node

(noun)

They are the individual actors within the networks, and ties are the relationships between the actors.

Related Terms

Examples of node in the following topics:

  • Node attributes

    • But, generally, a k-core is a set of nodes that are more closely connected to one another than they are to nodes in other k-cores.
    • Figure 4.3 shows four sub-groups, which are colored to identify which nodes are members of which group (the "West" group and the "WRO" group each contain only a single node).
    • Differences of amount: Figure 4.3 also uses the size of the nodes (Properties>nodes>size>attribute-based) to display an index of the number of nodes in each group.
    • This difference of amount among the nodes is best indicated, visually, by assigning the size of the node to values of some attribute.
    • Once these quantities are computed, they can be added to NetDraw (Transform>node attribute editor>edit>add column), and then added to the graph (Properties>nodes>size>attribute-based).

  • Location, location, location

    • Where a node or a relation is drawn in the space is essentially arbitrary -- the full information about the network is contained in its list of nodes and relations.
    • Circle graphs are commonly used to visualize which nodes are most highly connected.
    • The nodes are located at equal distances around a circle, and nodes that are highly connected are very easy to quickly locate (e.g.
    • There are many reasonable definitions of what it means for two nodes to be "similar. " In this example, two nodes are "similar" to the extent that they have similar shortest paths (geodesic distances) to all other nodes.
    • "West" and "Educ" have very different patterns of ties to the other nodes.

  • Highlighting parts of the network

    • Nodes that aren't connected are called "isolates. " Some nodes may be connected to the network by a single tie.
    • The geodesic distance between two nodes is the length of the shortest path between them.
    • The resulting graphic is one way of understanding which nodes are most similar to one another, and how the nodes may be classified into "types" based on their patterns of connection to other nodes.
    • Points are colored, and the information about which nodes fall in which partitions (i.e. which cases are in which factions) is saved to the node attributes database.Analysis>Subgroups>Newman-Girvan.
    • The network formed by selecting a node, including all actors that are connected to that node, and all the connections among those other actors is called the "ego network" or (1-step) neighborhood of an actor.

  • Blocks and cut-points

    • An alternative approach to finding the key "weak" spots in the graph is to ask: if a node were removed, would the structure become divided into un-connected parts?
    • If there are such nodes, they are called "cutpoints. " And, one can imagine that such cutpoints may be particularly important actors -- who may act as brokers among otherwise disconnected groups.
    • That is, we try to find the nodes that connects the graph (if there are any).
    • This means that if EDUC (node 3) were removed, the WRO would become isolated.
    • Node 3, then, is a cut-point.

  • Introduction to finding equivalence Sets

    • Basically, the nodes of a graph are exchanged, and the distances among all pairs of actors in the new graph are compared to the original graph.
    • When the new graph and the old graph have the same distances among nodes, the graphs are isomorphic, and the "swapping" that was done identifies the isomorphic sub-graphs.
    • One approach to binary data, "all permutations," (Network>Roles & Positions>Automorphic>All Permutations) literally compares every possible swapping of nodes to find isomorphic graphs.
    • An alternative approach with the same intent ("optimization by tabu search") (Network>Roles & Positions>Exact>Optimization) can much more quickly sort nodes into a user-defined number of partitions in such a way as to maximize automorphic equivalence.
    • When we have measures of the strength, cost, or probability of relations among nodes (i.e. valued data), exact automorphic equivalence is far less likely.

  • A few hints on data handling with NetDraw

    • A second method is to build a dataset within NetDraw itself.Begin by creating a random network (File>Random).This creates an arbitrary network of 20 nodes.You can then use the node attributes editor (Transform>Node Attribute editor) and the link editor (Transform>Link Editor) to modify the nodes (and add or delete nodes) and their attributes; and to create connections among nodes.This is great for small, simple networks; for more complicated data, it's best to create the basic data set elsewhere and import it.
    • There are four sections of code here (not all are needed, but the *node data and *tie data are, to define the network structure).
    • *Node data lists variables describing the nodes.An ID variable is necessary, the other variables in the example describe attributes of each node.The (optional) *Node properties section lists the variables, and gives values for ID, location on the diagram (X and Y coordinates from the upper left corner), shape, size, color, etc.Usually, one will not create this code; rather you input the data, use NetDraw to create a diagram, and save the result as a file -- and this section (and the *Tie properties) is created for you.The *Tie data section is necessary to define the connections among the nodes.There is one data line for each relation in the graph.Each data line is described by its origin and destination, and value.Here.since there are two relations, "KNOKI" and "KNOKM" there are two values -- each of which happens, in our example, to be binary (but they could be valued).
    • When you are working with NetDraw, it is a good idea to save a copy of your work in the format (.vna, above) that is native to the program (File>Save Data As>Vna).This format keeps all of the information about your diagram (what's visible and not, node and line attributes, locations) so that you can re-open the diagram looking exactly as you left it.
    • You may also want to save datasets created with NetDraw to other program's formats.You won't be able to save all of the information about node and line properties and locations, but you can save the basic network (what are the nodes, which is connected to which) and node attributes.

  • Summary

    • A graph (sometimes called a sociogram) is composed of nodes (or actors or points) connected by edges (or relations or ties).
    • In speaking the position of one actor or node in a graph to other actors or nodes in a graph, we may refer to the focal actor as "ego" and the other actors as "alters

  • Introduction to nodes

    • Network data are defined by actors and by relations (or "nodes" and "edges").
    • The nodes or actors part of network data would seem to be pretty straight-forward.
    • The nodes or actors included in non-network studies tend to be the result of independent probability sampling.

  • Social Networks

    • If all of your friends are nodes in your Facebook social network, how many of your connections are strong ties?
    • Social networks are composed of nodes and ties.
    • The person or organization participating in the network is called a node.
    • Ties are the various types of connections between these nodes.
    • The looser and larger the network, the more likely nodes are to introduce new ideas and opportunities to their members.

  • Structural equivalence

    • Two nodes are said to be exactly structurally equivalent if they have the same relationships to all other nodes.
    • Actor G, again, is in a class by itself. its profile of ties with the other nodes in the diagram is unique.
    • The nodes in a structural equivalence class are, in a sense, in the same position with regard to all other actors.