Heterogeneous Hyper-Network Embedding
Heterogeneous Hyper-Network Embedding
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DOI：
10.1109/icdm.2018.00104
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Inci M. Baytas;Cao Xiao;Fei Wang;Anil K. Jain;Jiayu Zhou
中科院分区：
文献类型：
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作者：
Inci M. Baytas;Cao Xiao;Fei Wang;Anil K. Jain;Jiayu Zhou
异构超网络用于表示数据点之间的多模态和复合交互。在这样的网络中，几种不同类型的节点形成一个超边。异构超网络嵌入在这种复杂的交互下学习分布式节点表示，同时保持网络结构。然而，这是一个具有挑战性的任务，由于多种形式和复合互动。在这项研究中，提出了一种深入的方法来嵌入异构属性超网络与复杂的和非线性的节点关系。特别地，设计了全连接和图卷积层以将不同类型的节点投影到公共低维空间中，提出了元组相似性函数以保持网络结构，并且使用基于排名的损失函数来提高嵌入空间中超边的相似性得分。在合成数据集和真实的世界数据集上对该方法进行了评估，并与基线相比获得了更好的性能。
Heterogeneous hyper-networks is used to represent multi-modal and composite interactions between data points. In such networks, several different types of nodes form a hyperedge. Heterogeneous hyper-network embedding learns a distributed node representation under such complex interactions while preserving the network structure. However, this is a challenging task due to the multiple modalities and composite interactions. In this study, a deep approach is proposed to embed heterogeneous attributed hyper-networks with complicated and non-linear node relationships. In particular, a fully-connected and graph convolutional layers are designed to project different types of nodes into a common low-dimensional space, a tuple-wise similarity function is proposed to preserve the network structure, and a ranking based loss function is used to improve the similarity scores of hyperedges in the embedding space. The proposed approach is evaluated on synthetic and real world datasets and a better performance is obtained compared with baselines.