PAKDD 2026
The 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) will take place in Hong Kong SAR, China, on June 9-12, 2026. PAKDD 2026 is soliciting contributed technical papers for presentation at the Conference and publication in the Conference Proceedings by Springer. We solicit novel, high-quality, and original research papers that provide innovative insights into all facets of knowledge discovery and data science, including but not limited to theoretical foundations of mining, inference, and learning, big data technologies, as well as security, privacy, and integrity. We also encourage visionary papers on emerging topics and application-based papers offering innovative technical advancements to interdisciplinary research and applications of data science.
重要信息
CCF推荐：C（数据库／数据挖掘／内容检索）
录用率：24.1%（168/696，2025年）
时间地点：2026年6月9日-香港·中国
截稿时间：2025年11月15日
Call for Papers - Main Track
Theoretical Foundations
Generative AI, quantum ML, neuro-symbolic methods and reasoning, causal reasoning, non-IID learning, OOD generalization, representation learning, mathematical and statistical foundations, information theoretic approaches, optimization method
Theoretical foundations for fairness, trustworthy AI, safety, model explainability, and XAI
Learning Methods and Algorithms
Clustering, classification, pattern mining and association rules discovery
Supervised learning, semi-supervised learning, few-shot and zero-shot learning, active learning
Reinforcement learning and bandits
Transfer learning, federated learning
Anomaly detection, outlier detection
Learning in recommendation engines
Learning in streams and in time series
Learning on structured data, images, texts and multi-modal data
Online learning, model adaption
Graph mining and Graph NNs
Trustworthy Machine Learning
Fairness
Data Processing for Learning
Dimensionality reduction, feature extraction, subspace construction
Data cleaning and preparation, data integration and summarization
Learning in real-time
Big data technologies
Information retrieval
Data/entity/event/relationship extraction
User interfaces and visual analytics
Security, Privacy, Ethics, Information Integrity and Social Issues
Modeling credibility, trustworthiness, and reliability
Privacy-preserving data mining and privacy models
Model transparency, interpretability, and fairness
Misinformation detection, monitoring, and prevention
Social issues, such as health inequities, social development, and poverty
Interdisciplinary Research on Data Science Applications
Social network/media analysis and dynamics, reputation, influence, trust, opinion mining, sentiment analysis, link prediction, and community detection
Symbiotic human-AI interaction, human-agent collaboration, socially interactive robots, and affective computing
Internet of Things, logistics management, network traffic and log analysis, and supply chain management
Business and financial data, computational advertising, customer relationship management, intrusion and fraud detection, and intelligent assistants
Urban computing, spatial data science and pervasive computing
Medical and public health applications, drug discovery, healthcare management, and epidemic monitoring and prevention
Methods for detecting and combating spamming, trolling, aggression, toxic online behaviors, bullying, hate speech, and low-quality and offensive content
Climate, ecological, and environmental science, and resilience and sustainability
Astronomy and astrophysics, genomics and bioinformatics, high energy physics, robotics, AI-assisted programming, and scientific data
Call for Papers - Survey Track
The PAKDD 2026 Survey Track solicits high-quality survey papers that present a structured synthesis of a particular topic in the area of knowledge discovery, data mining and machine learning, including but not limited to theoretical foundations of mining, inference, and learning, big data technologies, as well as security, privacy, explainability, and integrity.
Call for Papers - Special Track
The PAKDD 2026 Special Track on Large Language Models for Data Science aims to explore the transformative potential of LLMs for Data Science, bringing together researchers, practitioners, and industry experts to discuss the latest developments, challenges, and opportunities in this rapidly growing area.
Novel, high-quality, and original research papers that provide innovative insights into all facets of large language models and their applications in data science, including but not limited to science and algorithms of LLMs, enlarged language models, retrieval-augmented text generation, vision-language pretraining, vision transformers, trustworthiness and societal implications of LLMs, and LLMs on diverse applications are solicited.