Interactive networks representing user participation and interactions in specific "events" are highly dynamic, with communities reflecting collective behaviors that evolve over time. Predicting these community evolutions is crucial for forecasting the trajectory of the related "event". Some models for community evolution prediction have been witnessed, but they primarily focused on coarse-grained evolution types (e.g., expand, dissolve, merge, split), often neglecting fine-grained evolution extents (e.g., the extent of community expansion). Furthermore, these models typically utilize only one network data (here is interactive network data) for dynamic community featurization, overlooking the more stable friendship network that represents the friendships between people to enrich community representations. To address these limitations, we propose a two-stage model that predicts both the type and extent of community evolution. Our model unifies multi-class classification for evolution type and regression for evolution extent within a single framework and fuses data from both interactive and friendship networks for a comprehensive community featurization. We also introduce a hybrid strategy to differentiate between evolution types that are difficult to distinguish. Experimental results on three datasets show the significant superiority of the proposed model over other models, confirming its efficacy in predicting community evolution in interactive networks.

翻译：代表用户在特定"事件"中参与及交互的交互网络具有高度动态性，其社区反映的集体行为会随时间演化。预测这些社区演化对于预判相关"事件"的发展轨迹至关重要。现有社区演化预测模型主要关注粗粒度演化类型（如扩张、解散、合并、分裂），往往忽略细粒度演化程度（如社区扩张的幅度）。此外，这些模型通常仅利用单一网络数据（此处指交互网络数据）进行动态社区特征化，未能结合表征人际关系的、更稳定的友谊网络来丰富社区表征。为克服这些局限，我们提出一个两阶段模型，可同时预测社区演化的类型与程度。该模型在统一框架内整合了演化类型的多分类任务与演化程度的回归任务，并通过融合交互网络与友谊网络的数据实现全面的社区特征化。我们还引入混合策略以区分难以辨别的演化类型。在三个数据集上的实验结果表明，所提模型显著优于其他模型，证实了其在交互网络社区演化预测中的有效性。