… graph embedding, namely Customized Graph Embedding (CGE). In contrast to previous
semi-supervised graph embedding … of different graph paths for learning node embedding vectors…

… Finally, we summarize the applications that graph embedding … , problem settings, techniques,
and application scenarios. … 1e) as a 2D vector (ie, a point in a 2D space). In the next two …

… User profiling plays an increasingly important role in providing personalized services in e-…
Traditionally, learning a vector embedding usually cannot well capture such uncertainty. …

… graph embedding methods. We highlight the distinct advantages of graph embedding methods
in four diverse applications… Unlike the deterministic vector point--based graph embedding …

… each column vector represents the embedding of an element … , one would aim to tailor search
results to each specific user … 2011): This baseline uses a personalized navigation method …

… instantiate VERSE with Personalized PageRank, SimRank, … and thereby tailor the learning
process to the graph at hand in … computes full similarity distribution vectors per node instead …

… : we show that the vector embeddings not only can be used in … user has specified the type
of features in the form of custom … graph kernels have been proposed that are tailored towards …

… and Embedding Personalized Network (PEPNet) tailored to the … , mapping each feature to
an embedding vector quickly fills up the … We apply careful grid-search to find the best hyper-…

… and relations as dense vectors, termed embeddings. KGE models … add information by adding
their custom function to one of multiple … Large-scale projects are typically tailored towards …

… only the Top-K entity embeddings with the greater changes to … personalized embedding
aggregation for each client. It then identifies and transmits the Top-K aggregated embeddings to …