Co-citation analysis

Co-citation clustering finds groups of references that tend to be cited together in papers. The clustering is accomplished by building a cooccurrence matrix of references listing the number of common papers citing each pair of references. Normally, only highly cited references are included in the analysis.

Co-citation clustering

• The paper to reference matrix must be loaded into DIVA before co-citation clustering can be performed.
• In the TFGUI window, go to the Cooccurrence menu and select Use coocitation default. The cooccur_gui will appear. The primary entity will be reference and the secondary entity will be paper. This means that references will be clustered on cooccurrence in papers.
• If the number of references to cluster is greater than the number of clusters specified in coocur_gui, then DIVA will produce the number of clusters specified. Otherwise DIVA will produce cluster down to individual references, whatever that number is.
• It is normally best to cluster down to individual references, with number references between 50 to 200 references depending on the size of the dataset. A good rule of thumb is to used 50 references per 500 papers in the dataset.
• In citation clustering the occurrence threshold corresponds to the minimum number of times cited. References cited more times than the occurrence threshold are retained.
• You should experiment with the occurence threshold to get close to the desired number of retained references.
• Set the occurence threshold, click on Execute. An overwrite dialog will appear, click OK on this, then a dialog will appear telling the number of items (references) that will be clustered, and asking whether to continue.
• If the number of items is too few, click NO and go back and reduce the coocurrence threshold, if too few, click NO and increase the threashold. If the number of items that will be clustered is close to the desired number, then click Yes and clustering will proceed.
• Clustering will proceed quickly, but simulated annealing to seriate the dendrogram may take very long if many items are to be clustered.

Go to Make RF to reference crossmap