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Actor: An individual with agency who acts within a social network (and is represented as a node or point)
Acyclic network: A network which does not contain any cycles or reflexive links in which two nodes exchange resources (Acyclic networks include article citations which only refer in one direction—in the past—to refer to published works...or highly hierarchical organizations with resources and information only moving upwards to a central node.)
Adjacency: The phenomena of being neighbors
Adjacency matrix: A matrix in which individuals who are next to each other in a social space are listed together side-by-side (and thus the adjacency label); this matrix looks at the effects of proximity
Adoption curves: A distribution which shows the rate of acceptance or adoption of a new idea, technology, practice, or other aspect
Affiliation: The nature of interactions between vertices (nodes)
Affiliation network: A two-mode social network that consists of actors and events (as mutually exclusive categories, with no crossover in information type)
Affinity: A liking or sympathy for others; a sense of closeness
Alter (connections): Directly connected nodes to a focal or "ego" node; also known as neighbors in an ego network; nearest neighbor
Amplifier: A factor that magnifies or enlarges another force or message or energy
Arcs: Directed lines (with arrows on one or both ends) in a directed graph or directed sociogram
Asymmetry: An imbalance in a social relationship (or directed ties); potentially an unreciprocated relationship in which one side perceives a bonded tie and the other side does not
Attribute: A descriptor (descriptive variable) of a node; may lead to preferences or biases
Automorphic equivalence: The parallel structures existing between nodes and links; the concept of substitutability between nodes with similar structures (and presumed capabilities); a sense of similarity of roles within a social network
Backtesting: Testing the verifiability of a model by comparing what the model would suggest would happen given certain historical inputs as contrasted with what actually happened in those historical situations
Balanced / imbalanced networks: A theory of networks that suggests certain types of relationships within the network,with the idea that most networks work towards balance
Betweenness centrality: A measure of a node's or ego's centrality in a network (the number of shortest paths from all vertices to all others that pass through that node)
Bias: A preference of a vector or node in a social network
Binary graph: A graph which represents nodes as either a "yes" or "no" / present or non-present (binary) about a particular variable or phenomena
Block (bi-component): Elements (or divisions) of connected nodes in a graph that may be separated into components with the deletion of cutpoints
Blocking the matrix: Partitioning parts of a social network represented in a matrix to create blocks; sectioning off the matrix into partitions
Bonded ties: A reciprocal (co-present or co-occurring) relationships between two vertices or nodes (with the relationship represented as a double-headed arrow)
Broadcast search: Projecting a generalized message with a search request to the entire network
Broker: An individual who negotiates between two other parties; a go-between who manages informational or other resources / assets
Circle graphs: A diagram that places the vertices or nodes in a circle in order to highlight the actual connectors (lines, edges) between the various vertices or nodes; this form shows the highest concentrations of connections
Citation network: A social network of research citations showing which works are the most popular (or the most often cited)
Clique: A substructure in a social network in which every element of the set is connected to every other member of that set at a distance greater than one; a maximal fully-connected sub-graph which is a part of a larger graph or social network (with other variable definitions based on various analyses)
Closed trail: A walk between two actors that begins and ends with the same actor
Closed walk: A sequence of connections between two actors with clearly defined start and end points
Cluster: Groups of people represented as individual vertices who share dense close ties in local neighborhoods because of shared mutual interests; cohesive subgroups with various types of affiliations
Cohesion: The structural cohesion of a social network is determined by the degrees of vertices (the numbers of connectors leading to and from the vertices, which may be averaged out to describe the network's structural cohesion); a highly cohesive network can move information / messages much more efficiently
Complete network: A network with maximum density (theoretically where all possible vertices are connected? A network with high cohesive ties?)
