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.
Highlights
The particle competition in complex network can build an overall complex pattern based on the simple interactive operation of individuals
This paper proposes a semi-supervised node classification model based on particle competition
The first advantage of this model is that it improves the operating mechanism of the particle competition model, so that it can obtain more effective node classification results than those of other major particle competition models
Summary
The particle competition in complex network can build an overall complex pattern based on the simple interactive operation of individuals. The advantage of particle competition mechanism comes from it’s complex system function, which can solve a variety of problems based on the essence of individual in the network This advantage is mainly shown in two aspects: 1) Due to self-adaptive of the competition model, it may have better accuracy and adaptability than other methods that require global search. LCU, a new particle competition mechanism for solving the problem of semi-supervised learning in complex networks was proposed. In order to make the node classification result more effective than that of the current two major node classification models based on particle competition, such as SCL and LCU [35], [40], this paper redefines the particle competition mechanism and proposes the DPP model.
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