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
  • Joint learning of graph neural networks and graph structure
  • 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
  • Spatial and temporal graph prediction and generation

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

  • Learning and reasoning (machine reasoning, inductive logic programming, theory proving)
  • Natural language processing (information extraction, semantic parsing, text generation)
  • Bioinformatics (drug discovery, protein generation, protein structure prediction)
  • Program synthesis and analysis
  • Reinforcement learning (multi-agent learning, compositional imitation learning)
  • Financial security (anti-money laundering)
  • Cybersecurity (authentication graph, Internet of Things, malware propagation)
  • Geographical network modeling and prediction (Transportation and mobility networks, social networks)

Paper submission (GMT)

Submissions are limited to a total of 5 pages for initial submission (up to 6 pages for final camera-ready submission), excluding references or supplementary materials, and authors should only rely on the supplementary material to include minor details that do not fit in the 5 pages. All submissions must be in PDF format and formatted according to the new Standard AAAI Conference Proceedings Template. Following this AAAI 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 AAAI proceedings. Special issues in flagship academic journals are under consideration to host the extended versions of best/selected papers in the workshop.

Workshop website

Submission link