  Network embedding, as a promising way of the network representation learning,
is capable of supporting various subsequent network mining and analysis tasks,
and has attracted growing research interests recently. Traditional approaches
assign each node with an independent continuous vector, which will cause huge
memory overhead for large networks. In this paper we propose a novel multi-hot
compact embedding strategy to effectively reduce memory cost by learning
partially shared embeddings. The insight is that a node embedding vector is
composed of several basis vectors, which can significantly reduce the number of
continuous vectors while maintain similar data representation ability.
Specifically, we propose a MCNE model to learn compact embeddings from
pre-learned node features. A novel component named compressor is integrated
into MCNE to tackle the challenge that popular back-propagation optimization
cannot propagate through discrete samples. We further propose an end-to-end
model MCNE$_{t}$ to learn compact embeddings from the input network directly.
Empirically, we evaluate the proposed models over three real network datasets,
and the results demonstrate that our proposals can save about 90\% of memory
cost of network embeddings without significantly performance decline.
