Graph-Structured Data Base
A Graph-Structured Data Base is a multi-relational data base (composed of graph records) that represents graphs.
- AKA: Network/Linked Data.
- It can range from being a Structured Graph Dataset to being an Unstructured Graph Dataset.
- It can range from being a Undirected Graph Dataset to being a Directed Graph Dataset.
- It can range from being a Complex Graph Database (complex graphs) to being a Simple Graph Datafile (for simple graphs).
- It can be contained within a Graph Data Structure or within a Graph Data File.
- It can be managed by a Graph Data Management System.
- It can be processed by a Graph Data Processing System.
- It can be analyzed by a Graph Data Analysis System.
- a Semantic Graph Dataset.
- a Social Network Dataset, such as a DBLP Co-Author dataset.
- a Protein-Protein Interaction Dataset.
- a Knowledge Graph Database.
- a Taxonomy Database, such as a product taxonomy dataset or a CoA dataset.
- a KONNECT Benchmark Graph Dataset, such as KONNECT's Amazon ratings dataset
- See: Graph Query, Graph Data Metamodel.
- (Kunegis, 2017) ⇒ Jérôme Kunegis. (2017). “Handbook of Network Analysis [KONECT - the Koblenz Network Collection].” In: CoRR, abs/1402.5500.
- QUOTE: This is the handbook for the KONECT project, the “Koblenz Network Collection", a scientific project to collect, analyse, and provide network datasets for researchers in all related fields of research
- Below we provide information and pointers to datasets that are either already represented as a graph, or are relational in nature and lend themselves to a graph representation. Ultimately, we plan to evolve this resource into a collection of graph datasets that can be used as a testbed or benchmark for graph-based algorithms.