Connected phase: A point at which a network changes from a disconnected phase to one where a critical point (or a "tipping point") has been reached where a sufficient number of nodes have converted to accepting a new practice, and there are sufficient numbers to maintain momentum on its own or to accelerate momentum of conversions
Constellation: A node-link diagram that depicts data that is associative or hierarchical
Constraint: A limitation or restriction; something that constrains
Contamination: The process of diffusion of something (an idea, a technology, a practice, a meme, or other element) through a social network
Co-present or co-occurring: Ties between vertices which require the participation of the involved nodes in a particular event; bonded ties
Critical mass: A sufficient amount of nodes (adopters) that may enable a network to achieve a "tipping point" or sufficient momentum to be self-sustaining in the acceptance of a new innovation (in a diffusion of innovation model)
Cutpoints: Places in a network where the removal of a node would divide the structure into unconnected parts; weak links / brokers / bridges between otherwise disconnected groups in a graph
Cycle: A restricted walk of three or more actors (nodes), all of whom are distinct except for the origin / destination actor
Cyclic network: A network with clusters that are generally within one rank with equality among the vertices
Data array: The setup of a data set in a spreadsheet (or multiple spreadsheets) with variables listed across the top row and each of the following rows below as individual records, with unique identifiers running down the left column (a classic rectangular data array)
De-duplication: Removing any repeated data from a data set to ensure clean data
Degrees of separation: The concept that people may be connected by close ties in a "small world" through low degrees of separation in the "human web" (an initial idea by Dr. Stanley Milgram); the famous "sx degrees of separation" suggests that people are only six steps away from anyone else in the world by the six steps (in a concept expressed by Frigyes Karinthy and popularized in a play by John Guare)
A dynamic version of this may be seen here.
This visualization was created on NetLogo and is courtesy of the following:
References
Wilensky, U. (2005). NetLogo Small Worlds model. http://ccl.northwestern.edu/netlogo/models/SmallWorlds. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, Illinois.
Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, Illinois.
Dendrogram: A branching tree ("dendron") diagram which depicts a hierarchy of categories based on degree of similarity or shared characteristics, usually used in biological taxonomy. These may be used to represent pedigree or genealogies. For example, common ancestors of academic traditions may be cited as influences in citation dendrograms (in academia); non-biological affinities may be represented in dendrograms. Genealogies show ties to common ancestors.
Density: The number of lines (connectors) in a simple network, described as a proportion of the maximum possible number of lines or ties (a percentage of extant lines or arcs divided by all possible lines or arcs)
Diameter (of a social network): The length of the longest path between connected actors; the span of the network (as an indicator of size)
Diffusion: The spreading of an innovation, disease, practice, information, or some other element through a social network (often enhanced by the density of the social network)
Directed graph (digraph): A node-link diagram that has directional lines (the presence or absence of arrow-heads at the line ends) or arcs indicating directional aspects of relationships (including whether the connections are reciprocated or not)
Directed search: Reaching out to a targeted few in a social network to locate particular information or resources
Direction: The course or impetus of a relationship
Dyad: A pair of vertices (nodes) and the lines linking them
Eccentricity: A measure of how far one node is from the furthest other in a social network (the mean and standard deviation of their geodesic distances to describe their closeness to other actors)
Edges (ties, lines or links): Line indicators in a social network that indicate relationships between individuals, cliques, and groups in that social network; undirected ties (lines without arrows showing directionality on the ends)
Ego: A focal node, a vertex that represents a specific individual or agent (from whose perspective the other aspects of the network may be viewed) and the various alters connected to that node (others in the "neighborhood"); an ego may also refer to groups, organizations, or even whole countries or societies
Ego network: A social network of various individuals with a special focus on the local (neighborhood) connections of individual actors; a vertex (node) and its neighbors, including all the lines among the selected vertices (nodes)
Embeddedness: The closeness of a particular node in an ego-network with other members, characterized by dense local sub-structure connections; the extent to which actors are in social structures with "dense, reciprocal, transitive, strong ties" (Hanneman & Riddle, 2005, Ch. 9, "Ego networks," p. 1)
