![]() Experimental results demonstrate that, compared with the state-of-the-art static heuristic, IncInf achieves as much as 21X speedup in execution time while maintaining matching performance in terms of influence spread.R each great heights by building upon the achievements of othersĪs a student, you may often feel daunted by your professors. ![]() We carried out extensive experiments on real-world dynamic social networks including Facebook, NetHEPT, and Flickr. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to effectively narrow the search space into nodes experiencing major increases or with high degrees. Such observations shed light on the design of IncInf, an incremental approach that can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In this paper, we observe from real-world traces that the evolution of social network follows the preferential attachment rule and the influential nodes are mainly selected from high-degree nodes. ![]() ![]() While, as a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time, significantly affecting the efficiency. Previous studies mainly focus on designing efficient algorithms or heuristics to find top-K influential nodes on a given static social network. Download a PDF of the paper titled On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks, by Xiaodong Liu and 6 other authors Download PDF Abstract:Identifying the most influential individuals can provide invaluable help in developing and deploying effective viral marketing strategies. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |