A Block-based Generative Model for Attributed Networks Embedding
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Attributed network embedding has attracted plenty of interests in recent years. It aims to learn task-independent, low-dimension, and continuous vectors for nodes preserving both topology and attribute information. Most existing methods, such as GCN and its variations, mainly focus on the local information, i.e., the attributes of the neighbors. Thus, they have been well studied for assortative networks but ignored disassortative networks, which are common in real scenes. To address this issue, we propose a block-based generative model for attributed network embedding on a probability perspective inspired by the stochastic block model (SBM). Specifically, the nodes are assigned to several blocks wherein the nodes in the same block share the similar link patterns. These patterns can define assortative networks containing communities or disassortative networks with the multipartite, hub, or any hybrid structures. Concerning the attribute information, we assume that each node has a hidden embedding related to its assigned block, and then we use a neural network to characterize the nonlinearity between the node embedding and its attribute. We perform extensive experiments on real-world and synthetic attributed networks, and the experimental results show that our proposed method remarkably outperforms state-of-the-art embedding methods for both clustering and classification tasks, especially on disassortative networks.
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