NDLI: Coarsening Massive Influence Networks for Scalable Diffusion Analysis

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Coarsening Massive Influence Networks for Scalable Diffusion Analysis

Content Provider	ACM Digital Library
Author	Kawarabayashi, Ken-ichi Ohsaka, Naoto Sonobe, Tomohiro Fujita, Sumio
Abstract	Fueled by the increasing popularity of online social networks, social influence analysis has attracted a great deal of research attention in the past decade. The diffusion process is often modeled using influence graphs, and there has been a line of research that involves algorithmic problems in influence graphs. However, the vast size of today's real-world networks raises a serious issue with regard to computational efficiency. In this paper, we propose a new algorithm for reducing influence graphs. Given an input influence graph, the proposed algorithm produces a vertex-weighted influence graph, which is compact and approximates the diffusion properties of the input graph. The central strategy of influence graph reduction is coarsening, which has the potential to greatly reduce the number of edges by merging a vertex set into a single weighted vertex. We provide two implementations; a speed-oriented implementation which runs in linear time with linear space and a scalability-oriented implementation which runs in practically linear time with sublinear space. Further, we present general frameworks using our compact graphs that accelerate existing algorithms for influence maximization and influence estimation problems, which are motivated by practical applications, such as viral marketing. Using these frameworks, we can quickly obtain solutions that have accuracy guarantees under a reasonable assumption. Experiments with real-world networks demonstrate that the proposed algorithm can scale to billion-edge graphs and reduce the graph size to up to 4%. In addition, our influence maximization framework achieves four times speed-up of a state-of-the-art D-SSA algorithm, and our influence estimation framework cuts down the computation time of a simulation-based method to 3.5%.
Starting Page	635
Ending Page	650
Page Count	16
File Format	PDF
ISBN	9781450341974
DOI	10.1145/3035918.3064045
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Influence maximization Graph reduction Social networks
Content Type	Text
Resource Type	Article

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