Information Cascading in Social Networks

This chapter introduces the application of information cascading analysis in social networks. We present a deep learning based framework of social network information cascade analysis, and we show the challenges of applying the MDATA model. The phenomenon of information dissemination in social networks is widespread, and Social Network Information Cascade Analysis (SNICA) aims to acquire valuable knowledge in the process of information dissemination in social networks. As the number, volume, and resolution of social network data increase rapidly, traditional social network data analysis methods, especially the analysis method of social network graph (SNG) data have become overwhelmed in SNICA. At the same time, the MDATA model fuses data from multiple sources in a graph, which can be applied to the SNICA problems. Recently, deep learning models have changed this situation, and it has achieved success in SNICA with its powerful implicit feature extraction capabilities. This chapter provides a comprehensive survey of recent progress in applying deep learning techniques for SNICA.