Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
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DOI：
10.1145/3336191.3371829
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Carl Yang;Jieyu Zhang;Haonan Wang-;Sha Li;M. Kim;Matthew Walker;Yiou Xiao;Jiawei Han
中科院分区：
文献类型：
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作者：
Carl Yang;Jieyu Zhang;Haonan Wang-;Sha Li;M. Kim;Matthew Walker;Yiou Xiao;Jiawei Han
虽然节点语义已经在社交网络中被广泛地探索，但是很少有研究关注边缘语义，即，社会关系。理想的边语义不仅应该显示两个用户是连接的，而且应该显示他们为什么认识对方以及他们有什么共同点。然而，社交网络中的关系通常难以处理，这是由于噪声多模态信号和有限的用户生成的地面实况标签。在这项工作中，我们的目标是开发一个统一的艾德和原则性的框架，可以prole用户关系作为边缘语义在社交网络中集成多模态信号在噪声和不完整的数据的存在。我们的框架也容易受到有限或缺乏监督的影响。具体来说，我们假设每个用户链接下的多个关系的潜在分布，并使用多模态图边变分自编码器学习它们。我们用一个图卷积网络对网络数据进行编码，用多个重构网络对任意信号进行解码。在两个公开的DBLP作者网络和两个内部LinkedIn成员网络上的大量实验和案例研究表明了我们提出的模型的上级有效性和科学性。
While node semantics have been extensively explored in social networks, little research attention has been paid to pro le edge semantics, i.e., social relations. Ideal edge semantics should not only show that two users are connected, but also why they know each other and what they share in common. However, relations in social networks are often hard to pro le, due to noisy multi-modal signals and limited user-generated ground-truth labels. In this work, we aim to develop a uni ed and principled frame- work that can pro le user relations as edge semantics in social networks by integrating multi-modal signals in the presence of noisy and incomplete data. Our framework is also exible towards limited or missing supervision. Speci cally, we assume a latent distribution of multiple relations underlying each user link, and learn them with multi-modal graph edge variational autoencoders. We encode the network data with a graph convolutional network, and decode arbitrary signals with multiple reconstruction networks. Extensive experiments and case studies on two public DBLP author networks and two internal LinkedIn member networks demonstrate the superior e ectiveness and e ciency of our proposed model.