ACL 2017论文《CANE: Context-Aware Network Embedding for Relation Modeling》，以往的网络表示学习模型只会为网络节点学习固定的表示向量，而实际上，网络节点会根据交互对象的不对展现出不同的特性。在这篇工作中，我们提出了上下文敏感的网络表示的概念，利用mutual
attention技术为每个节点根据交互的邻居的不同学习不同的表示。该模型在链接预测上取得了不错的效果，而且能够对节点之间的关系进行解释。
摘要：
Network embedding (NE) is playing a critical role in network analysis, due to its ability to represent vertices with efficient low-dimensional embedding vectors. However, existing NE models aim to learn a fixed context-free embedding for each vertex and neglect the diverse roles when interacting with other vertices. In this paper, we assume that one vertex usually shows different aspects when interacting with different neighbor vertices, and should own different embeddings respectively. Therefore, we present Context-Aware Network Embedding (CANE), a novel NE model to address this issue. CANE learns context-aware embeddings for vertices with mutual attention mechanism and is expected to model the semantic relationships between vertices more precisely. In experiments, we compare our model with existing NE models on three real-world datasets. Experimental results show that CANE achieves significant improvement than state-of-the-art methods on link prediction and comparable performance on vertex classification. The source code and datasets can be obtained from https://github.com/thunlp/CANE.
论文链接：
http://t.cn/RaegPk5
数据和代码：
https://github.com/thunlp/cane
原文链接：
http://weibo.com/2313655094/F4qDweTJv?type=repost#_rnd1495707361922
