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Feature-enhanced probabilistic models for diffusion network inference (2012)
| Venue: | In European conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD’12 |
| Citations: | 8 - 0 self |
BibTeX
@INPROCEEDINGS{Wang12feature-enhancedprobabilistic,
author = {Liaoruo Wang and Stefano Ermon and John E. Hopcroft},
title = {Feature-enhanced probabilistic models for diffusion network inference},
booktitle = {In European conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD’12},
year = {2012},
pages = {499--514}
}
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Abstract
Abstract. Cascading processes, such as disease contagion, viral marketing, and information diffusion, are a pervasive phenomenon in many types of networks. The problem of devising intervention strategies to facilitate or inhibit such processes has recently received considerable attention. However, a major challenge is that the underlying network is often unknown. In this paper, we revisit the problem of inferring latent network structure given observations from a diffusion process, such as the spread of trending topics in social media. We define a family of novel probabilistic models that can explain recurrent cascading behavior, and take into account not only the time differences between events but also a richer set of additional features. We show that MAP inference is tractable and can therefore scale to very large real-world networks. Further, we demonstrate the effectiveness of our approach by inferring the underlying network structure of a subset of the popular Twitter following network by analyzing the topics of a large number of messages posted by users over a 10-month period. Experimental results show that our models accurately recover the links of the Twitter network, and significantly improve the performance over previous models based entirely on time. 1
Keyphrases
feature-enhanced probabilistic model diffusion network inference time difference social medium large number twitter network considerable attention additional feature underlying network many type disease contagion diffusion process pervasive phenomenon information diffusion viral marketing large real-world network popular twitter previous model intervention strategy underlying network structure experimental result major challenge novel probabilistic model map inference 10-month period latent network structure
