Network representation learning aims at learning a low-dimensional vector for each node in a network, which has attracted increasing research interests recently. However, most existing approaches only use topology information of each node and ignore its attributes information. In this paper, we propose an Improved Attributed Node Random Walks(IANRW) framework, which constructs the neighborhood of an attributed node and then leverages the skip-gram model to perform node embeddings. The method can be able to flexibly incorporate both the topology and attribute information. Additionally, it can easily deal with missing data and be applied to large networks. Extensive experiments on six datasets show that IANRW outperforms many state-of-the-art embedding models and can improve various attributed networks mining tasks.
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