计算机科学 ›› 2022, Vol. 49 ›› Issue (1): 133-139.doi: 10.11896/jsjkx.201000179
蒋宗礼, 樊珂, 张津丽
JIANG Zong-li, FAN Ke, ZHANG Jin-li
摘要: 现实世界中的信息网络大多为异质信息网络,旨在表示低维空间中节点数据的网络表示方法已普遍用于分析异质信息网络,从而有效融合异质网络中丰富的语义信息和结构信息。但是现有的异质网络表示方法通常采用负采样从网络中随机选择节点,并且对节点和边的异质性学习能力不足。受生成式对抗网络和元路径的启发,文中提出了一种新型的异质网络表示方法。首先对采样方法使用元路径的策略进行改进,根据元路径不同的权重取样,使样本更好地体现节点之间存在的直接和间接关系,增强样本的语义关联。然后在生成对抗的博弈过程中使模型充分考虑节点和边的异质性并具备关系感知能力,实现对异质信息网络的表示学习。实验结果表明,与目前的表示算法相比,该模型学习到的表示向量在分类和链路预测实验中具有更好的性能表现。
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