  Since most scientific literature data are unlabeled, this makes unsupervised
graph-based semantic representation learning crucial. Therefore, an
unsupervised semantic representation learning method of scientific literature
based on graph attention mechanism and maximum mutual information (GAMMI) is
proposed. By introducing a graph attention mechanism, the weighted summation of
nearby node features make the weights of adjacent node features entirely depend
on the node features. Depending on the features of the nearby nodes, different
weights can be applied to each node in the graph. Therefore, the correlations
between vertex features can be better integrated into the model. In addition,
an unsupervised graph contrastive learning strategy is proposed to solve the
problem of being unlabeled and scalable on large-scale graphs. By comparing the
mutual information between the positive and negative local node representations
on the latent space and the global graph representation, the graph neural
network can capture both local and global information. Experimental results
demonstrate competitive performance on various node classification benchmarks,
achieving good results and sometimes even surpassing the performance of
supervised learning.
