Social network analysis (SNA) software is software which facilitates quantitative or qualitative analysis of social networks, by describing features of a network either through numerical or visual representation.
Networks can consist of anything from families, [1] project teams, classrooms, sports teams, legislatures, nation-states, disease vectors, membership on networking websites like Twitter or Facebook, or even the Internet. Networks can consist of direct linkages between nodes or indirect linkages based upon shared attributes, shared attendance at events, or common affiliations. [2] Network features can be at the level of individual nodes, dyads, triads, ties and/or edges, or the entire network. For example, node-level features can include network phenomena such as betweenness and centrality, or individual attributes such as age, sex, or income. [3] SNA software generates these features from raw network data formatted in an edgelist, adjacency list, or adjacency matrix (also called sociomatrix), often combined with (individual/node-level) attribute data. [4] Though the majority of network analysis software uses a plain text ASCII data format, some software packages contain the capability to utilize relational databases to import and/or store network features.
Visual representations of social networks are important to understand network data and convey the result of the analysis. [5] Visualization often also facilitates qualitative interpretation of network data. With respect to visualization, network analysis tools are used to change the layout, colors, size and other properties of the network representation.
Some SNA software can perform predictive analysis. [6] This includes using network phenomena such as a tie to predict individual level outcomes (often called peer influence or contagion modeling), using individual-level phenomena to predict network outcomes such as the formation of a tie/edge (often called homophily models [7] ) or particular type of triad, or using network phenomena to predict other network phenomena, such as using a triad formation at time 0 to predict tie formation at time 1.
Product | Main Functionality | Input Format | Output Format | Platform | License and cost | Notes |
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Cytoscape | Network analysis and visualization software | .sif, .nnf, .gml, SBML, BioPAX, GraphML, Delimited text, .xls,. xlsx, Cytoscape.js JSON, Cytoscape CX | CX JSON / CX2 JSON, Cytoscapre.js JSON, GraphML, PSI-MI, XGMML, SIF | Windows, Linux, Mac | Open source | Cytoscape is a widely used open-source platform for visualizing and analyzing complex networks. It offers a user-friendly interface, extensive plugin support, and features for data integration and advanced analysis techniques. |
Gephi | Graph exploration and manipulation software | GEXF, GDF, GML, GraphML, Pajek NET, GraphViz DOT, CSV, UCINET DL, Tulip TPL, Netdraw VNA, Spreadsheet | CSV, GDF, GEXF, GraphML, Pajek NET, Spreadsheet, PDF, SVG | Any system supporting Java 1.6 and OpenGL | Open source (GPL3), seeking contributors | Gephi [8] is an interactive visualization and exploration platform for all kinds of networks and complex systems, dynamic and hierarchical graphs. It is a tool for people that have to explore and understand graphs. The user interacts with the representation, manipulate the structures, shapes and colors to reveal hidden properties. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new possibilities to work with complex data sets and produce valuable visual results. |
Graphviz | Graph visualization software | GraphViz(.dot) | Multiple image formats. | Windows, Linux, Mac | Open source (CPL) | Graphviz is open-source graph visualization framework. It has several main graph layout programs suitable for social network visualization. |
Network Overview Discovery Exploration for Excel (NodeXL) | Network analysis, content analysis and graph visualization software | xlsx (Excel 2010, 2013, 2016, 2019, 2021, 365), GDF, GEXF, Pajek, UCINet, GraphML | xlsx (Excel 2010, 2013, 2016, 2019, 2021, 365), csv, GDF, GEXF, Pajek, UCINet, GraphML, NodeXL Pro INSIGHTS, PowerPoint | Windows 10, 11 | NodeXL Basic is free, NodeXL Pro is a paid subscription | NodeXL is a (social) network analysis and visualization Add-in for Microsoft Excel written in C#. It integrates into Excel 2010, 2013, 2016, 2019, 2021, 365 and adds undirected and directed graphs as a chart type to the spreadsheet and calculates a core set of network metrics and scores. Supports data import from X (formerly Twitter), YouTube, Reddit, Wiki and Flickr social networks. Accepts edge lists and matrix representations of graphs. Allows for easy and automated manipulation and filtering of underlying data in spreadsheet format. Multiple network visualization layouts. Reads and writes Pajek, UCINet and GraphML files. |
NetMiner | All-in-one Software for Network Analysis and Visualization | .xls(Excel),.xlsx (Excel 2007), .csv(text), .dl(UCINET), .net(Pajek), .dat(StOCNET), .gml; NMF(proprietary) | .xls(Excel),.xlsx (Excel 2007), .csv(text), .dl(UCINET), .net(Pajek), .dat(StOCNET), NMF(proprietary) | Windows | Commercial with free trial | NetMiner is a software tool for exploratory analysis and visualization of large network data. NetMiner 4 embed internal Python-based script engine which equipped with the automatic Script Generator for unskilled users. Then the users can operate NetMiner 4 with existing GUI or programmable script language.
