dlg19: Deep Learning for Graph SIAM International Conference on Data Mining (SDM19) Calgary, Canada, May 2-4, 2019 |
Conference website | https://sites.google.com/view/graph-representation-workshop/ |
Submission link | https://easychair.org/conferences/?conf=dlg19 |
Submission deadline | March 29, 2019 |
Graphs (a.k.a., networks) are the universal data structures for representing the relationships between interconnected objects. They are ubiquitous in a variety of disciplines and domains ranging from computer science, social science, economics, medicine, to bioinformatics. Representative examples of real-world graphs include social networks, knowledge graph, protein-protein interaction graphs, and molecular structures. Graph analysis techniques can be used for a variety of applications such as recommending friends to users in a social network, predicting the roles of proteins in a protein-protein interaction network, and predicting the properties of molecule structures for discovering new drugs.
One of the most fundamental challenges of analyzing graphs is effectively representing graphs, which largely determines the performance of many follow-up tasks. This workshop aims to discuss the latest progress on graph representation learning and their applications in different fields. We aim to bring researchers from different communities such as machine learning, network science, natural language understanding, recommender systems, drug discovery. We specially invite submissions related to toolkits and frameworks which make it easy to apply deep learning on graphs. The topics of interest include but are not limited to:
- Unsupervised node representation learning
- Learning representations of entire graphs
- Graph neural networks
- Graph generation
- Adversarial attacks to graph representation methods
- Heterogeneous graph embedding
- Knowledge graph embedding
- Graph alignment
- Dynamic graph representation
- Graph matching
- Graph representation learning for relational reasoning
- Graph anomaly detection
- Applications in recommender systems
- Applications in natural language understanding
- Applications in drug discovery
- Toolkits and frameworks which make it easy to apply deep learning on graphs
- Other applications
Call for Papers
Papers should be submitted as PDF, using the SIAM conference proceedings style, available at https://www.siam.org/Portals/0/Publications/Proceedings/soda2e_061418.zip?ver=2018-06-15-102100-887. Submissions should be at most 8 pages (excluding references and appendix) and submitted via Easychair at https://easychair.org/cfp/deepgraph19.
Important Dates
Submission deadline: March 7, 2019
Acceptance Notification: March 30, 2019
Camera ready version due date: April 20, 2019
Conference dates: May 2-4, 2019