Virtual Special Session 16:
Machine Learning for Graphs
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
Chair: Arunkumar Bagavathi, Sathyanarayanan N. Aakur, Siddharth Krishnan, Manikandan Ravikiran
Bio: Dr. Arunkumar Bagavathi is an Assistant Professor in the Department of Computer Science at the Oklahoma State University. He received his doctoral degree from UNC Charlotte in 2019. His research interests include: data mining, graph mining, computational social science, and applied machine learning. His research works have been published in venues like IEEE Big Data, Complex Networks, IEEE/ACM ASONAM, IEEE ICMLA, and JMIR. Dr. Sathyanarayanan N. Aakur is an Assistant Professor in the Department of Computer Science at Oklahoma State University. His research interests lie at the intersection of artificial intelligence and psychology, particularly in the use of common sense reasoning in multimodal understanding. His research has been published in venues such as IEEE CVPR, AAAI, IEEE WACV, Quarterly of Applied Mathematics, etc. He received his doctoral degree in computer science from the University of South Florida in 2019. Dr. Siddharth Krishnan is an assistant professor in the department of computer science (college of computing) at UNC-Charlotte. His research interests are in web-mining, data analytics, computational social science, and applied machine learning with a primary emphasis on analyzing, characterizing, and forecasting information (news, rumors, memes, advertisements, etc.) dynamics on online social networks & social media. Furthermore, his research aims to leverage dynamical processes (like cascade propagation) to build explanatory & predictive models of actions of large groups of people and societies. He has published in several data science venues like ACM KDD, ACM WSDM, PLoS ONe, WebSci, AAAI, TKDD, etc. Prior to joining UNCC, he received his doctorate in computer science from Virginia Tech. Manikandan Ravikiran is a Research Scientist working at Artificial Intelligence Research Group at R&D Centre, Hitachi India to better understand how to improve software products of ADAS, Healthcare and Safety using NLP and Computer Vision under data constraint settings. His research focus includes test time generalization, few shot learning and continual learning for low resource vision and language problems. He has served in Program Committee of IEEE ICDDS, ACM ICMR, ACL, NAACL, EMNLP, COLING, Springer Language Resources and Evaluation Journal (LRE), ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Journal of Experimental and Theoretical Artificial Intelligence (JETAI) and Elsevier Journal of Engineering Applications of Artificial Intelligence (EAAI). In the past he has also organized Workshop on Cross Modal Learning (WCRML 2019) at ACM ICMR, Shared task on Offensive Span Identification at Second Workshop on Speech and Language Technologies for Dravidian Languages (DravidianLangTech 2022, EACL 2021 and RANLP 2023) and First workshop on Low Resource Cross-Domain, Cross-Lingual and Cross-Modal Offensive Content Analysis at SPELLL (LC4 2022). He is a member of IEEE, ACM, and ACL.
Technical Committee (TO BE ANNOUNCED)
Paper Submission Instructions
All papers will be double-blind reviewed and must present original work.
- CMT Submission Site
- Select the track: Virtual Special Session 16: Machine Learning for Graphs
Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from:
Keydates
- Submission due date: September 5, 2023
- Notification of Acceptance: September 25, 2023
- Camera Ready Papers: October 5, 2023
- Pre-registration: October 15, 2023
- Conference: December 15-17, 2023
Registration
In order for your paper to be presented in the virtual session and published in the proceedings you must register to the conference.
Paper Presentation Instructions
The papers submitted to this track will be presented in the virtual part of the conference. There is no in-person presentation for this session.
If you decide to participate in-person to the conference you must have an adequate internet connection for your presentation and to participate in this special session.
ICMLA'23