Multi-Layer Feature Fusion-Based Community Evolution Prediction

Abstract

Analyzing and predicting community evolution has many important applications in criminology, sociology, and other fields. In community evolution prediction, most of the existing research is simply calculating the features of the community, and then predicting the evolution event through the classifier. However, these methods do not consider the complex characteristics of community evolution, and only predict the community’s evolution from a single level. To solve these problems, this paper proposes an algorithm called multi-layer feature fusion-based community evolution prediction, which obtains features from the community layer and node layer. The final community feature is the fusion of the two layer features. At the node layer, this paper proposes a global and local-based role-extraction algorithm. This algorithm can effectively discover different roles in the community. In this way, we can distinguish the influence of nodes with different characteristics on the community evolution. At the community layer, this paper proposes to use the community hypergraph to obtain the inter-community interaction relationship. After all the features are obtained, this paper trains a classifier through these features and uses them in community evolution prediction. The experimental results show that the algorithm proposed in this paper is better than other algorithms in terms of prediction effect.

Context-aware Community Evolution Prediction in Online Social Networks

“…Wang et al [13] approached the community evolution prediction problem across two granular stages. The first stage computes the features for individual communities by considering user roles, then predicts intra-and inter-community relationships in the second stage.…”