Independent Asymmetric Embedding for Cascade Prediction on Social Networks

The prediction for information diffusion on social networks has great practical significance in marketing and public opinion control. Cascade prediction aims to predict the individuals who will potentially repost the message on the social network. One kind of methods either exploit demographical, structural, and temporal features for prediction, or explicitly rely on particular information diffusion models. The other kind of models are fully data-driven and do not require a global network structure. Thus massive diffusion prediction models based on network embedding are proposed. These models embed the users into the latent space using their cascade information, but are lack of consideration for the intervene among users when embedding. In this paper, we propose an independent asymmetric embedding method to learn social embedding for cascade prediction. Different from existing methods, our method embeds each individual into one latent influence space and multiple latent susceptibility spaces. Furthermore, our method captures the co-occurrence regulation of user combination in cascades to improve the calculating effectiveness. The results of extensive experiments conducted on real-world datasets verify both the predictive accuracy and cost-effectiveness of our approach

CasGCN: Predicting future cascade growth based on information diffusion graph

Sudden bursts of information cascades can lead to unexpected consequences such as extreme opinions, changes in fashion trends, and uncontrollable spread of rumors. It has become an important problem on how to effectively predict a cascade' size in the future, especially for large-scale cascades on social media platforms such as Twitter and Weibo. However, existing methods are insufficient in dealing with this challenging prediction problem. Conventional methods heavily rely on either hand crafted features or unrealistic assumptions. End-to-end deep learning models, such as recurrent neural networks, are not suitable to work with graphical inputs directly and cannot handle structural information that is embedded in the cascade graphs. In this paper, we propose a novel deep learning architecture for cascade growth prediction, called CasGCN, which employs the graph convolutional network to extract structural features from a graphical input, followed by the application of the attention mechanism on both the extracted features and the temporal information before conducting cascade size prediction. We conduct experiments on two real-world cascade growth prediction scenarios (i.e., retweet popularity on Sina Weibo and academic paper citations on DBLP), with the experimental results showing that CasGCN enjoys a superior performance over several baseline methods, particularly when the cascades are of large scale