  Relational data mining is becoming ubiquitous in many fields of study. It
offers insights into behaviour of complex, real-world systems which cannot be
modeled directly using propositional learning. We propose Symbolic Graph
Embedding (SGE), an algorithm aimed to learn symbolic node representations.
Built on the ideas from the field of inductive logic programming, SGE first
samples a given node's neighborhood and interprets it as a transaction
database, which is used for frequent pattern mining to identify logical
conjuncts of items that co-occur frequently in a given context. Such patterns
are in this work used as features to represent individual nodes, yielding
interpretable, symbolic node embeddings. The proposed SGE approach on a venue
classification task outperforms shallow node embedding methods such as
DeepWalk, and performs similarly to metapath2vec, a black-box representation
learner that can exploit node and edge types in a given graph. The proposed SGE
approach performs especially well when small amounts of data are used for
learning, scales to graphs with millions of nodes and edges, and can be run on
an of-the-shelf laptop.
