  User identity linkage (UIL), matching accounts of a person on different
social networks, is a fundamental task in cross-network data mining. Recent
works have achieved promising results by exploiting graph neural networks
(GNNs) to capture network structure. However, they rarely analyze the realistic
node-level bottlenecks that hinder UIL's performance. First, node degrees in a
graph vary widely and are long-tailed. A significant fraction of tail nodes
with small degrees are underrepresented due to limited structural information,
degrading linkage performance seriously. The second bottleneck usually
overlooked is super head nodes. It is commonly accepted that head nodes perform
well. However, we find that some of them with super high degrees also have
difficulty aligning counterparts, due to noise introduced by the randomness of
following friends in real-world social graphs. In pursuit of learning ideal
representations for these two groups of nodes, this paper proposes a
degree-aware model named DegUIL to narrow the degree gap. To this end, our
model complements missing neighborhoods for tail nodes and discards redundant
structural information for super head nodes in embeddings respectively.
Specifically, the neighboring bias is predicted and corrected locally by two
modules, which are trained using the knowledge from structurally adequate head
nodes. As a result, ideal neighborhoods are obtained for meaningful aggregation
in GNNs. Extensive experiments demonstrate the superiority of our model. Our
data and code can be found at https://github.com/Longmeix/DegUIL.
