Dynamic community detection using enhanced GraphSage deep model with fast semi-supervised time-step label matching in social networks

In dynamic social networks, the frequent changes in nodes and links pose significant challenges for community detection. Traditional community detection methods encounter with issues such as random behavior, high time complexity, or low efficiency across time steps. This paper presents a novel framework, called DyGraphSage, which integrates an enhanced GraphSage model with a Temporal GRU to identify community structures. DyGraphSage begins by defining both conventional and newly introduced structural features and employing GraphSage embeddings to learn network representations and detect communities in the initial snapshot. To address temporal evolution, two strategies are proposed for updating node labels in subsequent time steps. Primarily, a new semi-supervised method is introduced to efficiently update labels when only minor structural changes occur between consecutive snapshots. Alternatively, when substantial modifications are detected, the model retrains itself using an adaptive thresholding mechanism. A new efficient equation is proposed to compute the threshold (θ) dynamically, based on node structures and their connections across previous and current network snapshots. This process allows the model to manage community updates using adjusted weights and biases, eliminating the need for reinitialization. Experimental results demonstrate that DyGraphSage outperforms several state-of-the-art dynamic community detection algorithms, particularly in terms of NMI and ARI metrics.