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DLG-KDD 2021 : Deep Learning on Graphs: Method and Applications (DLG-KDD’21)

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Link: https://deep-learning-graphs.bitbucket.io/dlg-kdd21/index.html
 
When Aug 14, 2021 - Aug 18, 2021
Where Virtual
Submission Deadline May 31, 2021
Notification Due Jun 10, 2021
Final Version Due Jul 2, 2021
Categories    deep learning   deep learning on graphs   machine learning   data mining
 

Call For Papers

Topic of interest (including but not limited to)
We invite submission of papers describing innovative research and applications around the following topics. Papers that introduce new theoretical concepts or methods, help to develop a better understanding of new emerging concepts through extensive experiments, or demonstrate a novel application of these methods to a domain are encouraged.

Graph neural networks on node-level, graph-level embedding
Graph neural networks on graph matching
Dynamic/incremental graph-embedding
Learning representation on heterogeneous networks, knowledge graphs
Deep generative models for graph generation/semantic-preserving transformation
Graph2seq, graph2tree, and graph2graph models
Deep reinforcement learning on graphs
Adversarial machine learning on graphs

And with particular focuses but not limited to these application domains:

Learning and reasoning (machine reasoning, inductive logic programming, theory proving)
Computer vision (object relation, graph-based 3D representations like mesh)
Natural language processing (information extraction, semantic parsing (AMR, SQL), text generation, machine comprehension)
Bioinformatics (drug discovery, protein generation)
Program synthesis and analysis
Reinforcement learning (multi-agent learning, compositional imitation learning)
Financial security (anti-money laundering)

Paper submission (GMT)
Submissions are limited to a total of 5 pages, including all content and references, and must be in PDF format and formatted according to the new Standard ACM Conference Proceedings Template. Following this KDD conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible. The accepted papers will be posted on the workshop website and will not appear in the KDD proceedings.

Workshop website
http://deep-learning-graphs.bitbucket.io/dlg-kdd21/
Submission link
https://easychair.org/conferences/?conf=dlg21

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