Transactions on Graph Data and Knowledge
Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik Β· Germany Β· Est. 2023
Aims & Scope
Transactions on Graph Data and Knowledge (TGDK) is an Open Access journal that publishes original research, resource and survey articles on graph-based abstractions for data and knowledge, and the techniques that such abstractions enable with respect to integration, querying, reasoning and learning. The scope of the journal thus intersects with areas such as Graph Algorithms, Graph Databases, Graph Representation Learning, Knowledge Graphs, Knowledge Representation, Linked Data and the Semantic Web. Also in-scope for the journal is research investigating graph-based abstractions of data and knowledge in the context of Data Integration, Data Science, Information Extraction, Information Retrieval, Machine Learning, Natural Language Processing, and the Web.
General Information
Submission Info
Ethics & Quality
Think.Check.Submit Compliance
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Frequently asked questions about Transactions on Graph Data and Knowledge
Is Transactions on Graph Data and Knowledge a predatory journal?
PubScope has no integrity flags on record for Transactions on Graph Data and Knowledge: it is indexed in DOAJ, and is not on DOAJ's withdrawn list. Its PubScope Trust Score is 58/100. Indexing is a transparency signal, not a guarantee β always confirm fit and policies before submitting.
What is the impact factor of Transactions on Graph Data and Knowledge?
Transactions on Graph Data and Knowledge is not in the Web of Science Core Collection, so it has no official Clarivate Journal Impact Factor.
Is Transactions on Graph Data and Knowledge indexed in Scopus and Web of Science?
Transactions on Graph Data and Knowledge is indexed in DOAJ.
What is the aims and scope of Transactions on Graph Data and Knowledge?
Transactions on Graph Data and Knowledge (TGDK) is an Open Access journal that publishes original research, resource and survey articles on graph-based abstractions for data and knowledge, and the techniques that such abstractions enable with respect to integration, querying, reasoning and learning. The scope of the journal thus intersects with areas such as Graph Algorithms, Graph Databases, Graph Representation Learning, Knowledge Graphs, Knowledge Representation, Linked Data and the Semantic Web. Also in-scope for the journal is research investigating graph-based abstractions of data and kn
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See all βData updated: 2026-05-26 Β· Sources: SJR, DOAJ, OpenAlex, WoS, Crossref