The most basic way to approach a sociogram is as a data visualization. A data visualization consists of the data, the analyst and his / her questions, the mapping of the data to various visual attributes, and some level of interactivity. A surface reading of the visual will reveal some information, but applying various statistical analysis and rendering tools to the data will reveal even more.
Micro and Macro-Level Analysis: Node-link diagrams may be understood to be evaluated both at the micro and macro levels. The micro level involves particular ego nodes, so the individual point-of-view (POV) at that egocentric level. At this level, one can look at the node trajectories. One can also look at various features of individual nodes. The macro levels involve the nature of the social network (a sociocentric view) its breadth and depth; its types of nodes (its social network composition); its various connections / connectivity; its path lengths; the density of ties; its clustering; how it evolves; and what moves through the social network. In between are subnetworks or sub-cliques that may be pulled out for analysis. "Islands" and ego neighborhoods may also be focal points for research, depending on what it is that the data analyst may want to understand. A social network may be broken out into"partitions" that make it easier to analyze.
Layering (Complex) Information: Additional information may be layered or superimposed over a social network. There are visualizations that enable multi-dimensional viewing of a social network. Each of the layers of information have to be input and rendered for the various visualizations.
It helps to apply various statistical tools and algorithms to the data in order to output different visualizations that may be revealing. A transposition of data may show the opposite of a particular factor, to reveal further information. Or there can be data turned into binary information (like a dummy variable) to draw out further information.
Underlying all sociograms are various statistical techniques used in research. Understanding these techniques is critical in terms of using the software accurately and then representing the data analysis results.
Node (Ego) Attributes: The attributes of nodes are critical as well. The attribute may be described as an identity or role with related self-interests. Or a social network may actually be populated with real-world personalities and remote psychological readings on each based on their past patterns of behaviors, public statements, and public personas. The nature of nodes affects their choices and behaviors in a strategic context. Further, nodes may play multiple overlapping roles in a social network. The visual depiction should be relevant, and it should not be overwhelmed by complexity...but it also should not over-simplify the reality. Further, without textual labels of the nodes and more nuanced measures and indicators of relationships of the links, a diagram by itself will not have deeper analytical value.
The Criticality of Context / the Social Ecology: Further, a node-link diagram cannot be understood in isolation. It has to be understood in the context of the field. Looking at a node-link diagram without the background is not deeply informative. Said another way, social networks expressed as node-link diagrams (or other diagrams) are not fully stand-alone. They are one channel of information that must be used with other channels for actual meaning. In the same way that data must be triangulated with other sources, a social network diagram offers some information that may be combined with other data--such as geographic information systems (GIS) or spatialized data, survey or self-reported data, demographic information, press reports, and published analyses.
The Representation of Entitles and Relationships: Node-link diagrams are really about entities and the relationships between those entities. There is the assumption of dynamism, stochastic factors, and node-level self-interests. The social context, though, may shape power realities.
Network Centrality: Spatiality sometimes matters in a sociogram, such as in some which may place nodes in a core, semi-periphery, and periphery. Other times, spatiality is only a tool used for the expression of certain nodes and links. There are tools within various social networking software visualisation packages that enable clearer depictions (with less visual clutter). Other times, the users of the software can manually move the nodes to locations where the relationships are more visible (and the links follow automatically). Visual coherence is critical. Further, it helps to have an aesthetic sensibility regarding the diagram.
The Need for Accurate Data Sets (on the Back End): It is not possible to truly reverse engineer a data set and a data array from a sociogram. It helps to have the original data from which the sociogram was created. In this light, it's critical to originate your own data sets and ensure that the information is high quality; otherwise, it'll be much harder to analyze the information from that data.
Random Patterns: Not every pattern found is meaningful. Some apparent patterns may be due to random error alone. (The social network visualization and the interpretation of that visualization has to make sense with other known data, to a degree.)
A Human-Created Universe (of Sorts): One philosophical point at the heart of social network research is that humans do reify certain realities. They co-create certain realities in the world. In this context, perception and human decision-making matter.
