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Overview and Objective

Graph, as a unified data structure representing relationships among entities, exists universally across different domains, including computer science, social science, physics, chemistry, biology, infrastructure, finance, psychology, etc. Many real-world applications could be formulated as graph-based tasks. For example, social anomaly detection, drug property prediction, and product recommendation can be regarded as node/graph classification and link prediction tasks, academic paper question-answering requires retrieving documents from citation networks, agent collaboration and communication can be regarded as a graph structure optimization task, designing molecules and infrastructure networks can be deemed as a graph generation task, optimizing sensor localization can be formulated as an influence-maximization-based decision-making task.

Through years of research, graph machine learning has gradually formed a well-established ecosystem capable of automating these tasks and driving real-world applications. With the increasing maturity of GML, the recent emergence of large foundational models, such as large language models and multi-modal systems, has propelled machine learning into a new GenAI era and revolutionized the research landscape of many fields. However, the well-established GML is still predominately anchored in pre-GenAI paradigms, creating a significant gap in understanding how it should evolve to fully leverage the opportunities, address the challenges and comprehensively embrace this transformative GenAI era. This motivates us to host this workshop to gather academic and industrial researchers/practitioners to present and share the development of GML in the GenAI era and envision how GML communities could embrace this new GenAI era.

Submission Details

We welcome submissions of papers ranging from 4 to 8 pages as main content, excluding references and appendices. All submissions must be in PDF format and formatted according to the new ACM format published in ACM guidelines (e.g., using the ACM LaTeX template on Overleaf Here) and selecting the “sigconf” sample. Following the KDD’25 conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. Submitted works will be assessed based on their novelty, technical quality, potential impact, and clarity of writing (and should be in English). For papers that primarily rely on empirical evaluations, the experimental settings and results should be clearly presented and repeatable. We encourage authors to make data and code available publicly when possible.

  • All submissions must be uploaded electronically through Openreview
  • Accepted papers will not appear in the KDD’25 proceedings and are thus non-archival.
  • Authors are allowed to submit works concurrently under review elsewhere or published.
  • Accepted papers will be posted on this workshop website.
  • Accepted submissions will be accompanied by a poster presentation with selected ones for oral presentations.
  • For questions regarding submissions, please contact us at: mlgraph@googlegroups.com

    • Important Dates

    All submission deadlines are end-of-day in the Anywhere on Earth (AoE) time zone:

  • Workshop paper submission: May 8th, 2025
  • Workshop paper website: Openreview
  • Workshop paper notification: June 8th,2025
  • Workshop paper camera-ready: June 15th, 2025
  • Workshop date: August 6th, 2025

    • If you have any questions, please contact: mlgraph@googlegroups.com

    Topics

  • Graph-enhanced Agentic Learning
  • Multi-Agentic Collaboration
  • Graph-enhanced Agentic Reasoning and Planning
  • Graph-based Memory Systems
  • Agentic Social Network Analysis
  • Neural-Symbolic Harmonization
  • System 1 and System 2 Thinking
  • Symbolic-enhanced Neural Learning
  • Neural-enhanced Symbolic Learning
  • Structural and Neural Knowledge Representation
  • Graph Retrieval-augmented Generation (GraphRAG)
  • Graph-enhanced Retrieval
  • Graph-enhanced Generation
  • Graph Knowledge Base Construction and Management
  • Query Topology Analysis
  • Agentic Graph Retrieval
  • Multi-Modal Graph Learning
  • Text-rich Graph Learning
  • Image-rich Graph Learning
  • Graph and Other Modality Learning
  • Graph Generative Models
  • Continuous Graph Diffusion
  • Discrete Graph Diffusion
  • LLM for Graph Generation
  • Complex Graph Generation: Multi-attributed Graph, Dynamic Graph Generation, etc.
  • Foundations and Theory
  • Foundational Graph Model Design
  • Graph Transferability Analysis
  • LLMs for Graph Learning
  • Expressiveness of Graph Machine Learning Models
  • Graph Combinatorial Optimization
  • Complex Topology Mining and Modeling
  • Data-centric Graph Machine Learning
  • Distribution Shift
  • Imbalance
  • Limited Supervision
  • Trustworthy Graph Machine Learning
  • Reliability
  • Robustness
  • Resilience
  • Privacy
  • Explainability
  • Safety
  • Applications
  • Social Network Analysis
  • Recommender System
  • Neural Biology
  • Biochemistry
  • Cybersecurity
  • Infrastructure Networks
  • Spatial-Temporal Learning
  • Document Mining
  • Psychology
  • Computer System
  • Hardware Design