Due to their tremendous potential in predictive tasks, Machine Learning techniques such as
Artificial Neural Networks have received great attention from both research and practice …

Friend link prediction is an important research problem in recommender systems. Existing
network embedding and knowledge embedding methods mainly consider the structural …

Highly-heterogeneous and fast-arriving large amounts of data, otherwise said Big Data,
induced the development of novel Data Management technologies. In this paper, the …

This paper proposes novel algorithms for efficiently counting complex network motifs in
dynamic networks that are changing over time. Network motifs are small characteristic …

This paper outlines an approach towards socio-technical design of explicative systems
enabling transparent, interpretable and explainable analytics. We sketch the TIE approach …

Graphs (also called networks) are powerful data abstractions, but they are challenging to
work with, as many machine learning methods may not be applied to them directly. Network …

Abstract Knowledge Graphs represent real-world facts and are used in several applications;
however, they are often incomplete and have many missing facts. Link prediction is the task …

Abstract Graph Machine Learning (GML) has numerous applications, such as node/graph
classification and link prediction, in real-world domains. Providing human-understandable …

Abstract Even though Social Network Analysis is quite helpful in studying the structural
properties of interconnected systems, real-world networks reveal much more hidden …

Knowledge graphs are large, graph-structured databases used in many use-case scenarios
such as Intelligent Environments. Many Artificial Intelligent latent feature models are used to …