Inductive link prediction -- where entities during training and inference
stages can be different -- has shown great potential for completing evolving
knowledge graphs in an entity-independent manner. Many popular methods mainly
focus on modeling graph-level features, while the edge-level interactions --
especially the semantic correlations between relations -- have been less
explored. However, we notice a desirable property of semantic correlations
between relations is that they are inherently edge-level and
entity-independent. This implies the great potential of the semantic
correlations for the entity-independent inductive link prediction task.
Inspired by this observation, we propose a novel subgraph-based method, namely
TACO, to model Topology-Aware COrrelations between relations that are highly
correlated to their topological structures within subgraphs. Specifically, we
prove that semantic correlations between any two relations can be categorized
into seven topological patterns, and then proposes Relational Correlation
Network (RCN) to learn the importance of each pattern. To further exploit the
potential of RCN, we propose Complete Common Neighbor induced subgraph that can
effectively preserve complete topological patterns within the subgraph.
Extensive experiments demonstrate that TACO effectively unifies the graph-level
information and edge-level interactions to jointly perform reasoning, leading
to a superior performance over existing state-of-the-art methods for the
inductive link prediction task.

TACO 是一种基于子图的新型方法，通过建模与拓扑结构高度相关的关系之间的语义相关性，将图级特征和边级交互相结合，以同时进行推理的方式，在归纳链接预测任务中实现了卓越的性能。

学习关系间完整的拓扑感知相关性用于归纳链路预测

Learning Complete Topology-Aware Correlations Between Relations for  Inductive Link Prediction

Biological network alignment (NA) aims to find a node mapping between
species' molecular networks that uncovers similar network regions, thus
allowing for transfer of functional knowledge between the aligned nodes.
However, current NA methods do not end up aligning functionally related nodes.
A likely reason is that they assume it is topologically similar nodes that are
functionally related. However, we show that this assumption does not hold well.
So, a paradigm shift is needed with how the NA problem is approached. We
redefine NA as a data-driven framework, TARA (daTA-dRiven network Alignment),
which attempts to learn the relationship between topological relatedness and
functional relatedness without assuming that topological relatedness
corresponds to topological similarity, like traditional NA methods do. TARA
trains a classifier to predict whether two nodes from different networks are
functionally related based on their network topological patterns. We find that
TARA is able to make accurate predictions. TARA then takes each pair of nodes
that are predicted as related to be part of an alignment. Like traditional NA
methods, TARA uses this alignment for the across-species transfer of functional
knowledge. Clearly, TARA as currently implemented uses topological but not
protein sequence information for this task. We find that TARA outperforms
existing state-of-the-art NA methods that also use topological information,
WAVE and SANA, and even outperforms or complements a state-of-the-art NA method
that uses both topological and sequence information, PrimAlign. Hence, adding
sequence information to TARA, which is our future work, is likely to further
improve its performance.

TARA, a data-driven framework for biological network alignment, utilizes a classifier to predict functional relatedness based on network topological patterns and outperforms existing state-of-the-art methods.