A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact

Quantifying and predicting the long-term impact of scientific writings or individual scholars has important implications for many policy decisions, such as funding proposal evaluation and identifying emerging research fields. In this work, we propose an approach based on Heterogeneous Dynamical Graph Neural Network (HDGNN) to explicitly model and predict the cumulative impact of papers and authors. HDGNN extends heterogeneous GNNs by incorporating temporally evolving characteristics and capturing both structural properties of attributed graph and the growing sequence of citation behavior. HDGNN is significantly different from previous models in its capability of modeling the node impact in a dynamic manner while taking into account the complex relations among nodes. Experiments conducted on a real citation dataset demonstrate its superior performance of predicting the impact of both papers and authors

Information Diffusion Prediction with Latent Factor Disentanglement

Information diffusion prediction is a fundamental task which forecasts how an information item will spread among users. In recent years, deep learning based methods, especially those based on recurrent neural networks (RNNs), have achieved promising results on this task by treating infected users as sequential data. However, existing methods represent all previously infected users by a single vector and could fail to encode all necessary information for future predictions due to the mode collapse problem. To address this problem, we propose to employ the idea of disentangled representation learning, which aims to extract multiple latent factors representing different aspects of the data, for modeling the information diffusion process. Specifically, we employ a sequential attention module and a disentangled attention module to better aggregate the history information and disentangle the latent factors. Experimental results on three real-world datasets show that the proposed model SIDDA significantly outperforms state-of-the-art baseline methods by up to 14% in terms of hits@N metric, which demonstrates the effectiveness of our method