Equivalence: A state of similarity between vertices with "zero dissimilarity" (or similarity of a lesser degree)
Eulerian circuit or cycle: A Eulerian trail (a path in a graph which visits every line exactly once which starts and ends on the same node or vertex)
Fat node: A vertex or node which has high in-degrees of lines or arcs or edges as well as high out-degrees of lines, showing high connectivity and assumed popularity and influence
Geodesic: The shortest path between two vertices or nodes
Graph: A systematic and condensed representation of information; a set of vertices or nodes and a set of lines between pairs of vertices (including some non-lines between vertices)
Hierarchical clustering: A form of cluster analysis that builds a hierarchy of cluster based on criteria such as affinity or relatedness (used in genomic data), often expressed in a dendrogram
Immediacy index: A measure of a publication's power in terms of the citations to the contents within the year of its publication (a rate based on the immediacy of response within the publication year)
Impact factor: A measure of a journal's power in terms of how many other articles cite that work
In-group: A social group to which an ego node belongs
In-degree: The amount of lines leading in to a particular node
Innovation: A high-innovation individual is a person with a low threshold for adoption of a new technique or technology; the tendency to be susceptible to new ideas moving through a social network
Intellectual pedigree: The association of an individual with other thinkers or practitioners in a field (in an affinity)
Interdependence: A mutually reciprocal relation between entities
Island: A maximal subnetwork of vertices connected by lines with values greater than the lines to vertices outside the subnetwork; a large cluster of highly interactive nodes with fewer connections to the outside
Isolate: A node which is not connected to other nodes in a network; a node which is on the periphery of a social network without anything in the way of a connection or relationship tie
Isomorphic equivalence: The visual equivalency of structure between ego nodes
Legible: Able to be read; clear
Length (of a walk): The number of relations contained within a walk
Linegraph: A nodelink diagram that indicates relationships by lines in between connected nodes
Lines: Edges (without arrows, in an undirected social network diagram) and arcs (with arrows, as "directed lines" in a digraph)
Loop: Reflexive connections from one node back to itself
Logistic growth: A sigmoidal (S-shaped) curve in which the growth rate decreases with the increasing number of entities until it reaches zero at a maximum point; a curve that models a gradual increase initially, a more rapid middle growth period, and then a slowing off at the end at a maximum value (sometimes used to model the diffusion of innovation in a population)
Main path analysis: A technique for analyzing citation networks that transport scientific knowledge of information over time in time increments, where a "main path" moves from a source vertex to a sink vertex with the highest traversal weights on the arcs in between
Matrix: A visualization of multivariate data consisting of rows and columns, with cells at the intersection of a row and a column; here each row and each column represent one vertex; a filled cell means the presence of a phenomenon while a blank cell means the absence of a phenomenon (in a binary); these can be reconfigured to be sorted by various descriptors of the groups to identify patterns
Multiplier effect: An entity or resource whose use will magnify or amplify the impact
Multiplex data: A stack of actor-by-actor matrices with similar defined factors, enabling comparability; data capturing the multiple roles of social actors
Multiplex relations: Multiple relationships among multiple vertices to show more complex relationships (as contrasted to "simplex" relations)
Neighborhood: The area around an "ego" including the "alters" that are linked to the focal ego node; this includes a connection up to some maximum path length (with a minimum of at least one step of connection); includes all the ties among all of the actors to whom the ego has a direct connection; these may be indicated by color or a circle or some other indicator (or sometimes no indicator at all)
Neighbors: The "alters" or direct nodes connected to an ego node in a neighborhood
Network analysis: Learning about social networks based on analyses of various aspects of the relational ties between the individuals in the network
Network load: The amount of traffic or information or other network resources that move through a network
Network size: The number of original nodes or egos (or individuals, groups, organizations, or societies) in the network
Node: A point or vertex in a social network which represents an individual (or actor) within that network
Node load: The measure of load placed on a given node to show its importance to the network; load is based in part on how connected that node is to other nodes (and the directions of the relationships between nodes
Nonbiological (non-kin) affinity: The ties between individuals who were influenced by the same predecessors and so are considered to belong to the same family or tradition in a field
