File - Download Social Network Visualizer 2.2 Windows | ||||||||
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Always scroll to the bottom of the page to download files on OlderGeeks.com. We don't believe in fake/misleading download buttons and tricks. Tweet Social Network Visualizer 2.2 Windows Social Network Visualizer (SocNetV) is a cross-platform, user-friendly software for social network analysis and visualization. Social Networks are displayed and analyzed as mathematical graphs, where vertices depict actors/agents and edges represent their relations. With SocNetV you can draw social networks with a few clicks on a virtual canvas or load field data from various social network formats supported such as GraphML, GraphViz, Adjacency, Pajek, UCINET, etc. Furthermore, you can create random networks using various random network generation models (Barabási–Albert Scale-Free, Erdős–Rényi, Watts-Strogatz Small-World, d-regular, ring lattice, etc) or recreate famous social network analysis datasets, i.e. Padgett's Florentine families. A simple web crawler is also included to automatically create "social networks" from links found in a given initial URL. The crawler scans the given web page for links and visualizes the network of all webpages/sites linked from it. SocNetV enables you to edit your social network data through point-and-click, analyse their social and mathematical properties, produce reports for these properties and embed visualization layouts for relevant presentation of each network. It also supports multirelational loading and editing. You can load a social network consisting of multiple relations or create a social network on your own and add multiple relations to it. SocNetV easily computes basic graph-theoretic properties, such as density, diameter, geodesics and distances (geodesic lengths), connectedness, eccentricity, etc. But it also computes 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 pagerank prestige), triad census, clique census, clustering coefficient, etc. The application supports various layout algorithms based either on prominence indices (i.e. circular, level and nodal sizes by centrality score) or on force-directed models (i.e. Eades Spring Embedder, Fruchterman-Reingold, etc) for meaningful visualizations of the social networks. The program is Free Software, licensed under the GNU General Public License 3 (GPL3). You can copy it as many times as you wish, or modify it, provided you keep the same license. The documentation is also Free, licensed under the Free Documentation License (FDL). ChangeLog: Version 2.2 - Jan 21, 2016 Codename: "beyond" ------------------ * New feature: Hierarchical Clustering Analysis (HCA) - SocNetV can now perform hierarchical agglomerative cluster analysis on a social network. - Supported methods: Single-linkage (minimum), Complete-linkage (maximum) and Average-linkg (UPGMA). - The Structural Equivalence matrix can be computed from the adjacency or the geodesic distances matrix using a user-selected distance metric such as Euclidean distance, Manhattan distance Jaccard distance etc. - The result of the HCA is the list of clusters per clustering level and a dendrogram of the clusters hierarchy in SVG format. * New feature: Eigenvector Centrality (EVC) - Yet another centrality metric is now supported: Eigenvector centrality. - The EVC score of an actor is defined as the ith element of the leading eigenvector of the adjacency matrix. The leading eigenvector is the one corresponding to the largest positive eigenvalue. - This metric can also be used for embedding radial or level layout in the network. * New feature: Pearson product moment correlation coefficients - SocNetV can now correlate actor profiles (ties or distances to other actors) and compute a correlation matrix of pair-wise PCC scores. * New feature: Actor Similarity matrix - SocNetV compares the pair-wise tie/distance profiles of actors and produces a similarity matrix. - The user can select one of the supported measures (Simple Matching, Jaccard, Hamming, Cosine similarity or Euclidean distance) - The algorithm can compare rows (outbound links), columns or both of the input matrix. * New feature: Tie profile dissimilarities - SocNetV computes the pair-wise tie profile dissimilarities of the actors, using any of the supported "distance" metrics: Euclidean, Manhattan, Hamming, Jaccard, Chebyshev. - The algorithm uses the adjacency matrix as input. - It can compare rows (outbound links), columns (inbound links) or both. * New feature: Maximal clique census - SocNetV uses the Bronâ€“Kerbosch algorithm to compute all maximal cliques in an undirected graph. - The Clique Census report has been revamped with lots of useful statistics. * New feature: Symmetrize edges by examining Strong Ties - Given a network, the user can create a new relation with only strong ties (when both a->b and b->a exist). - The user has the option to create strong ties either from all relations or from current relation only * New feature: Cocitation matrix - SocNetV computes and displays the cocitation matrix of the network. - The Cocitation matrix, C=A*A^T, is a NxN matrix where each