  Network representation learning aims to generate an embedding for each node
in a network, which facilitates downstream machine learning tasks such as node
classification and link prediction. Current work mainly focuses on transductive
network representation learning, i.e. generating fixed node embeddings, which
is not suitable for real-world applications. Therefore, we propose a new
inductive network representation learning method called MNCI by mining
neighborhood and community influences in temporal networks. We propose an
aggregator function that integrates neighborhood influence with community
influence to generate node embeddings at any time. We conduct extensive
experiments on several real-world datasets and compare MNCI with several
state-of-the-art baseline methods on various tasks, including node
classification and network visualization. The experimental results show that
MNCI achieves better performance than baselines.
