Node role explainability in complex networks is very difficult yet is crucial in different
application domains such as social science, neurosciences, or computer science. Many …

Graph node embedding aims at learning a vector representation for all nodes given a graph.
It is a central problem in many machine learning tasks (eg, node classification …

Network embedding maps the nodes of a given network into a low-dimensional space such
that the semantic similarities among the nodes can be effectively inferred. Most existing …

Graph representation learning or graph embedding is a classical topic in data mining.
Current embedding methods are mostly non-parametric, where all the embedding points are …

In graph data, each node often serves multiple functionalities. However, most graph
embedding models assume that each node can only possess one representation. We …

July, 2023 Ryotaro SHIMIZU Data Interpretation Based on Embedded Data Representation
Models Analytical Models for Effective Onli Page 1 July, 2023 Ryotaro SHIMIZU Data …

Network embedding maps the nodes of a given network into a low-dimensional space such
that the semantic similarities among the nodes can be effectively inferred. Most existing …

The field of information access is of vital importance in our modern societies, since most of
the information is now accessible in a digital form and is increasing in volume at a fast pace …