DeepCas: an End-to-end Predictor of Information Cascades

TL;DR

This paper introduces DeepCas, an end-to-end deep learning model that automatically predicts the future size of information cascades in social networks by learning comprehensive cascade graph representations without manual feature engineering.

Contribution

The paper proposes a novel deep learning approach for cascade prediction that outperforms existing feature-based and graph kernel methods by learning holistic cascade graph representations.

Findings

DeepCas significantly improves prediction accuracy over baselines.

Node embeddings alone are insufficient for effective cascade prediction.

End-to-end learning of cascade graphs enhances generalization across platforms.

Abstract

Information cascades, effectively facilitated by most social network platforms, are recognized as a major factor in almost every social success and disaster in these networks. Can cascades be predicted? While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features. These predictors all depend on a bag of hand-crafting features to represent the cascade network and the global network structure. Such features, always carefully and sometimes mysteriously designed, are not easy to extend or to generalize to a different platform or domain. Inspired by the recent successes of deep learning in multiple data mining tasks, we investigate whether an end-to-end deep learning approach could effectively predict the future size of cascades. Such a method automatically learns the representation of individual cascade graphs in the context of the global network structure, without hand-crafted features and heuristics. We find that node embeddings fall short of predictive power, and it is critical to learn the representation of a cascade graph as a whole. We present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines that include feature based methods, node embedding methods, and graph kernel methods. Our results also provide interesting implications for cascade prediction in general.