Particle Propagation Model for Dynamic Node Classification

Abstract

With the popularity of online social networks, researches on dynamic node classification have received further attention. Dynamic node classification also helps the rapid popularization of online social networks. This paper proposes a particle competition model named DPP to complete the dynamic node classification. Existing node classification models based on particle competition do not perform well in terms of accuracy. Hence, we formulate a unique particle competition framework to make the node classification more effective. In addition, for applying the model in the dynamic network, based on the dynamic characteristics of the model, we have added an automatic update strategy of the source node to the model. The particles in the new model perform the steps of walking, splitting, and jumping according to the method introduced in this paper. Then, the domination matrix of the network has been changed with particle movements continuously. Although the particles randomly walk at the micro-level, the model can converge to obtain the node classification results. Finally, simulation results show both the effectiveness and superiority of our proposed node classification model with the comparison of other major particle competition models and dynamic node classification methods. Based on the above contributions, the proposed model may have compelling applications in the context of community detection and network embedding, etc. INDEX TERMS Complex network, particle competition, dynamic node classification.

Link Prediction and Node Classification Based on Multitask Graph Autoencoder

“…Thus, a mass of relationships between online users are worth exploring. By capturing the structural characteristics of real-world networks, experts and scholars can deal with multiple data analysis tasks efficiently, such as community detection [3], link prediction [4,5], and node classification [6]. The emergence of network representation learning [7,8] technology is of vital significance to social network analysis.…”