In applications such as social, energy, transportation, sensor, and neuronal networks, high-
dimensional data naturally reside on the vertices of weighted graphs. The emerging field of …

Networks have become increasingly important to model complex systems composed of
interacting elements. Network data mining has a large number of applications in many …

Background Macrophages are innate immune cells with diverse functional and molecular
phenotypes. This diversity is largely unexplored at the level of single-cell proteomes …

Unsupervised localization and segmentation are long-standing computer vision challenges
that involve decomposing an image into semantically-meaningful segments without any …

This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …

Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML
systems that are already used in production. The main appeal of post-processing is that it …

Network meta‐analysis is an active field of research in clinical biostatistics. It aims to
combine information from all randomized comparisons among a set of treatments for a given …

With the tremendous success of Graph Convolutional Networks (GCNs), they have been
widely applied to recommender systems and have shown promising performance. However …

Network embedding has been proven to be helpful for many real-world problems. In this
paper, we present a scalable multiplex network embedding model to represent information …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …