Using Gephi to determine trending subjects in libraries

Using Gephi to create a network of subjects, which were exposed to users who used the OPAC of the Peace Palace Library during the month of December, 2015, I am able to observe clusters of related subjects. In the upper level of this network I see one huge cluster with the subject “Human rights” more or less in the center of this cluster. Zooming in on this cluster enables me to weed out the subjects which are not strongly related to “Human rights”. After several zooming in sessions, one smaller cluster with “Human Rights” in the center remains.  Gephi no longer distinguishes subsections anymore. So the resulting table below contains a set of more strongly related subjects, centered around the main subject “Human rights” in December 2015.

slope	Label
-1.385	Human rights
-0.1599	Freedom of expression
-0.0741	Civil society
-0.0678	Human dignity
-0.0203	Oceania
-0.0171	Thailand
-0.0106	World Bank Group
-0.0102	Regional instruments
-0.0066	Blasphemy
-0.0036	Universal Declaration of Human Rights (New York, 10 December 1948)
-0.0033	Committee on the Elimination of Racial Discrimination
-0.0025	Human rights commissions
-0.0015	International Convention on the Elimination of All Forms of Racial Discrimination (New York, 7 March 1966)
-0.0012	Criminalization
-0.0009	Office of the United Nations High Commissioner for Human Rights
+0.2683	International instruments
+0.1403	Obligations of the state
+0.1362	Education
+0.1334	Civil and political rights
+0.0489	International law and domestic law
+0.045	Human Rights Committee
+0.0413	Legal remedies
+0.0409	International Covenant on Civil and Political Rights (New York, 16 December 1966)
+0.0202	Freedom of information
+0.0173	Hate speech
+0.0129	Reporting
+0.0078	Accountability
+0.0076	Committees
+0.0036	United Nations Human Rights Council
+0.0023	Treaty bodies
+0.0004	Freedom to provide education

How about the use of this set of interrelated subjects in other months of 2015? Is it possible to determine a trend during  the whole year 2015? In order to investigate this, I collected a lot of data which have to do with subjects. Every month of the year 2015 I collected all subjects exposed to our users of our OPAC and then let Gephi calculate several values related to each subject in these monthly networks. All these different values I stored in a database, but in the following I am just interested in the so-called betweenness centrality value. This value stands for, and I quote : “In brief,betweenness centrality is an indicator value for a key position. The higher the value the more important the role of the keyword. This value is calculated by counting the shortest paths between two keywords in our network. The keyword which appears the most times as being in between two different keywords, has the highest betweenness centrality value; these keywords are brokers or intermediaries.” [http://pushaqa.blogspot.nl/2014/11/just-below-surface.html]

So it is possible to measure the popularity of certain subject areas by using the 'weight' of these subject areas in the bigger picture of monthly subject networks. This means I used the values calculated in relation to the complete sets of subjects, not just te values of the subset. I then calculate the trendline of these weigths.  The slope of the trendline indicates an increase (positive slope) or a decrease (negative slope) in popularity. In the table of subjects above, I also mentioned the slope in accordance to each subject. A division in popularity is shown; there are subjects with a decreasing popularity like “Human rights” itself, or “Freedom of expression”. Increasing popularity can be observed in “International instruments” and “Obligations of the state”. If I take the complete set of subjects in consideration there is an average of -0.027, so a very slight decrease of interest in ‘Human rights and related subjects”.

There is a increase in interest if I do the same exercise with "Law of the sea": on average 0.142.

It is my belief that knowledge about the development of interest in a particular subject, can help libraries to create better services for its users.

Dit werk valt onder een Creative Commons Naamsvermelding-NietCommercieel-GeenAfgeleideWerken 4.0 Internationaal-licentie.

Just below the surface.

Using Gephi("an interactive visualization and exploration platform for all kinds of networks") to create unprocessed maps of exposed keywords to the user in the library of the Peace Palace, will result in an image in which a few huge subjects will dominate. These subjects are indicators of the core business of the library: Human Rights, European Union, United States of America, International Law and International Criminal Law to name just a few.To the left you see a very reduced image of such a map, but a few main keywords are still discernible.

These extra large topics veil the keywords just below. Zooming in will eventually bring you to the overshadowed keywords, but at a very deep level, so you will lose an overview of the structure. To the left we have zoomed in on an area clearly dominated by 'Human rights'. Now if I remove 'Human rights' from this cluster, Gephi will recalculate a lot of values, because one predominant element has been removed. After all, all keywords consitute one network. So the map gets a new shape. Especially, if all of the above mentioned subjects are removed and that is exactly what I have done. All the veiled keywords will float to the surface.

Let us now choose another criterion, in stead of the number of times a keyword occurs, to create a map. Gephi gives us a few other options, one of them is betweenness centrality.

Et voilà, after using the option 'rank parameter' in Gephi and choosing for betweenness centrality a new overall map appears, now with new highlighted nodes or keywords. Before zooming in, I will try to explain what betweenness centrality is. In brief, betweenness centrality is an indicator value for a key position. The higher the value the more important the role of the keyword. This value is calculated by counting the shortest paths between two keywords in our network. The keyword which appears the most times as being in between two different keywords, has the highest betweenness centrality value; these keywords are brokers or intermediaries. I used these values to create the map at the left.

After zooming in a little on the section of the map where the overall keyword 'Human Rights' used to be, a new picture arises. We see keywords like Children, Women and Family law, all of course related to Human rights and quite a few of them with a high key position or broker value. In short a new picture of related subjects emerges, indicating what the library of the Peace Palace could provide to its users.

By the way, the relations of keywords with a high betweenness centrality are not restricted to just one general subject. Between this kind of keywords there could be dense relations to other general subjects as well, see the image to the left. Using Gephi maps not only give students and scholars a tool in hand to explore the collections of libraries, it also is a clear reminder of the necessity of using keywords to conduct efficient bibliographic research.

Those of you who would like to have the data file used in Gephi to create all the maps shown, do contact me at a.janson at ppl dot nl.

My other blogs about 'Gephi in libraries':

• Keywords! Maps! Let's dive in.
• Library and user: one interest?
• Subjects by keywords.