Graphs and more Complex structures for Learning and Reasoning

Workshop at AAAI 2021

The study of complex graphs is a highly interdisciplinary field that aims to study complex systems by using mathematical models, physical laws, inference and learning algorithms, etc. Complex systems are often characterized by several components that interact in multiple ways among each other. Such systems are better modeled by complex graph structures such as edge and vertex labelled graphs (e.g., knowledge graphs), attributed graphs, multilayer graphs, hypergraphs, etc. In this GCLR (Graphs and more Complex structures for Learning and Reasoning) workshop, we will focus on various complex structures along with inference and learning algorithms for these structures. The current research in this area is focused on extending existing ML algorithms as well as network science measures to these complex structures. This workshop aims to bring researchers from these diverse but related fields together and embark interesting discussions on new challenging applications that require complex system modeling and discovering ingenious reasoning methods. We have invited several distinguished speakers with their research interest spanning from the theoretical to experimental aspects of complex networks.

All the invited talks by our speakers and panel discussion are uploaded on Youtube and can be accessed from this link. The flash ppt videos of accepted papers can also be found at this link.

Call for submissions

We invite submission from participants who can contribute to the theory and applications of modeling complex graph structures such as hypergraphs, multilayer networks, knowledge graphs, etc. The topics of interest include, but not limited to, the following:

  1. Constraint satisfaction and programming (CP), (inductive) logic programming (LP and ILP)

  2. Learning with Multi-relational graphs (alignment, knowledge graph construction, completion, reasoning with knowledge graphs, etc.)

  3. Learning with algebraic or combinatorial structure

  4. Link analysis/prediction, node classification, clustering for complex graph structures

  5. Network representation learning

  6. Theoretical analysis of graph algorithms or models

  7. Optimization methods for graphs/manifolds

  8. Probabilistic and graphical models for structured data

  9. Social network analysis and measures

  10. Unsupervised graph/manifold embedding methods

Papers will be presented in poster format and some will be selected for oral presentation. Through invited talks and presentations by the participants, this workshop will bring together current advances in Network Science as well as Machine Learning, and set the stage for continuing interdisciplinary research discussions.