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ACM Transactions on Knowledge Discovery from Data is a peer-reviewed journal published by ACM, covering Complex Network Analysis Techniques, Advanced Graph Neural Networks, Topic Modeling. Over its lifetime it has published 1,488 papers, which have received 46,409 citations.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. ACM Transactions on Knowledge Discovery from Data 12, 3, Article 45 (2023).
Formats any DOI in ACM Transactions on Knowledge Discovery from Data style. No sign-up. |
| Publishes research in | Complex Network Analysis Techniques Advanced Graph Neural Networks Topic Modeling Recommender Systems and Techniques Data Management and Algorithms |
| ISSN | 1556-4681 |
| Citation impact (2-yr) | 5.46 |
| h-index | 88 |
| i10-index | 696 |
| Top institutions publishing here | Tsinghua University |
| Journal website | tkdd.acm.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k -anonymity has gained popularity. In a k -anonymized dataset, each record is indistinguishable from at least k − 1 other records with respect to certain identifying attributes. In this article, we…
How do real graphs evolve over time? What are normal growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs , identifying properties in a single snapshot of a large network or in a very small number of snapshots; these include heavy tails for in- and out-degree distributions, communities,…
Anomalies are data points that are few and different. As a result of these properties, we show that, anomalies are susceptible to a mechanism called isolation . This article proposes a method called Isolation Forest ( i Forest), which detects anomalies purely based on the concept of isolation without employing any distance or density measure---fundamentally…
As a prolific research area in data mining, subspace clustering and related problems induced a vast quantity of proposed solutions. However, many publications compare a new proposition—if at all—with one or two competitors, or even with a so-called “naïve” ad hoc solution, but fail to clarify the exact problem definition. As a consequence, even if…
An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced in this article. The main module consists of an algorithm to compute hierarchical estimates of the level sets of a density, following Hartigan’s classic model of density-contour clusters and trees. Such an algorithm generalizes and improves existing density-based clustering techniques with…
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