Graphs or networks are ubiquitous structures that appear in a multitude of complex systems like social networks, biological networks, knowledge graphs, the world wide web, transportation networks, and many more.
This special session at ICMLA 2021 aims to bring researchers across disciplines to share their innovative ideas on learning with graphs and leverage existing methodologies across several application domains. This special session will also serve as a common ground to build collaborations, share datasets, and inspire machine learning on graphs research in areas where there are limitations in the existing approaches. Authors of the best papers from this special session will be invited to extend their work and publish it in selected journals (SNAM, Applied Network Science, and Journal of Intelligent and Information Systems).
We welcome novel research papers on the following algorithms and applications, including but not limited to:
Applications: Computational social science, Social network analysis, Gender equality, Affective polarization, Echo chambers, Civil unrest, Fake news and misinformation spread, Hate speech and polarization, Population migration, Computer Vision and Natural Language Processing, Question Answering using Knowledge Graphs and Deep Learning, Scene graph generation, Activity understanding from multimodal data, Image and Video captioning, Knowledge graphs for multimodal understanding, Neural-symbolic integration, Explainable methods for visual understanding, senesmaking knowledge graph construction, Applying knowledge graph embeddings to real-world scenarios, Health, Health informatics and analytics, Health misinformation, Disease epidemics, Genomics, Population health, Synthetic population
Paper Publication: Accepted papers will be published in the IEEE ICMLA 2021 conference proceedings (to be published by IEEE) and indexed via Google Scholar, DBLP, etc.
Notification of Acceptance: September 26, 2021
Camera-ready papers & Pre-Registration: October 1, 2021