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UCINET是一款强大的社会网络分析软件，专为社会科学领域的研究人员设计，尤其在社会学、组织研究、政治科学等领域广泛应用。这个“ucinet.rar_UCINET_Ucinet-for-windows”压缩包包含了适用于Windows操作系统的UCINET版本，以及相关的学习资料，旨在帮助初学者快速掌握该工具的使用。 UCINET的核心功能包括数据导入、数据管理、网络测量和分析，以及结果的可视化。它支持多种数据格式，如Pajek、SNA、Dyadic、Netscape等，能够处理大型的网络数据集。以下是一些关于UCINET的关键知识点： 1. 数据导入：UCINET可以处理各种类型的社会网络数据，包括个体间的关系矩阵、二元链接数据、多边关系数据等。用户可以通过简单的步骤将这些数据导入到软件中进行分析。 2. 数据管理：软件提供了丰富的数据操作工具，如筛选、排序、合并和拆分网络，以及计算个体的中心性指标等，有助于用户对数据进行预处理和整理。 3. 网络测量：UCINET提供了一系列的社会网络测量指标，包括度中心性（Degree Centrality）、接近中心性（Closeness Centrality）、中间中心性（Betweenness Centrality）、特征向量中心性（Eigenvector Centrality）等，这些指标可以帮助研究人员识别网络中的关键节点。 4. 社会网络分析：除了基本的测量指标，UCINET还支持更复杂的分析，如社团检测（Community Detection）、聚类分析（Clustering Analysis）、路径分析（Path Analysis）和流分析（Flow Analysis），这些分析方法能够揭示网络结构、群体动态和信息传播模式。 5. 可视化：UCINET内置的NetDraw模块是其可视化工具，能够生成各种网络图，如节点-链接图、矩阵图、圈图等，帮助用户直观地理解网络结构。用户还可以自定义节点大小、颜色、形状，以及边的宽度和样式，以增强可视化效果。 6. 学习资料：压缩包中的“ucinet.pdf”很可能是UCINET的用户手册或教程，它将详细介绍软件的安装、界面、功能及操作方法，是初学者入门的重要参考资料。 UCINET是一款功能强大的社会网络分析工具，通过这个压缩包，初学者不仅可以获取软件本身，还能获得学习资料，从而快速理解和掌握社会网络分析的方法和技术。在探索复杂的社会关系、组织结构和信息流动时，UCINET是不可或缺的研究工具。

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Introduction to Social Network Methods: Chapter 1: Social Network Data

Introduction to Social Network Methods

1. Social Network Data

This page is part of an on-line textbook by Robert A. Hanneman and Mark Riddle of the Department of

Sociology at the University of California, Riverside. Feel free to use and reproduce this textbook (with

citation). For more information, or to offer comments, you can

send me e-mail.

Table of Contents

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

● Nodes

Populations, samples, and boundaries

Modality and levels of analysis

● Relations

Sampling ties

Multiple relations

● Scales of measurement

● A note on statistics and social network data

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.

Networkers do use a specialized language for describing the structure and contents of the sets

of observations that they use. But, network data can also be described and understood using

the ideas and concepts of more familiar methods, like cross-sectional survey research.

On the other hand, the data sets that networkers develop usually end up looking quite different

from the conventional rectangular data array so familiar to survey researchers and statistical

analysts. The differences are quite important because they lead us to look at our data in a

different way -- and even lead us to think differently about how to apply statistics.

"Conventional" sociological data consists of a rectangular array of measurements. The rows of

the array are the cases, or subjects, or observations. The columns consist of scores

(quantitative or qualitative) on attributes, or variables, or measures. Each cell of the array then

describes the score of some actor on some attribute. In some cases, there may be a third

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Introduction to Social Network Methods: Chapter 1: Social Network Data

dimension to these arrays, representing panels of observations or multiple groups.

Name Sex Age In-Degree

Bob Male 32 2

Carol Female 27 1

Ted Male 29 1

Alice Female 28 3

The fundamental data structure is one that leads us to compare how actors are similar or

dissimilar to each other across attributes (by comparing rows). Or, perhaps more commonly,

we examine how variables are similar or dissimilar to each other in their distributions across

actors (by comparing or correlating columns).

"Network" data (in their purest form) consist of a square array of measurements. The rows of

the array are the cases, or subjects, or observations. The columns of the array are -- and note

the key difference from conventional data -- the same set of cases, subjects, or observations.

In each cell of the array describes a relationship between the actors.

Who reports liking whom?

Choice:

Chooser: Bob Carol Ted Alice

Bob --- 0 1 1

Carol 1 --- 0 1

Ted 0 1 --- 1

Alice 1 0 0 ---

We could look at this data structure the same way as with attribute data. By comparing rows of

the array, we can see which actors are similar to which other actors in whom they choose. By

looking at the columns, we can see who is similar to whom in terms of being chosen by others.

These are useful ways to look at the data, because they help us to see which actors have

similar positions in the network. 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.

The analyst might note that there are about equal numbers of ones and zeros in the matrix.

This suggests that there is a moderate "density" of liking overall. The analyst might also

compare the cells above and below the diagonal to see if there is reciprocity in choices (e.g.

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Introduction to Social Network Methods: Chapter 1: Social Network Data

Bob chose Ted, did Ted choose Bob?). This is the second major emphasis of network

analysis: seeing how the whole pattern of individual choices gives rise to more holistic patterns.

