Call for Papers

Topics

The topics of interest of the GraDSci special session include, but are not limited to:

Graph data science algorithms and methods

Representation learning on graphs
Deep learning and graph neural networks
Scalable graph learning models and methods
Bias and fairness in graph machine learning
Probabilistic graphical models
Statistical models of graphs
Graph kernels and graph similarity
Semi-supervised, self-supervised, and unsupervised graph learning
Graph sampling and inference
Graph clustering and community detection
Graph summarization
Graph anomaly detection
Learning on spatial, temporal, and dynamic networks
Learning on heterogeneous and multi-relational graphs
Graph data processing and management
Graph visualization

Application domains

Social media and social network analysis
Influence propagation and misinformation detection
Knowledge graphs and semantic networks
Recommender systems
Natural language processing
Multimedia signal processing and computer vision
Computational health, biomedicine, and epidemiology
Computational biology and bioinformatics
Neuroscience and brain network analysis
Drug design and pharmaceutical sciences
Materials science
Urban network analysis
Environment
Communication networks and cybersecurity