With the rapid increase of users, Scratch, as a popular online social and programming platform, has accumulated massive project resources and complex relations across its social and programming learning network. However, it is challenging to utilize the network information for providing Scratch users with personalized services, despite there are already some useful network representation learning models. In this paper, a network representation learning model with preference-based generative adversarial nets for Scratch (ScratchGAN) is proposed to resolve this problem. In ScratchGAN, we first design a node-vector initialization approach to preserve structure information and side information of Scratch network. Then, considering to learn the fine-grained user preference information of network, we propose a novel Scratch adversarial learning model which includes a Scratch generative adversarial net and a user preference difference constraint component. The former aims to capture user preferences through a new generating strategy based on the delivery nature of preference. The latter attempts to embed users' detailed preference differences according to their interaction behaviors. ScratchGAN can mine user preferences while preserving network structure information and side information. Extensive experiments on the Scratch network show that ScratchGAN outperforms other state-of-the-art models in link prediction and recommendation tasks.

中文翻译：

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ScratchGAN：基于偏好的生成对抗网络的网络表示学习

随着用户的快速增长，Scratch 作为一个受欢迎的在线社交和编程平台，在其社交和编程学习网络中积累了海量的项目资源和复杂的关系。然而，尽管已经有一些有用的网络表示学习模型，但利用网络信息为 Scratch 用户提供个性化服务具有挑战性。在本文中，提出了一种具有基于偏好的 Scratch 生成对抗网络（ScratchGAN）的网络表示学习模型来解决这个问题。在 ScratchGAN 中，我们首先设计了一种节点向量初始化方法来保存 Scratch 网络的结构信息和边信息。然后，考虑学习网络细粒度的用户偏好信息，我们提出了一种新颖的 Scratch 对抗学习模型，其中包括 Scratch 生成对抗网络和用户偏好差异约束组件。前者旨在通过基于偏好传递性质的新生成策略来捕获用户偏好。后者试图根据用户的交互行为嵌入用户的详细偏好差异。ScratchGAN 可以在保留网络结构信息和边信息的同时挖掘用户偏好。Scratch 网络上的大量实验表明，ScratchGAN 在链接预测和推荐任务中优于其他最先进的模型。前者旨在通过基于偏好传递性质的新生成策略来捕获用户偏好。后者试图根据用户的交互行为嵌入用户的详细偏好差异。ScratchGAN 可以在保留网络结构信息和边信息的同时挖掘用户偏好。Scratch 网络上的大量实验表明，ScratchGAN 在链接预测和推荐任务中优于其他最先进的模型。前者旨在通过基于偏好传递性质的新生成策略来捕获用户偏好。后者试图根据用户的交互行为嵌入用户的详细偏好差异。ScratchGAN 可以在保留网络结构信息和边信息的同时挖掘用户偏好。Scratch 网络上的大量实验表明，ScratchGAN 在链接预测和推荐任务中优于其他最先进的模型。