# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 2 Related Work
### Community Evolution Prediction  
Most of the existing research on community evolution prediction uses four steps to make predictions. The four steps are network data split, community discovery, evolution analysis, obtaining relevant features, and making predictions. In these four steps, most of the papers focus on the last step, which is the obtaining of features.  
The existing algorithms can be divided into two types: (a) methods that do not contain historical information; and (b) methods that contain historical information. The difference between the two types of method is whether to use historical information.  
The method that does not contain historical information only uses the community of the current snapshot for prediction. The advantage of these methods is that it is relatively simple, and at the same time, the obtained feature dimension is lower, so the prediction can be made more quickly. [PERSON] et al. (2012) proposed the concept of the meta-community. Through the meta-community, the evolutionary relationship of the non-adjacent snapshot can be tracked. In addition, they used properties of influential nodes, properties of the community, and temporal changes in these properties predict community evolution. [PERSON] et al. (2015) pre-trained a multi-classifier through many synthetic networks. The multi-classifier can automatically select features useful for prediction from a feature set before prediction.  
Community evolution is a sequential process. The historical evolution of the community will affect the current evolution. The method that contains historical information considers the historical evolution, so the effect is better than the method that does not contain historical information. [PERSON] et al. (2013) hypothesized that communities in social networks are the result of user interaction. Therefore, they selected structure, content, contextual features, and the previous state of community as community features. However, their method does not consider the split and merge events of the community. [PERSON] et al. [14] used ARIMA (autoregressive integrated moving average) to predict the future changes in community features. Then, they spliced the predicted features with the community features of the historical snapshot to obtain the final feature representation. [PERSON] et al. [10] considered historical community features and the influence of leader nodes on community evolution prediction, and used the change rate of community features to predict community evolution. The above methods are mainly based on GED (group evolution discovery) [17] for community evolution analysis. [PERSON] et al. [8] proposed a new community evolution model called stable group change identification (SGCI) and used it to predict community evolution. SGCI used leadership, community density, cohesion, and group size to predict the future evolution of the community.  
Although the above methods have been able to achieve good results, these algorithms do not consider the complex characteristics of the community evolution. Specifically, the existing algorithms have the following drawbacks: first, although existing algorithms consider the influence of leader nodes on the prediction results, it is obvious that there are other types of nodes in the community. The influence of these nodes on the evolution of the community has not been considered. Second, the existing algorithms do not consider the influence between communities. The relationship between communities is an important factor affecting the evolution of the community.  
To solve the above problems, this paper proposes multi-layer feature fusion-based community evolution prediction (MCEP).