element (i,j) is the number of actors that have outbound ties/links to both actors i and j. * New feature: Cocitation network - SocNetV creates a new symmetric relation by connecting actors that are cocitated by others. - In the new relation, an edge will exist between actor i and actor j only if C(i,j) > 0, where C the Cocitation Matrix. Thus the actor pairs cited by more common neighbors will appear with a stronger tie between them than pairs those cited by fewer common neighbors. * New feature: Filter (temporarily disable) unilateral (non-reciprocal) edges. - This feature enables the user to have a directed network symmetrized by focusing only on the strong, reciprocal ties. * New feature: Multi-relational data read and write in GraphML - SocNetV now supports reading and writing .graphml files with multiple relations. * New feature: GML format support - The application can load and parse GML formatted network data. - At the moment only graph, directed, node, edge, id and labels work * New feature: Import multirelational directed networks from Pajek files - SocNetV can read Pajek files with multiple relations in *Arcs * New feature: Support for EdgeLists with labels - SocNetV can now parse simple and valued EdgeList files where the nodes are referenced by their labels. * New feature: HTML reports - SocNetV reports are now saved by default in HTML format. - This allows us to have a vastly improved formatting in all reports. - Centrality and Prestige reports allow the user to sort results by any column on-the-fly. - By default, HTML reports are opened in the system web browser. * New feature: Plot adjacency matrix - SocNetV can now plot the adjacency matrix to a file using unicode chars * New feature: Create basic subgraphs with one click - SocNetV can now create clique, star, cycle, and line subgraphs from given or selected vertices * Improved performance - SocNetV can multiply matrices faster, by using a recursive Exponentiation by squaring or Fast Modulo multiplication algorithm. - All TextEditor reporting windows will be closed on app exit/init * GUI improvements: Revamped Control Panel and more app icons in the toolbar - The left toolbox (Control Panel) interface was changed to provide a more organized one-click functionality. - The add/remove buttons for nodes and edges have been removed from the Control Panel. They now appear as toolbar icons, along with icons for node finding and properties, edge filtering and application Settings. - The Analyze options in the Control Panel were re-organized in Matrix, Cohesion, Prominence, Communities and Equivalence select popups. - In the Matrix popup list, the user can select which Matrix to compute and view, ie. Adjacency, Cocitation, Laplacian, etc. - The Cohesion popup list includes all basic graph-theory metrics: distances, walks, average distance, clustering coefficient etc. The Equivalence popup lists all available Structural Equivalence algorithms in SocNetV: HCA, Similarities, Pearson Coefficients, and Tie Profile Dissimilarities. * Cosmetic changes: - Select Relation Combo box now editable. User can change the name of the current relation on the fly. - In case of errors during file importing, the applicaiton informs the user about the line in the file where the errors occurs. * Datasets: - New dataset: Petersen Graph - Transformed Krackhardt: High-tech managers and Zachary Karate club into multirelational datasets * Bugs resolved: #1645504 Wrong distances in valued/weighted social networks #1463087 zero appeal parameter not working in scale free nets #1629997 Edges with floating point weights are not saved #1632857 creating nodes randomly may position some of them out of canvas #1634634 Wrong interpretation of edgedefault graph attribute value #1636525 Edge labels are not saved in GraphML when the network is undirected #1637890 Cannot read edgelist files where edges are declared by labels (include non-digit characters) #1632874 Rubber band selection does not always work #1633194 Edge arrows option does not exist in Settings #1633225 Deselect All does not deselect last clicked node #1622870 Fix large matrix formatting in txt exports #1637326 Loading multirelational pajek files leaves the app in "not saved" mode #1 Wrong Group Degree Centralization computation (https://github.com/socnetv/app/issues/1) #2 Use GitHub as code hosting/repository service (https://github.com/socnetv/app/issues/2) * Important Notice #1: Project's new domain: http://socnetv.org * Important Notice #2: Project's code and files are now hosted in Github. |
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Downloads | Views | Developer | Last Update | Version | Size | Type | Rank | |
518 | 3,141 | Dimitris V. Kalamaras <img src="http://www.oldergeeks.com/downloads/gallery/thumbs/Social Network Visualizer1_th.png"border="0"> | Jun 15, 2017 - 11:48 | 2.2 | 25.71MB | EXE | , out of 6 Votes. | |
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Social Network Visualizer Windows 2.2 | ||||||||
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56Kbit Modem: 1h 4m 12s · 256Kbit: 14m 3s · 1Mbit: 3m 31s · 5Mbit: 43s |
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