Social Network Visualizer

Social Network Visualizer (SocNetV) is a cross-platform, user-friendly tool for the analysis and visualization of Social Networks, built in C++ and Qt.

It lets you construct social networks (mathematical graphs) with a few clicks on a virtual canvas or load social network data of various formats (GraphML, GraphViz, Adjacency, Pajek, UCINET, etc).

SocNetV enables you to modify the social networks, analyse their social and mathematical properties, produce reports for these properties and apply visualization layouts for relevant presentation of each network. The application supports multirelational loading and editing. You can load a network consisting of multiple relations or create a network on your own and add multiple relations to it.

With regards to network analysis, SocNetV computes graph-theoretic properties, such as density, diameter, geodesics and distances (geodesic lengths), connectedness, eccentricity, etc. It also calculates advanced structural measures for social network analysis such as centrality and prestige indices (i.e. closeness centrality, betweeness centrality, information centrality, power centrality, proximity and rank prestige), triad census, cliques, clustering coefficient, etc.

Furthermore, random networks (Erdos-Renyi, Watts-Strogatz, ring lattice, etc) and well-known social network datasets (i.e. Padgett's Florentine families) can be easily recreated. SocNetV also offers a built-in web crawler, allowing you to automatically create networks from links found in a given initial URL.

SocNetV offers various layout algorithms based on either prominence indices (i.e. circular, level and nodal sizes by centrality score) or force-directed models (i.e. Eades, Fruchterman-Reingold, etc) for meaningful visualizations of the social networks.

The application includes a simple web crawler, which scans a given web page for links and visualizes the network of all webpages/sites linked from it.

There is also comprehensive documentation, both online and while running the application, which explains each feature and algorithm of SocNetV in detail.

Source code, packages and executables for Windows, Linux and Mac OS X are available.

The program is Free Software, licensed under the GNU General Public License 3 (GPL3).

Version 1.9 - June 23, 2015
Codename: "summer breeze"
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* Version 1.9 fixes many bugs and brings a faster matrix inverse routine
The new matrix inverse routine is now using LU decomposition.
Also Information Centrality is greatly improved in terms of computation speed.
PageRank Prestige algorithm corrected to compute PR using the correct formula. The initial PR score
of each node is now 1/N.
Bugs closed:
#1463069 wrong average distance when there are isolates
#1365037 certain sparse matrices crash socnetv on invertMatrix method
#1365582 centralityInformation() is slow when network N>100
#1463095 edge filter works but the user cannot undo
#1464422 wrong pagerank results
#1464430 socnetv refuses to read pajek files not starting with *Network
#1465774 edges do not always follow relations
#1463082 edge color change is not taking place
#1464418 socnetv crashes on pagerank computation on isolated nodes


Version 1.8 - June 05, 2015
Codename: "speedy"
--------------------------
* New feature: Clique census computes maximal cliques
The new clique census report includes aggregate counts of
cliques (up to clique number 4), along with disaggregation
by vertex and co-membership information.

* New feature: Scale-free random generation
SocNetV generates random scale-free networks of n nodes
according to the Barabási–Albert (BA) model which uses a
preferential attachment mechanism. The algorithm starts
with the given m0 connected nodes. In each step it adds
a single new node with m edges to existing nodes.
The probability that the new node will connect to an
existing node i is:
p_i = (α + d_i ^ p) / Sum_j (d_j)

* New feature: Improved Erdos-Renyi generation
This version includes the G(n,M) model. In this model,
a new random network is created witg n nodes and M edges.
SocNetV already supported the G(n,p) model where edges
are created with Bernoulli trials.

* New feature: improved dialogs
New improved dialogs for easy random network generation:
Scale-free, Erdos-Renyi, and Small-World.

* Bugfixes:
#1453743 CluCof is correctly computed in all cases.
#1457774 Node Properties dialog is now populated with
current node settings.