Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

The adaptive processing of graph data is a long-standing research topic that has been lately
consolidated as a theme of major interest in the deep learning community. The snap …

We investigate the representation power of graph neural networks in the semi-supervised
node classification task under heterophily or low homophily, ie, in networks where …

In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

Algorithmic bias and fairness in the context of graph mining have largely remained nascent.
The sparse literature on fair graph mining has almost exclusively focused on group-based …

Existing approaches to solving combinatorial optimization problems on graphs suffer from
the need to engineer each problem algorithmically, with practical problems recurring in …

Object detection is a fundamental but challenging issue in the field of generic image
analysis; it plays an important role in a wide range of applications and has been receiving …

Systems engineering is an ubiquitous discipline of Engineering overlapping industrial,
chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It …

Graph Convolutional Networks (GCNs) have been widely used due to their outstanding
performance in processing graph-structured data. However, the undirected graphs limit their …

Deep learning, particularly convolutional neural networks (CNNs), has yielded rapid,
significant improvements in computer vision and related domains. But conventional deep …