These are additional functions that can be used to upload and analyze social networks.
The app can analyze any network in the Pajek “.net” format (see here for more information). Please:
Here is an example of a correct network file (directed, weighted):
*Vertices 6
1 "v1"
2 "v2"
3 "v3"
4 "v4"
5 "v5"
6 "v6"
*Arcs
2 6 1
3 4 10
3 5 1
4 5 5
4 1 1
5 6 1
5 1 3
6 3 1
Validating networks after the upload is mandatory. Only in the case validation succeeds you could proceed with the analysis. Please click on “Validate Networks” after the upload.
The module allows the calculation of different social network analysis metrics.
betweenness centrality
: calculates the betweenness centrality of nodes.
closeness centrality
: calculates the closeness centrality of nodes.
community detection
: finds network communities (partitions) by using the Louvain Clustering Algorithm.
degree centrality
: calculates the degree centrality of nodes (defined as the number of links incident upon a node). Includes weighted degree and in-degree and out-degree for directed networks. In case of directed networks, the contribution index is also calculated:
CI = (weighted out-degree - weighted in-degree) / (weighted all-degree)
distinctiveness centrality
: calculates the distinctiveness centrality of nodes. Includes in-distinctiveness and out-distinctiveness for directed networks.
network similarity (tf-idf cosine)
: calculates the degree of similarity among all networks. Specifically, a document-term matrix is created considering each network as a document (i.e. a matrix row) and calculating term-frequency as the weighted all-degree of nodes. Subsequently, the matrix is transformed following a TF-IDF logic and using L2 normalization. Cosine similarity is later used to calculate similarities. This operation usually makes sense when networks originate from text and represent links among words.
network similarity (jaccard)
: calculated the degree of similarity among all networks, using the Jaccard index (either weighted or unweighted).
rotating leadership
: calculates rotating leadership of network nodes (i.e., their oscillations in betweenness centrality). Please pay attention that betweenness variations are calculated sequentially (from one network to the next), taking files in alphabetical order (please be careful while labeling your networks and use letters instead of numbers). This is usually applied to time series of networks.
Consider arc weights
: if flagged, arc weights will be considered for the analysis. Otherwise, weighted networks will be dichotomized, in most cases. Arc weights are regarded as link strength (not as a distance), so their reciprocal value is used in some analyses.Directed network
: if flagged, it will tell the system to treat the network as a directed graph. If a directed graph has been uploaded and you leave this box unchecked, then your networks will be transformed to undirected graphs before some of the analysis.Remove Loops
: if flagged, loops will be removed.Resolution parameter for community detection
: will change the size of the communities, default to 1. Represents the time described here.Variation threshold for rotating leadership
: is the threshold used to define a significant betweenness oscillation, in the calculation of rotating leadership. It indicates the minimum percentage change in betweenness to produce an oscillation, considering one network and the one that follows (in alphabetical order).Alpha parameter for distinctiveness centrality
: is the value of the alpha coefficient used to calculate Distinctiveness Centrality. Default is 1.