DocumentCode :
2984072
Title :
IRIE: Scalable and Robust Influence Maximization in Social Networks
Author :
Kyomin Jung ; Wooram Heo ; Wei Chen
Author_Institution :
KAIST, Daejeon, South Korea
fYear :
2012
fDate :
10-13 Dec. 2012
Firstpage :
918
Lastpage :
923
Abstract :
Influence maximization is the problem of selecting top k seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates the advantages of influence ranking (IR) and influence estimation (IE) methods for influence maximization in both the independent cascade (IC) model and its extension IC-N that incorporates negative opinion propagations. Through extensive experiments, we demonstrate that IRIE matches the influence coverage of other algorithms while scales much better than all other algorithms. Moreover IRIE is much more robust and stable than other algorithms both in running time and memory usage for various density of networks and cascade size. It runs up to two orders of magnitude faster than other state-of-the-art algorithms such as PMIA for large networks with tens of millions of nodes and edges, while using only a fraction of memory.
Keywords :
belief maintenance; marketing; optimisation; social networking (online); IC model; IRIE algorithm; independent cascade model; influence diffusion model; influence estimation method; influence maximization; influence ranking method; negative opinion propagation; social network; viral marketing; Algorithm design and analysis; Computational modeling; Greedy algorithms; Integrated circuit modeling; Mathematical model; Social network services; independent cascade model; influence maximization; social network analysis; social network mining; viral marketing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining (ICDM), 2012 IEEE 12th International Conference on
Conference_Location :
Brussels
ISSN :
1550-4786
Print_ISBN :
978-1-4673-4649-8
Type :
conf
DOI :
10.1109/ICDM.2012.79
Filename :
6413832
Link To Document :
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