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Python | Social network analysis within the versatile and popular Python environment | Python will read in almost any format data file | Python has write capability for most data formats | Windows, Linux, Mac | Open source | Python contains several packages relevant for social network analysis:
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R | Social network analysis within the versatile and popular R environment | R will read in almost any format data file | R has write capability for most data formats | Windows, Linux, Mac | Open source | R contains several packages relevant for social network analysis:
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Tulip | Social Network Analysis tool | Tulip format (.tlp), GraphViz (.dot), GML, txt, adjacency matrix | .tlp, .gml, GraphVis format (.dot), GML, PNG / SVG / JPEG | Windows, Linux, Mac | Open source | Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Tulip aims to provide the developer with a complete library, supporting the design of interactive information visualization applications for relational data that can be tailored to the problems he or she is addressing. |
Vladimir Batagelj is a Slovenian mathematician and an emeritus professor of mathematics at the University of Ljubljana. He is known for his work in discrete mathematics and combinatorial optimization, particularly analysis of social networks and other large networks (blockmodeling).
Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes and the ties, edges, or links that connect them. Examples of social structures commonly visualized through social network analysis include social media networks, meme spread, information circulation, friendship and acquaintance networks, peer learner networks, business networks, knowledge networks, difficult working relationships, collaboration graphs, kinship, disease transmission, and sexual relationships. These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.
In mathematics, computer science and network science, network theory is a part of graph theory. It defines networks as graphs where the vertices or edges possess attributes. Network theory analyses these networks over the symmetric relations or asymmetric relations between their (discrete) components.
In sociology, social complexity is a conceptual framework used in the analysis of society. In the sciences, contemporary definitions of complexity are found in systems theory, wherein the phenomenon being studied has many parts and many possible arrangements of the parts; simultaneously, what is complex and what is simple are relative and change in time.
Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.
Barry Wellman was an American-Canadian sociologist and was the co-director of the Toronto-based international NetLab Network. His areas of research were community sociology, the Internet, human-computer interaction and social structure, as manifested in social networks in communities and organizations. His overarching interest was in the paradigm shift from group-centered relations to networked individualism. He has written or co-authored more than 300 articles, chapters, reports and books. Wellman was a professor at the Department of Sociology, University of Toronto for 46 years, from 1967 to 2013, including a five-year stint as S.D. Clark Professor.
Harrison Colyar White was an American sociologist who was the Giddings Professor of Sociology at Columbia University. White played an influential role in the “Harvard Revolution” in social networks and the New York School of relational sociology. He is credited with the development of a number of mathematical models of social structure including vacancy chains and blockmodels. He has been a leader of a revolution in sociology that is still in process, using models of social structure that are based on patterns of relations instead of the attributes and attitudes of individuals.
Mathematical sociology is an interdisciplinary field of research concerned with the use of mathematics within sociological research.
Dynamic network analysis (DNA) is an emergent scientific field that brings together traditional social network analysis (SNA), link analysis (LA), social simulation and multi-agent systems (MAS) within network science and network theory. Dynamic networks are a function of time to a set of graphs; for each time point there is a graph. This is akin to the definition of dynamical systems, in which the function is from time to an ambient space, where instead of ambient space time is translated to relationships between pairs of vertices.
