用GNN+BiLSTM预测动态图中最有影响力的节点，提升推荐效率。
GNN-Based Candidate Node Predictor for Influence Maximization in Temporal Graphs
- 融合GNN与BiLSTM捕捉图结构和时间变化特征
- 在多类网络上实现90%的候选节点预测准确率
- 适合需要实时分析社交传播的营销与网络研究
在信息通过社交媒体快速传播的时代，有效识别动态网络中的关键节点至关重要。传统影响力最大化方法难以适应关系与结构的快速演变，导致错失机会并降低效率。为此，我们提出一种基于学习的新型方法，结合图神经网络（GNN）与双向长短期记忆网络（BiLSTM）。该混合框架同时捕获图的结构特征与时间动态，实现对种子节点候选集的精准预测。BiLSTM的双向特性使模型可分析历史与未来网络状态，增强对随时间变化的适应能力。通过在每个时间快照动态调整，本方法显著提升了种子节点计算效率，在多种网络上平均达到90%的潜在种子节点预测准确率，大幅减少用于种子选择评估的节点数量，降低计算开销。该方法在病毒式营销与社交网络分析等需理解时间动态的领域尤为有效。
原文摘要 · Abstract (English)
In an age where information spreads rapidly across social media, effectively identifying influential nodes in dynamic networks is critical. Traditional influence maximization strategies often fail to keep up with rapidly evolving relationships and structures, leading to missed opportunities and inefficiencies. To address this, we propose a novel learning-based approach integrating Graph Neural Networks (GNNs) with Bidirectional Long Short-Term Memory (BiLSTM) models. This hybrid framework captures both structural and temporal dynamics, enabling accurate prediction of candidate nodes for seed set selection. The bidirectional nature of BiLSTM allows our model to analyze patterns from both past and future network states, ensuring adaptability to changes over time. By dynamically adapting to graph evolution at each time snapshot, our approach improves seed set calculation efficiency, achieving an average of 90% accuracy in predicting potential seed nodes across diverse networks. This significantly reduces computational overhead by optimizing the number of nodes evaluated for seed selection. Our method is particularly effective in fields like viral marketing and social network analysis, where understanding temporal dynamics is crucial.
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