Información de la conferencia

KDD 2027: ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Día de Entrega:
2026-07-19
Fecha de Notificación:
2026-11-14
Fecha de conferencia:
2027-08-09
Ubicación:
San Jose, California, USA
Ediciones:
CCF: A   ICORE: A*   QUALIS: A1   Vistas: 2050957   Seguidores: 549   Asistentes: 61

Solicitud de Artículos

KDD 2027 (ACM SIGKDD Conference on Knowledge Discovery and Data Mining) is a CCF A / ICORE A* / QUALIS A1 conference held in San Jose, California, USA on 2027-08-09. The paper submission deadline is 2026-07-19. Acceptance notifications are sent on 2026-11-14.

Scope For the Research track, we invite submission of papers describing innovative research on all aspects of knowledge discovery, data science and AI, ranging from theoretical foundations to novel models and algorithms for applied problems in science, business, medicine, and engineering. Visionary papers on new and emerging topics are also welcome, as are application-oriented papers that make innovative technical contributions to research. Topics of interest include, but are not limited to: Foundations of Knowledge Discovery and Data Science. Submissions are invited to discuss core models, algorithms, and theoretical insights for knowledge discovery. Topics may include data-driven learning and structured knowledge extraction, including supervised, unsupervised, semi-supervised, and self-supervised learning, classification, regression, clustering, and dimensionality reduction; model selection and optimization; probabilistic and statistical methods (e.g., Bayesian inference, graphical models); matrix and tensor methods; structured and relational learning from data. Modern AI and Big Data. Submissions are invited to elaborate on the intersection of AI and massive data repositories. Topics may include deep representation learning, meta-learning, in-context learning, prompt engineering, continual learning, few-shot adaptation, reinforcement learning, generation, and reasoning, including generative models (e.g., GANs, VAEs), large language models (LLMs), multimodal foundation and frontier models operating on big data; AI’s role in emergent reasoning, automated insight generation, knowledge graph construction, hypothesis generation, neural-symbolic integration, and deriving novel concepts from large complex data. Trustworthy and Responsible Data Science. Submissions are invited to feature techniques and frameworks that ensure responsible data use, management, and analysis. Topics may include data security, data privacy, data transparency, accountability in data-driven systems, privacy-preserving learning, adversarial robustness, interpretability and explainability of models, decision support visualization, fairness in data mining, ethical data processing, algorithmic auditing, and frameworks for responsible AI development and deployment. Systems for Data Science and Scalable AI. Submissions that detail new architectures, systems, and infrastructures for large-scale data analysis and machine learning (e.g., distributed computing, federated learning, cloud-based systems) are invited. Topics may include efficient approaches to support high-volume data analysis, streaming, sampling, and summarization, data integration, transformation, and cleaning at scale, and data mining and machine learning for systems—machine learning for database management, learning device placement, orchestration, and scheduling of computational and data workflows. Data Science Applications. Submissions are invited for innovative data science and artificial intelligence (AI) applications. Topics may include methods for analyzing social science, and legal data, as well as time series, text, graphs, Internet of Things (IoT) data, and more. We also welcome contributions on recommender systems. New directions that push the boundaries of data science applications are of particular interest, such as quantum data science, including algorithms and information-theoretic approaches for quantum machine learning and data processing in quantum systems. For additional application domains from the physical and biological sciences and biomedical applications, we’d like to draw attention to the AI for Sciences track at KDD2027! Applications with strong relationships and applications to physical, biological and biomedical science domains are encouraged to consider this track. Papers that describe general methodologies that may apply in science as well as other KDD domains should continue to submit to the research track. Survey papers that seek to provide a comprehensive understanding of the current state of research on a specific topic, rather than to contribute a novel intellectual contribution, is out of scope.
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Tasa de aceptación

Tasa de aceptación media: 16.5% en 24 años (2000–2024).

AñoEnviadosAceptadosAceptados(%)
2024204640920%
2023141331322.2%
2022169525415%
2021154123815.4%
2020127921616.9%
2019120017014.2%
201898310710.9%
2017748648.6%
20161115665.9%
201581916019.5%
2014103615114.6%
201372612517.2%
201275513317.6%
201171412617.6%
201057810117.5%
200953710519.6%
200859311819.9%
200757311119.4%
20064575010.9%
20053587621.2%
20043374011.9%
20032583413.2%
20012035225.6%
20002465020.3%

Los Mejores Artículos

AñoLos Mejores Artículos
2025When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark
2025A Guide to Misinformation Detection Data and Evaluation
2025HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark
2025Improving Group Robustness on Spurious Correlation via Evidential Alignment
2025Monitoring Robustness and Individual Fairness
2025SIGEM: A Simple yet Effective Similarity based Graph Embedding Method
2025Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity
2025Brain Effective Connectivity Estimation via Fourier Spatiotemporal Attention
2025Verification of Incomplete Graph Unlearning through Adversarial Perturbations
2023All in One: Multi-task Prompting for Graph Neural Networks
2022Learning Causal Effects on Hypergraphs
2021Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries
2020On Sampled Metrics for Item Recommendation
2019Optimizing Impression Counts for Outdoor Advertising
2018Adversarial Attacks on Neural Networks for Graph Data
2017Accelerating Innovation Through Analogy Mining
2017HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network
2016Predicting Matchups and Preferences in Context
2016FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
2016Ranking Relevance in Yahoo Search
2016TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size
2016Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations
2016Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta
2016Contextual Intent Tracking for Personal Assistants
2016Matrix Computations and Optimization in Apache Spark
2015Efficient Algorithms for Public-Private Social Networks
2014Reducing the Sampling Complexity of Topic Models
2013Simple and Deterministic Matrix Sketching
2012Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping
2011Leakage in data mining: formulation, detection, and avoidance
2010Large linear classification when data cannot fit in memory
2010Connecting the dots between news articles
2009Collaborative filtering with temporal dynamics
2008Fastanova: an efficient algorithm for genome-wide association study
2007Predictive discrete latent factor models for large scale dyadic data
2007Cost-effective outbreak detection in networks
2006Training linear SVMs in linear time
2005Graphs over time: densification laws, shrinking diameters and possible explanations
2004A probabilistic framework for semi-supervised clustering
2003Maximizing the spread of influence through a social network
2002Pattern discovery in sequences under a Markov assumption
2001The 'DGX' distribution for mining massive, skewed data
2001Robust space transformations for distance-based operations
2000Hancock: a language for extracting signatures from data streams
1999MetaCost: A General Method for Making Classifiers Cost-Sensitive
1998Occam's Two Razors: The Sharp and the Blunt
1997Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions

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