# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 1 Introduction  
Many complex networks show the structure of communities. A community is a set of nodes, where connections within the community are relatively dense, and connections between communities are relatively sparse [1]. The community is the basic structure of the network, and has many important applications, such as influence maximization [2, 3] and rumor tracing. Due to the wide application of community, ever since the concept of community has been proposed, many scholars have devoted themselves to related research on community. These studies include community discovery [4, 5], community evolution analysis [6], and community evolution prediction. Among many research directions related to communities, community evolution prediction is an important direction. In dynamic networks, communities change over time, which we have called community evolution. Community evolution prediction can be defined as predicting the future evolution of a community when its historical evolution is known. Community evolution prediction has many important applications in many fields. For example, in criminology [7], a criminal group can be regarded as a community, and tracking and predicting its changes can help to control them over time. In an infectious disease transmission network, by tracking the changes in infected communities, the structural characteristics of highly infectious communities can be found to predict the future spread of infectious diseases. Although community evolution prediction has important applications in many fields, there are few related studies. Most of the existing research simply calculates community features based on manually selected features and then uses classifiers to make predictions. Most of the differences among these algorithms lie in the way they calculate features [8; 9; 10; 11; 12; 13; 14; 15]. For example, in references [8; 11], the authors simply calculate features based on manual selection. In references [9; 10], the authors take into account the influence of the leader node on the evolution of the community. In the reference [15], the author automatically selects relevant features through pre-training classifiers. Although the existing algorithms can achieve good effects, they still have some shortcomings. First, the existing research does not consider the impact of the nodes with different features on the evolution of the community. To solve this problem, in references [9; 10], the authors introduced leader nodes when calculating community features. However, in reality, there are not only leader nodes in the community, but also other types of nodes. These nodes also affects the community evolution. For example, in a community, there may be nodes that want to separate. If there are many such nodes, the community is likely to split in the future. Therefore, when predicting the evolution of a community, it is not enough to only consider the features of influence nodes. Second, the existing algorithms do not consider mutual influence between communities when calculating community features. Obviously, the relationship between communities will affect the evolution of the community. For example, if there are more edges between two communities, these two communities are more likely to merge in the future. To solve the above problems, this paper proposes multi-layer feature fusion-based community evolution prediction (MCEP). MCEP obtains features from the node layer and the community layer. Then MCEP merges the obtained features and uses the merged features to predict the evolution of the community. At the node layer, we give community roles to different types of nodes in the community. In contrast to the traditional definition of the role, community roles need to consider both the global scope and local scope features of nodes. Therefore, the traditional role-discovery algorithm [16] cannot be used to discover community roles. To accurately discover community roles, we propose the global and local-based community role-extraction algorithm.