ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA Home

ACM Transactions on Knowledge Discovery from Data

Volume 19, Issue 1

January 2025

The ACM Transactions on Knowledge Discovery from Data (TKDD) welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include: scalable and effective algorithms for data mining and data warehousing, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.

Editor-in-Chief:

Jian Pei

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Announcements

Double-Anonymous Peer Review

Starting June 1, 2024, TKDD will transition to a double-anonymous reviewing process. Please refer to Author’s Guidelines on how to prepare manuscripts to comply with the anonymity requirement.

ACM Updates Its Peer Review Policy

ACM is pleased to announce that its Publications Board has approved an updated Peer Review Policy. If you have any questions regarding the update, the associated FAQ addresses topics such as confidentiality, the use of large language models in the peer review process, conflicts of interest, and several other relevant concerns. If there are any issues that are not addressed in the FAQ, please contact ACM’s Director of Publications, Scott Delman.

New ACM Policy on Authorship

ACM has a new Policy on Authorship, covering a range of key topics, including the use of generative AI tools. Please familiarize yourself with the new policy and the associated list of Frequently Asked Questions.

Most Frequent Affiliations

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Latest Issue

Volume 19, Issue 1 January 2025
Issue-in-Progress
ISSN:1556-4681
EISSN:1556-472X
View Table of Contents
research-article
Exploiting Pre-Trained Language Models for Black-Box Attack against Knowledge Graph Embeddings
- Guangqian Yang
  University of Science and Technology of China, Hefei, China
  ,
- Lei Zhang
  University of Science and Technology of China, Hefei, China
  ,
- Yi Liu
  People’s Daily Online, Beijing, China
  ,
- Hongtao Xie
  University of Science and Technology of China, Hefei, China
  ,
- Zhendong Mao
  University of Science and Technology of China, Hefei, China
November 2024Article No.: 1 pp 1-14 https://doi.org/10.1145/3688850
research-article
Question Embedding on Weighted Heterogeneous Information Network for Knowledge Tracing
- Jianwen Sun
  National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China and Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China
  ,
- Shangheng Du
  Shanghai Institute of Artificial Intelligence for Education, East China Normal University, Shanghai, China and School of Computer Science and Technology, East China Normal University, Shanghai, China
  ,
- Jianpeng Zhou
  School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China
  ,
- Xin Yuan
  National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China and Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China
  ,
- Xiaoxuan Shen
  National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China and Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China
  ,
- Ruxia Liang
  National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China and Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China
December 2024Article No.: 17 pp 1-28 https://doi.org/10.1145/3703158