RaRE: Social Rank Regulated Large-scale Network Embedding
RaRE: Social Rank Regulated Large-scale Network Embedding
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DOI：
10.1145/3178876.3186102
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Yupeng Gu;Yizhou Sun;Yanen Li;Yang Yang-Yang
中科院分区：
文献类型：
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作者：
Yupeng Gu;Yizhou Sun;Yanen Li;Yang Yang-Yang
由于在许多基于网络的任务中，将网络中的节点映射到低维矢量空间的网络是普遍存在的彼此之间的概率通常是在网络科学中解释的。在链接生成中考虑了两个因素，并分别学习基于邻近的嵌入和基于社会等级的嵌入，而不是仅仅将这两个因素彼此独立地处理，因此提出了一个精心设计的链接生成模型，这明确地模拟了这两个类型的嵌入方式，这两个相互依存关系是相互依存的。在最新方法上。
Network embedding algorithms that map nodes in a network into a low-dimensional vector space are prevalent in recent years, due to their superior performance in many network-based tasks, such as clustering, classification, and link prediction. The main assumption of existing algorithms is that the learned latent representation for nodes should preserve the structure of the network, in terms of first-order or higher-order connectivity. In other words, nodes that are more similar will have higher probability to connect to each other. This phenomena is typically explained as homophily in network science. However, there is another factor usually neglected by the existing embedding algorithms, which is the popularity of a node. For example, celebrities in a social network usually receive numerous followers, which cannot be fully explained by the similarity of the two users. We denote this factor with the terminology "social rank»». We then propose a network embedding model that considers both of the two factors in link generation, and learn proximity-based embedding and social rank-based embedding separately. Rather than simply treating these two factors independent with each other, a carefully designed link generation model is proposed, which explicitly models the interdependency between these two types of embeddings. Experiments on several real-world datasets across different domains demonstrate the superiority of our novel network embedding model over the state-of-the-art methods.