Attribute Network Representation Learning Based on Global Attention

The attribute network not only has complex topology,its nodes also contain rich attribute information.Attribute network represent learning methods simultaneously extracts network topology and node attribute information to learn low-dimensional vector embedding of large attribute networks.It has very important and extensive applications in network analysis techniques such as node classification,link prediction and community identification.In this paper,we first obtain the embedded vector of the network structure according to the topology of the attribute network.Then we propose to learn the attribute information of adjacent nodes through global attention mechanism,use convolutional neural network to convolve the attribute information of the node to obtain the hidden vectors,and the weight vector and correlation matrix of global attention are generated from the hidden vectors of convolution,and then the attribute embedding vector of nodes is obtained.Finally,the structure embedding vector and the attribute embedding vector are connected to obtain the joint embedding vector which reflects the network structure and the attribute simultaneously.On three real data sets,the new algorithm is compared with the current eight network embedding models on tasks such as link prediction and node classification.Experimental results show that the new algorithm has good attribute network embedding effects.

Abstract licence: CC BY-NC-ND

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