Abstract
Cascades are ubiquitous in various network environments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomena (i.e. cascading process) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node's neighbors getting infected by a cascade after this node getting infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics, and present a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Data Mining, ICDM |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 559-568 |
| Number of pages | 10 |
| Volume | 2016-January |
| ISBN (Print) | 9781467395038 |
| DOIs | |
| State | Published - Jan 5 2016 |
| Event | 15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States Duration: Nov 14 2015 → Nov 17 2015 |
Other
| Other | 15th IEEE International Conference on Data Mining, ICDM 2015 |
|---|---|
| Country | United States |
| City | Atlantic City |
| Period | 11/14/15 → 11/17/15 |
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Keywords
- Dynamic Processes Prediction
- Information Cascades
- Social Network
ASJC Scopus subject areas
- Engineering(all)
Cite this
From micro to macro : Uncovering and predicting information cascading process with behavioral dynamics. / Yu, Linyun; Cui, Peng; Wang, Fei; Song, Chaoming; Yang, Shiqiang.
Proceedings - IEEE International Conference on Data Mining, ICDM. Vol. 2016-January Institute of Electrical and Electronics Engineers Inc., 2016. p. 559-568 7373360.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - From micro to macro
T2 - Uncovering and predicting information cascading process with behavioral dynamics
AU - Yu, Linyun
AU - Cui, Peng
AU - Wang, Fei
AU - Song, Chaoming
AU - Yang, Shiqiang
PY - 2016/1/5
Y1 - 2016/1/5
N2 - Cascades are ubiquitous in various network environments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomena (i.e. cascading process) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node's neighbors getting infected by a cascade after this node getting infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics, and present a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.
AB - Cascades are ubiquitous in various network environments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomena (i.e. cascading process) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node's neighbors getting infected by a cascade after this node getting infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics, and present a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.
KW - Dynamic Processes Prediction
KW - Information Cascades
KW - Social Network
UR - http://www.scopus.com/inward/record.url?scp=84963615160&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963615160&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2015.79
DO - 10.1109/ICDM.2015.79
M3 - Conference contribution
SN - 9781467395038
VL - 2016-January
SP - 559
EP - 568
BT - Proceedings - IEEE International Conference on Data Mining, ICDM
PB - Institute of Electrical and Electronics Engineers Inc.
ER -