Graph Neural Networks (GNNs) learn low dimensional representations of nodes
by aggregating information from their neighborhood in graphs. However,
traditional GNNs suffer from two fundamental shortcomings due to their local
($l$-hop neighborhood) aggregation scheme. First, not all nodes in the
neighborhood carry relevant information for the target node. Since GNNs do not
exclude noisy nodes in their neighborhood, irrelevant information gets
aggregated, which reduces the quality of the representation. Second,
traditional GNNs also fail to capture long-range non-local dependencies between
nodes. To address these limitations, we exploit mutual information (MI) to
define two types of neighborhood, 1) \textit{Local Neighborhood} where nodes
are densely connected within a community and each node would share higher MI
with its neighbors, and 2) \textit{Non-Local Neighborhood} where MI-based node
clustering is introduced to assemble informative but graphically distant nodes
in the same cluster. To generate node presentations, we combine the embeddings
generated by bi-level aggregation - local aggregation to aggregate features
from local neighborhoods to avoid noisy information and non-local aggregation
to aggregate features from non-local neighborhoods. Furthermore, we leverage
self-supervision learning to estimate MI with few labeled data. Finally, we
show that our model significantly outperforms the state-of-the-art methods in a
wide range of assortative and disassortative graphs.

本文通过使用互信息（mutual information）引入局部区域和全局区域的概念，并利用双层聚合对复杂的节点特征进行建模，实现了对于无序图中节点表示的高效学习和训练。同时，借助于自监督学习，将有限的标签数据结合互信息理论进行更加细致的标签预测，证明了本文提出的模型在各种图结构中都有着优越的表现。