Network science tries to understand the complex relationships among entities or actors of a
system through graph formalism. For instance, biological networks represent …

Deep learning-based graph generation approaches have remarkable capacities for graph
data modeling, allowing them to solve a wide range of real-world problems. Making these …

Gene Regulatory Networks (GRNs) depict the causal regulatory interactions between
transcription factors (TFs) and their target genes [2], where TFs are proteins that regulate …

Graph representation learning recently has proven their excellent competency in
understanding large graphs and their inner engineering for various downstream tasks. Link …

Biological networks are essential for understanding the complex cellular mechanisms of
living organisms. Simulating biological networks allows researchers to model and …

Significant progression has been made in active learning algorithms for graph networks in
various tasks. However real-world applications frequently involve incomplete graphs with …

Traditional classification approaches, grounded in closed-set frameworks, face limitations in
their ability to handle novel, unseen instances that frequently arise in dynamic real-world …

Network science tries to shed light into the complex relationships among entities of a system.
For instance, biological networks represent relations between macro molecules such as …

Several effective online and offline inference tools offer an interactive platform for inference,
visualization, and analysis of gene regulatory networks. However, a tool currently needs to …