2014 MMRateInferringMultiAspectDiffu

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Subject Headings: Diffusion Network.

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Abstract

Inferring diffusion networks from traces of cascades has been extensively studied to better understand information diffusion in many domains. A widely used assumption in previous work is that the diffusion network is homogenous and diffusion processes of cascades follow the same pattern. However, in social media, users may have various interests and the [[social connection}connection]]s among them are usually multi-faceted. In addition, different cascades normally diffuse at different speeds and spread to diverse scales, and hence show various diffusion patterns. It is challenging for traditional models to capture the heterogeneous user interactions and diverse patterns of cascades in social media. In this paper, we investigate a novel problem of inferring multi-aspect diffusion networks with multi-pattern cascades. In particular, we study the effects of various diffusion patterns on the information diffusion process by analyzing users' retweeting behavior on a microblogging dataset. By incorporating aspect-level user interactions and various diffusion patterns, a new model for inferring Multi-aspect transmission Rates between users using Multi-pattern cascades (MMRate) is proposed. We also provide an Expectation Maximization algorithm to effectively estimate the parameters. Experimental results on both synthetic and microblogging datasets demonstrate the superior performance of our approach over the state-of-the-art methods in inferring multi-aspect diffusion networks.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2014 MMRateInferringMultiAspectDiffuPhilip S. Yu
Zhoujun Li
Senzhang Wang
Xia Hu		MMRate: Inferring Multi-aspect Diffusion Networks with Multi-pattern Cascades10.1145/2623330.26237282014
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