  Network representation learning (NRL) is a powerful technique for learning
low-dimensional vector representation of high-dimensional and sparse graphs.
Most studies explore the structure and metadata associated with the graph using
random walks and employ an unsupervised or semi-supervised learning schemes.
Learning in these methods is context-free, because only a single representation
per node is learned. Recently studies have argued on the sufficiency of a
single representation and proposed a context-sensitive approach that proved to
be highly effective in applications such as link prediction and ranking.
  However, most of these methods rely on additional textual features that
require RNNs or CNNs to capture high-level features or rely on a community
detection algorithm to identify multiple contexts of a node.
  In this study, without requiring additional features nor a community
detection algorithm, we propose a novel context-sensitive algorithm called GAP
that learns to attend on different parts of a node's neighborhood using
attentive pooling networks. We show the efficacy of GAP using three real-world
datasets on link prediction and node clustering tasks and compare it against 10
popular and state-of-the-art (SOTA) baselines. GAP consistently outperforms
them and achieves up to ~9% and ~20% gain over the best performing methods on
link prediction and clustering tasks, respectively.