Some Assumptions of Sociometry
Costs and Transactional Relationships: There are costs to create ties and linkages to others and to maintain them. While it is costly to maintain relationships in terms of social capital / time / resources / attention and other elements (and one has to assume a transactional element in all human relationships), there are further costs and risks to breaking ties, too, particularly if one's acquaintances are linked to a particular node that one is considering breaking off from. These ties entail "constraints." They limit some freedom of action; they limit the ability to de-link (temporarily or permanently) from a network or a portion of a network.
All social alliances are strategic. They are created for particular ends. Without shared interests, alliances tend to break off. In game theory, this is referred to as the continuous Prisoner's Dilemma, in which a player chooses constantly whether to cooperate with or defect from another. (With the prospect of a continuing relationship, the optimal way is for both to cooperate.)
Social Network Types: Heterogeneous networks are more beneficial than homogeneous ones. Heterogeneous ones include much more diversity and many more links, enabling many more connections. However, networks are about those who are included and those who are excluded. There are spoken and unspoken rules for joining and maintaining membership in a social network.
What Moves through Social Networks: All sorts of things move through social networks--information, resources, habits, diseases, attitudes, culture, and other elements. Some are positive, and some are negative. Understanding social networks enables ways to engage the larger world to troubleshoot issues and to multiply positive effects and to cut off negative ones (to a degree). There is the understanding that some rewiring of social networks may be possible.
Sociograms (sociomatrixes) consist of entities and relationships depicted on a 2-D grid space / graph.
Different types of information are better aligned to be expressed in certain visual formats. Certain types of data can only be expressed coherently in certain formats.
a dendrogram (a tree diagram, usually showing taxonomic relationships)
a block matrix (a matrix consisting of smaller partitions or "blocks")
an adjacency matrix (a graph which examines the adjacency of certain vertices / nodes to others)
"For each time point, adjacency (as spatial structure) and squad membership (as social context) are significant predictors of the social relations formed among the recruits."
(Note: Matrices are used to represent multi-variate data. Block matrices compare what the data shows about relationships against a random matrix, and these are analyzed by the ability / inability to reject the null hypothesis. In other words, how much of the variance from randomness can be attributed to the particular potential causal factor? A data set may also be compared with an idealized theoretical expectation of the data set to test out particular theories or hypotheses.)
Doreian, P. & Conti, N. 2012. Social context, spatial structure and social network structure. Social Networks: 34(1), 32 - 46.
a node-link diagram
a ring lattice
a two-mode network (containing entities / nodes and "events")
Origins of Social Network Research
The precursor thinkers to the social network include the following sociologists: David Émile Durkheim (social pathologies), Ferdinand Tönnies ("gemeninschaft and gesellschaft" / community and society), and Georg Simmel (social geometry, the metropolis).
The study of social networks started in the 1930's with the work of Jacob Moreno, who originated the "sociogram"--a connection diagram which shows people's connections with each other. John Barnes (a British anthropologist) originated the term "social network" in the 1950's.
This approach has been modified over the years.
Social network research combines network analysis (from computer science); graph theory (from math), and the social sciences.
Subject Domain Influences
Social network research draws on a variety of domain fields. One source cites the following influences: sociometry, psychometry, social anthropology, sociology, ecology, organizational studies, epidemiology, linguistics, political science, and discrete maths...
This practice has come to the fore in part because of advancements in technologies that enable the mass capturing of vast amounts of electronic data through various systems: networks, the Internet, email networks, the blogosphere, social sites, communications technologies, massively multiplayer online role-playing game (MMORPG) gaming platforms, knowledge structures, library collections, newspaper repositories, and others. Electronic data extraction is often less labor-intensive than conducting full data collection "manually."
Further, the practice of analyzing trace electronic information for understanding, analysis, and decision-making, has further added depth to social network research. Trace electronic information includes the minutiae of incidental data that is available in the world that may reveal hidden patterns that may be informative about human behaviors.
Social Network Research Today
Today, social network research is highly interdisciplinary. It is used to theorize; to research; to analyze, and to make decisions. A light sweep of the research literature has brought up hundreds of articles from various areas of study.
These include work in...
Some social networks are mapped to physical spaces.
As indicated above, such "social" networks apply to animals as well. They may be used to describe phenomena in society.
A Social Network Research "Sampler" (on the following pages)
The following sections offer a small sampler of some social network research insights about
