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| Content Provider | ACM Digital Library |
|---|---|
| Author | Chen, Huan Fan, Yao-Chung |
| Abstract | Social networking service platforms have gained a great success in recent years. Analyzing the social network data from the platforms presents new opportunities for various applications. Among the applications, the social influence analysis has gained great attentions, which provide great business values in helping companies determine which potential customers to market to. However, as social networks become increasingly large, scalability is quickly becoming the major challenge for conducting the social influence analysis in large-scale social networks. To this point, the common practice is to adopt parallel processing model. However, from the initial experimentation, we find that the traffics load between nodes is very high, and becomes a bottleneck for analysis. In this paper, we present a novel approximation framework which significantly reduces the amount of data traffics for processing social influence analysis. The proposed framework exhibit high efficiency and ensures a tunable (ε, Δ) accuracy constraint, which guarantees the error in the reported result is within a factor of ε with probability (1--Δ). In addition, we conduct a comprehensive performance evaluation to validate and evaluate the proposed techniques. The experimental results clearly show the superiority of the proposed framework. |
| Starting Page | 610 |
| Ending Page | 615 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450324694 |
| DOI | 10.1145/2554850.2554952 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-03-24 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Social networking Parallel processing Approximate algorithm Business intelligence Social influence analysis |
| Content Type | Text |
| Resource Type | Article |
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