Abstract: Deep convolutional graph learning has demonstrated effectiveness in various graph clustering tasks.However,when applied to clustering,traditional GCNs( Graph Convolutional Networks ) are designed specifically for processing static graphs.In complex,dynamically changing real-world scenarios,the graph topology evolves over time,giving rise to the scenario of dynamic graph learning,which presents the following challenges:owing to limited labeling capabilities and environmental dynamics,newly emerging nodes may either belong to known categories or fall outside all pre-existing categories.To address this issue,this paper proposes a novel open graph diffusion clustering(OGDC) model.Firstly,a Graph Deviation Diffusion(GDD) approach is put forward,which integrates variance into graph convolutional networks.In this approach,fluctuations in the standard deviation of embedding vectors are used to distinguish unlabeled nodes,while the model is trained under the guidance of the expectation-maximization(EM) algorithm.Subsequently,the diffusion model is extended and integrated with the proposed OGDC model using the Tweedie formula.This integration aims to constrain the standard deviation range in low-probability regions.Furthermore,by pulling data toward high-density regions,the model mitigates training challenges posed by edge data.Meanwhile,it generates similar samples to alleviate training difficulties caused by small-sized class samples.Experimental results confirm the effectiveness of the proposed algorithm,which outperforms baseline methods.Specifically,it achieves 1%~8% performance gains in metrics like F1-score on datasets(e.g.,Cora).

Key words: Graph clustering, Dynamic graph learning, Generative model, Deep learning, Unsupervised learning