N-step neighborhood: The size of an ego's neighborhood including all nodes at a path length of N, inclusive of all the connections among those actors (most neighborhood path lengths are 1, which include the egos and their adjacent nodes)
Null dyads: A pair of vertices (nodes) without any lines between them (no connectors)
Ordinal data: Rank-ordered data
Outdegree: The number of lines or edges leading from a particular node
Out-group: A social group to which an ego does not belong
(Pure) out-tree: A sociogram in which all actors are embedded into a single component as one structure, with no reciprocated ties and each node with an in-degree of one (or each actor has one boss, in a unified command, except the ultimate boss)
Partition: A part; a section
Path: A walk in which each actor and relation in the graph may be used at most one time (except for a closed path in which the first actor is also the ending actor); a Eulerian path defines a once-through of a network that touches every path / line once except for the starting node or vertex
Pendant: A case or node which is connected to the graph by only one tie; this has earned its name because such cases will "dangle" off more central cases that are heavily connected
Percolation theory: The concept that sudden changes seem to occur after a certain amount of percolation of changes, at which a tipping point is reached in connected clusters in a random graph
Perfect hierarchy: A social network in which all arcs (directional lines) point up, and none point down; an acyclic social network (a network without cycles or without arcs pointing down returning to a starting point); a perfect hierarchy suggests that all such networks have a concentration of power at the top with resources and information moving up and a potential enervating of the peripheries
Periphery: The outer edge of a social network, usually represented by nodes that are not connected to others in the network or are connected thinly to others
Permutation: The reordering or sorting or renumbering of vertices of a network to highlight particular descriptions of the network (with other patterning)
Popularity: The state of being recognized, appreciated, and liked by many
Power curve: An exponential curve that shows a high incidence of the phenomena early on but with a steep drop-off and then a long tail (said to represent various phenomenon in the world)
Power law: A mathematical relation between two quantities, when the frequency of an event varies as a power of an attribute; a power law distribution starts at its maximum value and decreases to infinity; this features a long tail leading towards infinity with a slower decay than the decay rate for a normal distribution, which suggests a greater likelihood of extreme events or variability
Predictive analytics: The uses of data to anticipate future behaviors, events, and trends, among other things (This is a kind of "forward testing.)
Property (bias): Tendencies as defined by node descriptors; tendencies extrapolated from node descriptors
Proximity: Closeness or nearness based on various dimensions: physical, spatial, relational, structural; temporal; emotional, or otherwise; vicinity
Random network: The nature of a network if the null hypothesis for the contents cannot be rejected; what a random network would look like (with each of the nodes having an equal opportunity of being chosen in this randomized network)
Rank: Stratification within social groups (whether discreetly or indiscreetly expressed) that may be inferred by the way information moves in a social network
Reachability: Any set of connections which trace from a source to the target actor
Salience: Importance, criticality, most noticeable
Scale-free networks: Networks whose degree distributions display power laws
Semi-path: "A semi-walk in which no vertex in between the first and last vertex of the semiwalk occurs more than once" (de Nooy, Mrvar, & Batagelj, 2005 / 2011, p. 78)
Semi-walk: A sequence of lines from vertex u to vertex v such that "the end vertex of one line is the starting vertex of the next line and the sequence starts at vertex us and ends at vertex v" (de Nooy, Mrvar, & Batagelj, 2005 / 2011, p. 77)
Sensitivity: The ability to respond to slight environmental or other changes
Simple graph: An undirected graph (nodes without lines)
Sink vertex (or "sink"): In an acyclic network (usually used for networks in time), a vertex with in-degree links (as the receiver of ties) but zero out-degree (which suggests an ending vertex on the periphery of a network or at least at the end of a main path analysis)
Small world phenomena: The existence of a social network in which there are clusters of nodes which enable the connection between one node and another through a few number of steps; a social network in which strangers may be connected through a mutual acquaintance; technically defined to be "a network where the typical distance L between two randomly chosen nodes (the number of steps required) grows proportionally to the logarithm of the number of nodes N in the network" ("Small-world network," Wikipedia) where
(For more on "small-world networks," visit the prior link from Mathematics Illuminated.)