A weighted network is a network where the ties among nodes have weights assigned to them. A network is a system whose elements are somehow connected. The elements of a system are represented as nodes and the connections among interacting elements are known as ties, edges, arcs, or links. The nodes might be neurons, individuals, groups, organisations, airports, or even countries, whereas ties can take the form of friendship, communication, collaboration, alliance, flow, or trade, to name a few.
A social network is a social structure made up of a set of social actors, sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures. The study of these structures uses social network analysis to identify local and global patterns, locate influential entities, and examine network dynamics.
NodeXL is a network analysis and visualization software package for Microsoft Excel 2007/2010/2013/2016. The package is similar to other network visualization tools such as Pajek, UCINet, and Gephi. It is widely applied in ring, mapping of vertex and edge, and customizable visual attributes and tags. NodeXL enables researchers to undertake social network analysis work metrics such as centrality, degree, and clustering, as well as monitor relational data and describe the overall relational network structure. When applied to Twitter data analysis, it showed the total network of all users participating in public discussion and its internal structure through data mining. It allows social Network analysis (SNA) to emphasize the relationships rather than the isolated individuals or organizations, allowing interested parties to investigate the two-way dialogue between organizations and the public. SNA also provides a flexible measurement system and parameter selection to confirm the influential nodes in the network, such as in-degree and out-degree centrality. The software contains network visualization, social network analysis features, access to social media network data importers, advanced network metrics, and automation.
Stanley Wasserman is an American statistician and prior to retirement was the Rudy Professor of Statistics, Psychology, and Sociology at Indiana University Bloomington and the Academic Supervisor of the International laboratory for Applied Network Research at Moscow's National Research University – Higher School of Economics. He is known for his work on social network analysis, mathematical sociology, network science and multidimensional networks. In 2017 Wasserman launched the Master's program 'Applied statistics with Network Analysis' at National Research University – Higher School of Economics.
Network homophily refers to the theory in network science which states that, based on node attributes, similar nodes may be more likely to attach to each other than dissimilar ones. The hypothesis is linked to the model of preferential attachment and it draws from the phenomenon of homophily in social sciences and much of the scientific analysis of the creation of social ties based on similarity comes from network science. In fact, empirical research seems to indicate the frequent occurrence of homophily in real networks. Homophily in social relations may lead to a commensurate distance in networks leading to the creation of clusters that have been observed in social networking services. Homophily is a key topic in network science as it can determine the speed of the diffusion of information and ideas.
Structural holes is a concept from social network research, originally developed by Ronald Stuart Burt. A structural hole is understood as a gap between two individuals who have complementary sources to information. The study of structural holes spans the fields of sociology, economics, and computer science. Burt introduced this concept in an attempt to explain the origin of differences in social capital. Burt’s theory suggests that individuals hold certain positional advantages/disadvantages from how they are embedded in neighborhoods or other social structures.
Autologistic actor attribute models (ALAAMs) are a family of statistical models used to model the occurrence of node attributes in network data. They are frequently used with social network data to model social influence, the process by which connections in a social network influence the outcomes experienced by nodes. The dependent variable can strictly be binary. However, they may be applied to any type of network data that incorporates binary, ordinal or continuous node attributes as dependent variables.
Blockmodeling is a set or a coherent framework, that is used for analyzing social structure and also for setting procedure(s) for partitioning (clustering) social network's units, based on specific patterns, which form a distinctive structure through interconnectivity. It is primarily used in statistics, machine learning and network science.
Patrick Doreian is an American mathematician and social scientist, whose specialty is network analysis. His specific research interests include blockmodeling, social structure and network processes.
Andrej Mrvar is a Slovenian computer scientist and a professor at the University of Ljubljana. He is known for his work in network analysis, graph drawing, decision making, virtual reality, electronic timing and data processing of sports competitions.
Peter Marsden is an American sociologist. He is the Edith and Benjamin Geisinger Professor of Sociology at Harvard University.