Graph representation learning (GRL) is a powerful technique for learning
low-dimensional vector representation of high-dimensional and often 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, resulting in only a single
representation per node. Recently studies have argued on the adequacy of a
single representation and proposed context-sensitive approaches, which are
capable of extracting multiple node representations for different contexts.
This 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 complex and expensive 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 we show that in-order to extract high-quality context-sensitive
node representations it is not needed to rely on supplementary node features,
nor to employ computationally heavy and complex models. We propose GOAT, a
context-sensitive algorithm inspired by gossip communication and a mutual
attention mechanism simply over the structure of the graph. We show the
efficacy of GOAT using 6 real-world datasets on link prediction and node
clustering tasks and compare it against 12 popular and state-of-the-art (SOTA)
baselines. GOAT consistently outperforms them and achieves up to 12% and 19%
gain over the best performing methods on link prediction and clustering tasks,
respectively.

本文提出了一种基于 gossip 通信和 mutual attention 机制的 GOAT 算法，无需任何附加文本特征，仅仅通过图的结构就能提取高质量的上下文敏感节点表示。我们使用 6 个真实数据集对 GOAT 进行了比较，并与 12 个流行和最先进的基线进行了比较，发现 GOAT 在链接预测和节点聚类方面的表现均优于其他方法，相对最佳性能方法的提高率达到 12% 和 19%。