**3. Methodological aspects of the study**

2992 scientific articles published between 2006 and 2015 were analyzed for this study, all indexed in WoS, Scopus and other regional and international bibliographic databases (PsycINFO, Scielo, REDALyC, Political Science Complete, ProQuest, and others). The works belonged to the areas of Psychology, Education, Law, Sociology, Political Sciences, Journalism and Other Social Sciences (Economics, Anthropology, i.a). The production was developed by 7774 researchers from 168 research groups classified in the Colombian National System of Science, Technology and Innovation (SCIENTI-Col).

The keyword networks analysis was accomplished through the construction of vertical edge matrices using the NodeXL Excel Template (2016 version) software. For the generation of sub-groups inside networks the cluster coefficient of each area of knowledge was used. The cluster coefficient is the measure in which the nodes of a graph tend to cluster together with a relatively high density of the links. In this study, the clusters were calculated using the Clauset-Newman-Moore algorithm [31], which is highly effective for inferring community structure from network topology, being much faster than other algorithms that precede it, as well as allowing the calculation of community structure analysis in very large networks. Subsequently the group metrics were calculated, being: word counting by semantic group, number of established connections, maximum geodetic distance (DGM), its respective statistic measure (GDμ), and the relational density of each group. As a general criterion, it was defined that the main groups chosen would have a minimum integration of 10 keywords.

studies surrounding health, life quality and well-being of individuals in the civil reintegration process are highlighted. The scientific interest is also attracted by child labor, particularly referring to exploitation, violation of rights and exposure to risk conditions associated with work.

**Group Group metrics Grouped thematic lines (group name)**

G2 24 75 6 3.007 0.170 Post-conflict and social recovery G3 22 75 4 2.112 0.212 Socio-environmental conflicts G4 21 67 4 2.222 0.210 Social reintegration and Life quality G5 21 63 4 2.150 0.190 Child labor and children development G6 20 66 5 2.300 0.211 Urban development, basic needs and Health

G8 10 33 2 1.380 0.467 Administrative and financial processes

**Table 1.** Keywords networks analysis in Other Social Sciences: description of metrics by thematic lines.

G7 11 37 2 1.455 0.400 Bibliometric studies

**Figure 1.** Graph of thematic groups with inter-group relations in Other Social Sciences.

G9 10 23 4 1.860 0.333 Logistics

G1 55 179 4 2.245 0.080 Science studies—social violence—diverse social

approaches

Identification of Research Thematic Approaches Based on Keywords Network Analysis…

http://dx.doi.org/10.5772/intechopen.76834

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**Words Connections MGDa GDμb Density**

Groups 8 and 9 cross the eminent social barrier of the other groups, focusing on financial or administrative nature issues. Clustering terms related to the organizational activities and

**Figure 1** shows that thematic groups in Other Social Sciences have low density. The main inter-group relations are given between groups 1, 2 and 3, and between 2 and 4. Few thematic

their financial affairs.

a = Maximum Geodetic Distance. b = Mean geodetic distance.

The visualizations of the networks (graphs) were organized using *grid algorithm,* which allows to clearly identify the sub-groups and their interaction.

The graph distribution was made with Harel-Koren Fast Multiscale [32], which eases the esthetic drawing of non-directed graphs with edges ordered in straight line, accomplishing the drawing procedure quickly for big networks. The node sizes were assigned according to the gross nodal degree obtained, that is the number of mentions in each term, the visualizations show all the nodes sized above 5, the lesser-graded nodes were overshadowed from 25 to 40% for the purpose of esthetic, simplicity and better readability of the graphs.
