A two-stage deep graph clustering method for identifying the evolutionary patterns of the time series of animation view counts

Abstract

Time-series clustering of view counts with changes in online time can identify animated series with similar evolutionary count patterns over time, which may help companies reduce their investment risk. For time-series data of animation view counts, most existing time-series clustering methods ignore both the local relationships within data and global relationships between data, making it challenging to identify animated series with similar evolutionary patterns. Therefore, we propose a two-stage deep graph clustering method that involves graph data construction and deep graph clustering. Specifically, graph data construction converts time-series data into graph data, while deep graph clustering uses a temporal convolutional network and graph convolutional network to learn features from the time-series data and graph data, respectively. The entire model is then trained end-to-end to complete the clustering task, utilizing a dual self-supervised module. This study utilizes two datasets to evaluate the clustering performance of our proposed model in comparison to baseline methods. The datasets consist of view count data from 185 animated series on Tencent Video from 2018 to 2022, as well as a public time-series BME dataset. The experimental results demonstrate that our model outperforms the baseline methods on both datasets. Additionally, through visualizing the identified three classes of evolutionary patterns, we propose new product development strategies for animation enterprises.