DLG-KDD 2022 : The Eighth International Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD‘22)

This workshop aims to bring together both academic researchers and industrial practitioners from different backgrounds to discuss a wide range of topics of emerging importance for GNN, including 1) the deep understanding of basic concepts and theory of GNNs; 2) major recent advances of GNNs research with the state-of-the-art algorithms; and 3) explore novel research opportunities of GNNs and how to use or even design GNNs algorithms for real-world applications. The foundation and advanced problems include but not limited to:

Representation learning on graphs
Graph neural networks on node classification, graph classification, link prediction
The expressive power of Graph neural networks
Scalable methods for large graphs
Interpretability in Graph Neural Networks
Graph Neural Networks: adversarial robustness
Graph neural networks for graph matching
Graph structure learning
Dynamic/incremental graph-embedding
Learning representation on heterogeneous networks, knowledge graphs
Deep generative models for graph generation/semantic-preserving transformation
Graph Neural Networks: AutoML
Graph2seq, graph2tree, and graph2graph models
Deep reinforcement learning on graphs
Self-supervised learning on graphs
Spatial and temporal graph prediction and generation.
And with particular focuses but not limited to these application domains:

Graph Neural Networks in Modern Recommender Systems
Graph Neural Networks for Automated planning in Urban Intelligences
Learning and reasoning (machine reasoning, inductive logic programming, theory proving)
Natural language processing (information extraction, semantic parsing (AMR, SQL), text generation, machine comprehension)
Bioinformatics (drug discovery, protein generation, protein structure prediction)
Graph Neural Networks Program synthesis and analysis and software mining
Graph Neural Networks for Automated planning
Reinforcement learning (multi-agent learning, compositional imitation learning)
Financial security (Anti-Money Laundering)
Computer vision (object relation reasoning, graph-based representations for segmentation/tracking)
Deep learning in neuroscience (brain network modeling and prediction)
Cybersecurity (authentication graph, Internet of Things, malware propagation)
Geographical network modeling and prediction (Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks)
Circuit network design, prediction, and defense