计算机科学 ›› 2026, Vol. 53 ›› Issue (4): 143-154.doi: 10.11896/jsjkx.250300147
张雪芹1,2, 王智能1, 李晋生1, 陆一松1, 罗飞1
ZHANG Xueqin1,2, WANG Zhineng1, LI Jinsheng1, LU Yisong1, LUO Fei1
摘要： 社交网络是信息传播的主要渠道,识别社交网络中的关键节点对发现信息传播枢纽、进行信息传播控制等具有重要意义。现实社交网络具有时变性,合理建模时序网络,并对节点的空间和时间关系进行全面描述和深度挖掘,是准确识别网络关键节点的重要因素。为了提高关键节点识别的精度,提出了一种基于深度学习和多特征融合的时序社交网络关键节点识别方法MCNN(Multidimensional CNN)。该方法首先将时序网络建模为基于快照的多维关系网络,对于一个节点,在每个快照,分别从空间结构、时间耦合和三类时空传播关系,提取节点的空间、时间和时空上下文,并构建节点特征矩阵。为了深度分析节点在每个快照中的时空关系,使用卷积神经网络CNN分别提取3类节点特征,并使用自注意力机制融合形成节点快照特征。为了捕捉节点行为在快照间的演变,组合所有快照的节点快照特征作为时间序列,采用长短期记忆网络LSTM挖掘快照序列特征。最后,使用全连接层预测节点的影响力。在6个真实时序社交网络上的实验结果表明,MCNN在时序社交网络关键节点识别方面优于基线方法。
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