issue network

Examples of issue network in the following topics:

• Introduction to relations

  • The other half of the design of network data has to do with what ties or relations are to be measured for the selected nodes.
  • There are two main issues to be discussed here.
  • In many network studies, all of the ties of a given type among all of the selected nodes are studied -- that is, a census is conducted.
  • There is also a second kind of sampling of ties that always occurs in network data.
  • When we collect network data, we are usually selecting, or sampling, from among a set of kinds of relations that we might have measured.

• After word

  • The application of existing methods to a wider range of social science problems, and the development of new methods to address additional issues in the social sciences are "cutting edges" in most social science disciplines.
  • Social network analysis is also increasingly connected to the broader field of network analysis.
  • These, and many other resources are cited in the web site for the International Network of Social Network Analysts (INSNA).
  • We've not provided a rigorous grounding of social network analysis in graph theory.
  • We've not given much attention to the cutting-edge issues of the evolution of networks, and the interface between network theory and complexity theory.

• Modality and levels of analysis

  • The network analyst tends to see individual people nested within networks of face-to-face relations with other persons.
  • A family, for example, is a network of close relations among a set of people.
  • Most social network analysts think of individual persons as being embedded in networks that are embedded in networks that are embedded in networks.
  • Statistical analysts deal with the same issues as "hierarchical" or "nested" designs.
  • In chapter 17, we'll take a look at some methods for multi-mode networks.

• Potential external relationship obstacles

  • Additionally, problems and issues may emerge due to the integration of services and systems provided by the vendor.
  • Problems within the networked organization usually arise due to the failure in identifying all stakeholders and network partners.
  • All nodes and partners in the networked organization have to know and recognize all the stakeholders involved.
  • Therefore, when choosing a supplier or a partner in the networked organization, having similar goals, missions, and similar ways of performing the business processes are vital for the success of the relationship.

• Introduction to describing one network

  • Many of these tools find immediate application in working with social network data.
  • There are, however, two quite important distinctive features of applying these tools to network data.
  • In applying statistics to network data, we are concerned the issues like the average strength of the relations between actors; we are concerned with questions like "is the strength of ties between actors in a network correlated with the centrality of the actors in the network?"
  • In network analysis, we focus on relations, not attributes.
  • Instead, alternative numerical approaches to estimating standard errors for network statistics are used.

• Components

  • Since the Knoke information network has a single component, it isn't very interesting as an example.
  • Let's look instead at the network of large donors to California political campaigns, where the strength of the relation between two actors is defined by the number of times that they contributed on the same side of an issue.
  • Network>Regions>Components> Simple graphs is used for binary data.
  • If we set a very high cut-off value of 13 issues in common, then our graph has only non-isolate component (made up of the Democratic Party and the School Employees union).
  • Progressively lower cut-offs produce multiple, separate components until we reach a value of 7 issues in common.

• Introduction: What's different about social network data?

  • On one hand, there really isn't anything about social network data that is all that unusual.
  • "Network" data (in their purest form) consist of a square array of measurements.
  • This is the first major emphasis of network analysis: seeing how actors are located or "embedded" in the overall network.But a network analyst is also likely to look at the data structure in a second way -- holistically.
  • Indeed, many of the techniques used by network analysts (like calculating correlations and distances) are applied exactly the same way to network data as they would be to conventional data.While it is possible to describe network data as just a special form of conventional data (and it is), network analysts look at the data in some rather fundamentally different ways.
  • But the special purposes and emphases of network research do call for some different considerations.In this chapter, we will take a look at some of the issues that arise in design, sampling, and measurement for social network analysis.

• Multiple relations

  • In the most common social network data set of actor-by-actor ties, only one kind of relation is described.
  • Actors may be tied together closely in one relational network, but be quite distant from one another in a different relational network.
  • The locations of actors in multi-relational networks and the structure of networks composed of multiple relations are some of the most interesting (and still relatively unexplored) areas of social network analysis.
  • One needs to be cautious here, however, not to confuse the simple possession of a common attribute (e.g. gender) with the presence of a tie (e.g. the exchange of views between two persons on issues of gender).
  • "multiplex" network data in chapter 16).

• Equivalence of distances: Maxsim

  • First, let's look at the "line" network (figure 14.5).
  • Figure 14.6 shows the results of analyzing this network with Network>Roles & Positions>Automorphic>MaxSim
  • This small part of a large piece of output (there are 100 donors in the network) shows that a number of non-Indian casinos and race-tracks cluster together, and separately from some other donors who are primarily concerned with education and ecological issues.
  • That is, actors that have similar positions in the network as a whole.
  • Automorphic equivalence of geodesic distances in the line network.

• The rules of cooperative business networking

  • Most cooperative business networks rely heavily on relationship building.
  • Rather than wait for partners to come to your aid, be the first to plan meetings, raise issues, tackle problems and introduce needs.
  • Cooperative business networks are professional relationships and should not be considered as friendships.Keep partners close yet still at ‘arm's length'.