Special Track: Graph Machine Learning on Web and Social Media: Trends, Challenges, and Applications

About:

For updated news and information about this special track, please visit the special track webpage

Graphs, which encode pairwise relations between entities, are commonly utilized data structures in the realm of the Web and Social Media. As the most representative technique, graph machine learning methods have been extensively developed to effectively analyze graph patterns in a computational manner, which has achieved great success in numerous real-world applications. Meanwhile, the boom of large language models (LLMs) has revolutionized Natural Language Processing (NLP) and Artificial Intelligence (AI). Consequently, increasing attention has been paid to exploring the potential of leveraging LLMs for advancing graph machine learning techniques. This emerging intersection presents both opportunities and challenges that warrant attention and further exploration.  The goal of this special topic track is to bring together researchers from academia and practitioners from the industry, providing them with an opportunity to present their recent progress and share valuable insights related to advancements on the web and social media.

Organizers:

  • Qing Li (Lead Organizer), Hong Kong Polytechnic University.
  • Irwin King, The Chinese University of Hong Kong.
  • Wenqi Fan, Hong Kong Polytechnic University.

Topics of Interest:

Areas of interest include (but not limited to):

  • Information and web mining
  • Social media analysis
  • Social network analysis
  • Graph machine learning
  • Anomaly and outlier detection in social media
  • Dynamic social media monitoring
  • Spatio-temporal aspects in social networks and social media
  • Semantic and Knowledge
  • LLMs-enhanced graphs
  • Graph-enhanced LLMs
  • Graph foundation models
  • Large-scale graph algorithms
  • Trustworthy web mining
  • Multimodal web mining
  • Community discovery and analysis
  • Recommender Systems
  • Computer Vision on web and social media
  • Natural Language Processing on web and social media
  • Information systems
  • Search and filtering technology
  • Web-related economic activities, online markets, and human computation
  • Web-based mobile and ubiquitous computing

Submission Guidelines

Papers should be submitted in PDF format. The results described must be unpublished and must not be under review elsewhere. Submissions must conform to Springer’s LNCS format and should not exceed 15 pages, including all text, figures, references, and appendices. Submissions not conforming to the LNCS format, exceeding 15 pages, or being obviously out of the scope of the conference, will be rejected without review. Information about the Springer LNCS format can be found at Springer. Three to five keywords characterizing the paper should be indicated at the end of the abstract.

All submissions must go through EasyChair system via Easychair

Important Dates

  • Submission Deadline: 30 June, 2024
  • Acceptance/Rejection Notification: 30 August, 2024
  • Camera-Ready Files Submission Deadline: 07 September, 2024

Publication

Please note that for every accepted paper, it is required that at least one person registers for the conference and presents the paper. All accepted papers will be included in the proceedings published as Springer’s LNCS series.