000000303 001__ 303
000000303 005__ 20150618054358.0
000000303 037__ $$aLERSSE-RefConfPaper-2015-002
000000303 100__ $$aHyoungshick Kim
000000303 245__ $$aA Study on the Influential Neighbors to Maximize Information Diffusion in Online Social Networks
000000303 260__ $$c2015-02-27
000000303 300__ $$a15
000000303 520__ $$aThe problem of spreading information is a topic of considerable recent interest, but the traditional influence maximization problem is inadequate for a typical viral marketer who cannot access the entire network topology. To fix this flawed assumption that the marketer can control any arbitrary k nodes in a network, we have developed a decentralized version of the influential maximization problem by influencing k neighbors rather than arbitrary users in the entire network. We present several practical strategies and evaluate their performance with a real dataset collected from Twitter during the 2010 UK election campaign. Our experimental results show that information can be efficiently propagated in online social networks using neighbors with a high propagation rate rather than those with a high number of neighbors. To examine the importance of using real propagation rates, we additionally performed an experiment under the same conditions except the use of synthetic propagation rates, which is widely used in studying the influence maximization problem and found that their results were significantly different from real-world experiences.
000000303 700__ $$aKonstantin Beznosov
000000303 700__ $$aEiko Yoneki
000000303 8560_ $$flersse-it@ece.ubc.ca
000000303 8564_ $$uhttp://lersse-dl.ece.ubc.ca/record/303/files/s40649-015-0013-8.pdf
000000303 8564_ $$uhttp://lersse-dl.ece.ubc.ca/record/303/files/s40649-015-0013-8.pdf?subformat=pdfa$$xpdfa
000000303 909C4 $$pKim, K. Beznosov, and E. Yoneki, “A Study on the Influential Neighbors to Maximize Information Diffusion in Online Social Networks” in Computational Social Networks, February 2015, v2n3.
000000303 980__ $$aRefConfPaper