - diffusion KW - diffusion_data KW - dynamics KW - influence KW - socialnetworks KW - toread AU - Kimura, Masahiro AU - Saito, Kazumi AU - Nakano, Ryohei AU - Motoda, Hiroshi PY - 2009/// UR - http://dx.doi.org/10.1007/978-1-4419-0056-2_18 ER -