  In real-world complex networks, understanding the dynamics of their evolution
has been of great interest to the scientific community. Predicting future links
is an essential task of social network analysis as the addition or removal of
the links over time leads to the network evolution. In a network, links can be
categorized as intra-community links if both end nodes of the link belong to
the same community, otherwise inter-community links. The existing
link-prediction methods have mainly focused on achieving high accuracy for
intra-community link prediction. In this work, we propose a network embedding
method, called NodeSim, which captures both similarities between the nodes and
the community structure while learning the low-dimensional representation of
the network. The embedding is learned using the proposed NodeSim random walk,
which efficiently explores the diverse neighborhood while keeping the more
similar nodes closer in the context of the node. We verify the efficacy of the
proposed embedding method over state-of-the-art methods using diverse link
prediction. We propose a machine learning model for link prediction that
considers both the nodes' embedding and their community information to predict
the link between two given nodes. Extensive experimental results on several
real-world networks demonstrate the effectiveness of the proposed framework for
both inter and intra-community link prediction.
