Call for Papers |
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Graphs or networks are ubiquitous structures that appear in a multitude of complex systems like social networks, biological networks, knowledge graphs, world wide web, transportation networks, and many more. Real-world networks are massive and unstructured, apart from dynamic and multi-modal. Many existing domains can benefit from data analysis modelled as a networks problem that provide many computational and algorithmic challenges. Essentially, networks provide enormous potential to address long-standing scientific questions and particularly inform the design of several machine learning applications. Graph-based learning and reasoning approaches offer a way to integrate symbolic reasoning (which offer more interpretability) with the representation learning capabilities of deep neural networks to introduce causality, interpretability, and transferability.
The third year of Machine Learning for Graphs special session aims to bring researchers across disciplines to share their innovative ideas on machine learning for graphs and leverage existing methodologies across several application domains. This special session will also serve as a common ground to showcase recent advancements in ML for graphs, build collaborations across disciplines, share benchmark datasets for graph-based ML algorithm evaluation, and inspire machine learning for graphs research in domains where there are limitations in the existing approaches. Authors of the best papers from this special session will have an opportunity to extend their work and publish in selected journals. Scope and Topics: We welcome novel research papers on the following algorithms and applications, including but not limited to: Algorithms Graph representation learning Hyperbolic graph embedding ML on Signed networks ML on multi-layer, multi-modal, and heterogeneous graphs ML on knowledge graphs ML on evolving graphs and graph streams ML on cascades and cascade growth ML on low-resource settings ML on Test-Time Generalization Network growth models ● Graph summarization Graph partitioning Graph matching Graph generative models Network fusion Graph reinforcement learning Scalable ML algorithms for graphs Applicatioins in computational social science Social network analysis Cyberbullying Affective polarization Echo chambers Civil unrest Fake news and misinformation spread Hate speech Population migration Local and global politics Applications in Computer Vision, Natural Language Processing and Speech 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 Common sense knowledge graph construction Applying knowledge graph embeddings to real world scenarios Speaker Diarization, Speech Emotion Recognition and Speech Enhancement Applications in Health and Medicine Health informatics and analytics Health misinformation Disease epidemics Genomics Population health Synthetic population Drug discovery |
Credits and Sources |
| [1] ML4GRAPHS 2024 : Special Session on Machine Learning for Graphs |