Sociogram: A social network represented as a graph (bar charts, pie charts, trend charts, line charts, and others), node-link diagram, or other graphic display
Social prestige: Social recognition or respect
Social roles: A set of connected behaviors and obligations for individuals in a social situation (may be formal or informal)
Sociogram: An informational graphic that shows social links or the social structure of an individual, a group, or other social relations
Sociometry: The study of relationships among people, usually quantitative tools
Source vertex: In an acyclic network (usually used for networks in time), a vertex with zero indegree (no lines going into it, suggesting that these are originating vertexes)
Strong component: A maximally connected (cyclic) sub-network in which each vertex can reach any other vertex (with pairs of lines going in both directions to all vertices)
Strong link: An interpersonal tie that is close, long-standing, and over-which many resources may be exchanged
Structural hole: A triad of three nodes with one connected to the two nodes, who are not directed connected to each other
Structural prestige: The "social respect" indicated by the importance and power of a node in a social network
Subnetwork: A social cluster within a network
Symmetry: The sense of a balanced relationship (directed ties) in which two individuals (nodes) share the same sort of tie (bonded or non-bonded)
Thin node: A vertex with a low degree of connectivity with other vertices; low connectivity; low popularity
Threshold: A minimum limit which must be attained to create a certain effect
Trail: A walk between two actors that includes a given relation no more than once
Transitivity: A state of: when A = B and B = C, then A = C (from algebra)
Transposition: Switching the locations of two different objects with each other; reversing the order of objects
Traversal count / traversal rate: The extent to which a citation or article is needed for linking articles
Tree: A structure within social networks which do not contain any semicycles or cycles; a structure often used to show genealogies in time
Triad: Three vertices (nodes) which may be combined in a range of ways by the lines between them
Triad census: A listing of the triads in a social network (based on the types found as compared to the numbers expected); four possible types of triadic relationships are possible—with no ties, one tie, two ties, or three ties (in a nondirected network)
Triadic closure: The idea that if Node A knows B and B knows C, then Node C is more likely to know A than just anyone picked at random
Undirected lines (edges): Unordered or undirected pair of vertices or nodes
Universal classes: Groupings of networks which share broad assumptions and about which descriptive generalizations may be made
Universality: The idea of shared descriptions between various social networks
Valued graph: A graph that represents how the various individuals surveyed think or feel about a particular issue in terms of a ranked measurement
Vector: An entire social network matrix or part of a larger matrix; a singular column of data (that is part of a matrix)
Vertex (vertices, plural): The common end point of two or more rays or line segments; a corner or a point where lines meet (a point where three or more edges meet, in solid geometry); the highest or lowest points in a parabola (as in a quadratic equation)
Walk: The most general connection between two actors, usually a sequence of actors (nodes) and relations that begins and ends with actors; a sequence of nodes
Weak link: A weak tie between nodes; a link that is weakly traveled or weakly used
Borgatti, S.P., Everett, M.G. & Freeman, L.C. 2002. UCInet for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies.
Christakis, N.A. & Fowler, J.H. (2009). Connected: The Surprising Power of our Social Networks and How They Shape Our Lives. New York: Little, Brown and Company. 197.
De Nooy, W., Mrvar, A., & Batagelj, V. (2005, 2011). Exploratory Social Network Analysis with Pajek: Structural Analysis in the Social Sciences. Cambridge: Cambridge University Press.
Hanneman, Robert A. & Mark Riddle. (2005). Introduction to social network methods. Riverside, CA: University of California, Riverside ( published in digital form at http://faculty.ucr.edu/~hanneman/ )
Watts, D.J. (2003). Six Degrees: the Science of a Connected Age. New York: W.W. Norton & Company.
Introduction to Social Network Methods (Robert A. Hanneman and Mark Riddle)
Hanneman, Robert A. and Mark Riddle. (2005). Introduction to social network methods. Riverside, CA: University of California, Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/ )
http://faculty.ucr.edu/~hanneman/nettext/
Some Readings: Variations on a Theme
Kim, KT., Ko, S., Elmqvist, N., & Ebert, D.S. (2011). WordBridge: Using composite tag clouds in node-link diagrams for visualizing content and relations in text corpora. IEEE.
Ware, C. & Bobrow, R. (2004). Motion to support rapid interactive queries on node-link diagrams. ACM.
Zhao, S., McGuffin, M.J., & Chignell, M.H. (2005). Elastic hierarchies: Combining treemaps and node-link diagrams. IEEE.
Downloadable Data Sets
Public Data Sets (for UCINET): http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm
Preexisting Electronic Information
Some types of research draw from the textual communications between individuals and groups in online spaces like computer-supported collaborative learning (CSCL) work spaces; newsgroups; wikis; web logs (blogs); micro-blogs, help ticket systems, and other spaces. Others involve "scraping" social networking technologies. Still others draw from learning / course management systems and back-end data. Others conduct online or electronic surveys. Some use the online tagging features of various networks. Some are able to tap "big data" such as those gathered by large entities like Facebook(TM) and Google(TM), reportedly two or the largest data collectors in the world today.
