The Web&Graph Workshop aims to bring together researchers and practitioners from web search, data mining, artificial intelligence, and social sciences to discuss algorithmic, theoretical, and methodological advances for dynamic, reliable, and human-aligned graph analytics.

The workshop will focus on the algorithmic foundations and the applied aspects of graph reasoning for evolving networks, misinformation detection, provenance tracking, and human–AI collaboration.

Submission Guidelines

We invite authors to submit unpublished, original papers written in English. Submitted papers should not have been previously published or accepted for publication in substantially similar form in any peer-reviewed venue, such as journals, conferences, or workshops. Manuscripts must be submitted in PDF according to the new ACM format published in ACM guidelines (use documentclass [sigconf,anonymous,review]{acmart}).

We will consider two different submission types:

• Full papers (up to 9 pages + unlimited references) should clearly describe the state of the art and state the proposal's contribution in the application domain, even if presenting preliminary results. In particular, research papers should describe the methodology in detail, experiments should be repeatable, and a comparison with the existing approaches in the literature should be made.
• Short and position papers (up to 4 pages all included) should introduce new points of view on the workshop topics or summarize a group's experience in the field. Practice and experience reports must detail real-world scenarios in which Machine Unlearning is needed.

Submissions should not exceed the indicated pages, including any diagrams and references. All submissions will undergo a double-blind review process and be reviewed by at least three reviewers based on relevance to the workshop, novelty/originality, significance, technical quality and correctness, quality and clarity of presentation, quality of references, and reproducibility. Submitted papers will be rejected without review if they are not correctly anonymized, do not comply with the template, or do not follow the above guidelines.

Generative AI Usage Policy. Generative AI models, including Chat-GPT, BARD, LLaMA, or similar LLMs, do not satisfy the criteria for authorship of papers accepted in the workshop. If authors use an LLM in any part of the paper-writing process, they assume full responsibility for all content, including checking for plagiarism and correctness of all text.

Proceedings. This year, the WSDM organization will publish a Companion Proceedings Volume that includes the workshop papers.

List of Topics

We particularly welcome works that provide innovative and interpretable solutions or address real-world challenges by combining graphs, AI, and web algorithms. The expected, but not exhaustive, contributions are:

• Theoretical Topics
  • Design and analysis of graph algorithms for web and social networks
  • Optimization and approximation on large and dynamic graphs
  • Diffusion modeling, and algorithmic fairness
  • Graph compression, sparsification, and structure-aware pruning
  • Robust, explainable, and provable graph representations
• Technical and Applied Topics
  • Graph neural networks and graph transformers for web data
  • Graph-based trust, credibility, and misinformation detection
  • Influence propagation and provenance tracking for social domains
  • Integration of LLMs with structured graph representations
  • Human-in-the-loop knowledge-graph curation and correction
  • Deep learning on dynamic and streaming graph data
  • Social network analytics and algorithmic transparency
  • Optimization frameworks for scalable and interpretable graph learning

Committees

Organizing committee

• Matteo Spezialetti, University of L'Aquila
• Andrea D'Angelo, University of L'Aquila
• Francesca Ciccarelli, University of L'Aquila
• Giuseppe Costanzo, University of L'Aquila
• Daniele Fossemò, University of L'Aquila
• Filippo Mignosi, University of L'Aquila

Venue

Boise, Idaho, USA, as part of the 19th ACM International Conference on Web Search and Data Mining (WSDM 2026)

Contact

All questions about submissions should be emailed to matteo.spezialetti@univaq.it, andrea.dangelo6@graduate.univaq.it, francesca.ciccarelli@graduate.univaq.it, giuseppe.costanzo@graduate.univaq.it, daniele.fossemò@graduate.univaq.it