RNe2Vec: information diffusion popularity prediction based on repost network embedding

With the rapid development in artificial intelligence and mobile networks, the past decade has witnessed the flourish of social media, and information diffusion popularity prediction in social media has attracted wide attention in both academics and industrials. However, existing popularity prediction methods either rely heavily on human experience to handcraft the features, designate the generative model, or largely depend on the underlying user relation network for embedding learning. Motivated by the above observation, this paper studies the precise prediction of the information diffusion popularity only based on early repost information, given that the underlying user relation network is unknown. To solve this problem, we propose RNe2Vec (repost network to vector), a repost network embedding-based diffusion popularity prediction algorithm. Specifically, we first build a repost network from the early repost data, and then use biased random walks to generate node sequences, in which we elaborately design walking rules to capture different repost behaviors. After that we employ the skip-gram method to learn low dimensional node vectors from the node sequences. Finally, we apply PCA (principal component analysis) algorithm on the node vectors for dimensionality reduction, and combine the embedding features with handcrafted features to train the downstream machine learning models. Experimental results on a microblog dataset show that incorporating network embedding features can significantly improve the overall prediction accuracy.