Sparse Attributed Network Embedding via Adaptively Aggregating Neighborhood Information

Network representation learning (NRL), which aims to map nodes in a network into low-dimensional vectors, has attracted wide attention due to its potential on various net-work applications. Recently, attributed network embedding that incorporates both network structure and node attributes has shown a good performance in NRL. However, most existing methods cannot achieve promising results in sparse attributed networks because of the lack of structural information. In order to further alleviate the negative effect of sparseness, we propose a novel model named AANI (Adaptively Aggregating Neighborhood Information). Specifically, AANI exploits the information of current node and its neighborhood in a smoothing way to obtain more informative node representations. At the same time, AANI introduces attention mechanism to adaptively aggregate the smoothed information according to their respective importance. We conduct extensive experiments on three real-world datasets which demonstrate that AANI outperforms the state-of-the-art embedding methods in sparse attributed networks.