tie

(noun)

One or more equal values or sets of equal values in the data set.

Related Terms

Examples of tie in the following topics:

  • Summary

    • Each tie or relation may be directed (i.e. originates with a source actor and reaches a target actor), or it may be a tie that represents co-occurrence, co-presence, or a bonded-tie between the pair of actors.
    • Directed ties are represented with arrows, bonded-tie relations are represented with line segments.
    • The strength of ties among actors in a graph may be nominal or binary (represents presence or absence of a tie); signed (represents a negative tie, a positive tie, or no tie); ordinal (represents whether the tie is the strongest, next strongest, etc.); or valued (measured on an interval or ratio level).

  • F-groups

    • A strong tie triad is formed when, if there is a tie XY and a tie YZ, there is also a tie XZ that is equal in value to the XY and YZ ties.
    • A weakly transitive triad is formed if the ties XY and YZ are both stronger than the tie XZ, but the tie XZ is greater than some cut-off value.
    • Network>Subgroups>f-Groups takes the value of a strong tie to be equal to the largest valued tie in a graph.
    • The user selects the cut-off value for what constitutes a weak tie.
    • We have set our cut-off for a "weak tie" to be three campaigns in common.

  • Regular equivalence

    • These actors are regularly equivalent to one another because a) they have no tie with any actor in the first class (that is, with actor A) and b) each has a tie with an actor in the second class (either B or C or D).
    • Actors B, C, and D form a class because a) they each have a tie with a member of the first class (that is, with actor A) and b) they each have a tie with a member of the third class.
    • B and D actually have ties with two members of the third class, whereas actor C has a tie to only one member of the third class; this doesn't matter, as there is a tie to some member of the third class.
    • Actor A is in a class by itself, defined by a) a tie to at least one member of class two and b) no tie to any member of class three.

  • Introduction to kinds of graphs

    • Figure 3.2 is an example of a binary (as opposed to a signed or ordinal or valued) and directed (as opposed to a co-occurrence or co-presence or bonded-tie) graph.
    • Figure 3.3 is an example of a "co-occurrence" or "co-presence" or "bonded-tie" graph that is binary and undirected (or simple).

  • The probability of a dyadic tie: Leinhardt's P1

    • For any pair of actors in a directed graph, there are three possible relationships: no ties, an asymmetric tie, or a reciprocated tie.
    • This differs a bit from the approaches that we've examined so far which seek to predict either the presence/absence of a tie, or the strength of a tie.
    • The probability of a null relation (no tie) between two actors is a "residual."
    • Using the equations, it is possible to predict the probability of each directed tie based on the model's parameters.
    • For example, the model predicts a 93% chance of a tie from actor 1 to actor 2.

  • Simplex or multiplex relations in the graph

    • We graphed a tie if there was either a friendship or spousal relation.
    • But, we could have graphed a tie only if there were both a friendship and spousal tie (what would such a graph look like?

  • Combining multiple relations

    • Where there is no tie in either matrix, the type "0" has been assigned.
    • Where there is both a friendship and a spousal tie, the number "2" has been assigned; where there is a friendship tie, but no spousal tie, the number "3" has been assigned.
    • There could have been an additional type (spousal tie, but no friendship) which would have been assigned a different number.
    • This is a pretty useful tool kit, and captures most of the ways in which quantitative indexes might be created (weakest tie, strongest tie, average tie, interaction of ties).
    • In this dialog, we've said: if there is a friendship tie and there is no spousal tie, then code the output relation as "1."

  • Reciprocity

    • Actors A and B have reciprocated ties, actors B and C have a non-reciprocated tie, and actors A and C have no tie.
    • One approach is to focus on the dyads, and ask what proportion of pairs have a reciprocated tie between them?
    • This would yield one such tie for three possible pairs (AB, AC, BC), or a reciprocity rate of .333.
    • More commonly, analysts are concerned with the ratio of the number of pairs with a reciprocated tie relative to the number of pairs with any tie.

  • Visualizing multiplex relations

    • Alternatively, one can "bundle" the relations into qualitative types and represent them with a single graph using line of different colors or styles (e.g. kin tie = red; work tie = blue; kin and work tie = green).

  • Binary relations

    • There are several useful measures of tie profile similarity based on the matching idea that are calculated by Tools>Similarities
    • A very simple and often effective approach to measuring the similarity of two tie profiles is to count the number of times that actor A's tie to alter is the same as actor B's tie to alter, and express this as a percentage of the possible total.
    • The number .625 in the cell 2,1 means that, in comparing actor #1 and #2, they have the same tie (present or absent) to other actors 62.5% of the time.
    • One approach to solving this problem is to calculate the number of times that both actors report a tie (or the same type of tie) to the same third actors as a percentage of the total number of ties reported.
    • These differences could be either adding or dropping a tie, so the Hamming distance treats joint absence as similarity.