Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction
Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction
复制标题
DOI：
10.1137/1.9781611972795.94
复制
发表时间：
2009-12
期刊：
影响因子：
--
通讯作者：
Hisashi Kashima;Tsuyoshi Kato;Yoshihiro Yamanishi;Masashi Sugiyama;Koji Tsuda
中科院分区：
文献类型：
--
作者：
Hisashi Kashima;Tsuyoshi Kato;Yoshihiro Yamanishi;Masashi Sugiyama;Koji Tsuda
我们提出链接传播作为一种新的半监督学习方法的链接预测问题，其中的任务是预测未知部分的网络结构，通过使用辅助信息，如节点的相似性。由于所提出的方法可以填充张量的缺失部分，因此它适用于多关系域，允许我们同时处理多种类型的链接。我们还提出了一种基于加速共轭梯度法的链路传播算法。
We propose Link Propagation as a new semi-supervised learning method for link prediction problems, where the task is to predict unknown parts of the network structure by using auxiliary information such as node similarities. Since the proposed method can fill in missing parts of tensors, it is applicable to multi-relational domains, allowing us to handle multiple types of links simultaneously. We also give a novel efficient algorithm for Link Propagation based on an accelerated conjugate gradient method.