It is quite possible to think of the network data set in the same terms as "conventional data."

One can think of the rows as simply a listing of cases, and the columns as attributes of each

actor (i.e. the relations with other actors can be thought of as "attributes" of each actor).

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. Rather than

thinking about how an actor's ties with other actors describes the attributes of "ego," network

analysts instead see a structure of connections, within which the actor is embedded. Actors

are described by their relations, not by their attributes. And, the relations themselves are just

as fundamental as the actors that they connect.

The major difference between conventional and network data is that conventional data focuses

on actors and attributes; network data focus on actors and relations. The difference in

emphasis is consequential for the choices that a researcher must make in deciding on

research design, in conducting sampling, developing measurement, and handling the resulting

data. It is not that the research tools used by network analysts are different from those of other

social scientists (they mostly are not). 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. Our discussion will focus on the two parts of network

data: nodes (or actors) and edges (or relations). We will try to show some of the ways in which

network data are similar to, and different from more familar actor by attribute data. We will

introduce some new terminology that makes it easier to describe the special features of

network data. Lastly, we will briefly discuss how the differences between network and actor-

attribute data are consequential for the application of statistical tools.

Return to the table of contents of this page

Nodes

Network data are defined by actors and by relations (or nodes and ties, etc.). The nodes or

actors part of network data would seem to be pretty straight-forward. Other empirical

approaches in the social sciences also think in terms of cases or subjects or sample elements

and the like. There is one difference with most network data, however, that makes a big

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Introduction to Social Network Methods: Chapter 1: Social Network Data

difference in how such data are usually collected -- and the kinds of samples and populations

that are studied.

Network analysis focuses on the relations among actors, and not individual actors and their

attributes. This means that the actors are usually not sampled independently, as in many other

kinds of studies (most typically, surveys). Suppose we are studying friendship ties, for

example. John has been selected to be in our sample. When we ask him, John identifies

seven friends. We need to track down each of those seven friends and ask them about their

friendship ties, as well. The seven friends are in our sample because John is (and vice-versa),

so the "sample elements" are no longer "independent."

The nodes or actors included in non-network studies tend to be the result of independent

probability sampling. Network studies are much more likely to include all of the actors who

occur within some (usually naturally occurring) boundary. Often network studies don't use

"samples" at all, at least in the conventional sense. Rather, they tend to include all of the

actors in some population or populations. Of course, the populations included in a network

study may be a sample of some larger set of populations. For example, when we study

patterns of interaction among students in a classrooms, we include all of the children in a

classroom (that is, we study the whole population of the classroom). The classroom itself,

though, might have been selected by probability methods from a population of classrooms (say

all of those in a school).

The use of whole populations as a way of selecting observations in (many) network studies

makes it important for the analyst to be clear about the boundaries of each population to be

studied, and how individual units of observation are to be selected within that population.

Network data sets also frequently involve several levels of analysis, with actors embedded at

the lowest level (i.e. network designs can be described using the language of "nested"

designs).

Return to the table of contents of this page

Populations, samples, and boundaries

Social network analysts rarely draw samples in their work. Most commonly, network analysts

will identify some population and conduct a census (i.e. include all elements of the population

as units of observation). A network analyst might examine all of the nouns and objects

occurring in a text, all of the persons at a birthday party, all members of a kinship group, of an

organization, neighborhood, or social class (e.g. landowners in a region, or royalty).

Survey research methods usually use a quite different approach to deciding which nodes to

study. A list is made of all nodes (sometimes stratified or clustered), and individual elements

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Introduction to Social Network Methods: Chapter 1: Social Network Data

are selected by probability methods. The logic of the method treats each individual as a

separate "replication" that is, in a sense, interchangeable with any other.

Because network methods focus on relations among actors, actors cannot be sampled

independently to be included as observations. If one actor happens to be selected, then we

must also include all other actors to whom our ego has (or could have) ties. As a result,

network approaches tend to study whole populations by means of census, rather than by

sample (we will discuss a number of exceptions to this shortly, under the topic of

sampling ties).

The populations that network analysts study are remarkably diverse. At one extreme, they

might consist of symbols in texts or sounds in verbalizations; at the other extreme, nations in

the world system of states might constitute the population of nodes. Perhaps most common, of

course, are populations of individual persons. In each case, however, the elements of the

population to be studied are defined by falling within some boundary.

The boundaries of the populations studied by network analysts are of two main types. Probably

most commonly, the boundaries are those imposed or created by the actors themselves. All

the members of a classroom, organization, club, neighborhood, or community can constitute a

population. These are naturally occuring clusters, or networks. So, in a sense, social network

studies often draw the boundaries around a population that is known, a priori, to be a network.

Alternatively, a network analyst might take a more "demographic" or "ecological" approach to

defining population boundaries. We might draw observations by contacting all of the people

who are found in a bounded spatial area, or who meet some criterion (having gross family

incomes over $1,000,000 per year). Here, we might have reason to suspect that networks

exist, but the entity being studied is an abstract aggregation imposed by the investigator --

rather than a pattern of institutionalized social action that has been identified and labeled by it's

participants.

Network analysts can expand the boundaries of their studies by replicating populations. Rather

than studying one neighborhood, we can study several. This type of design (which could use

sampling methods to select populations) allows for replication and for testing of hypotheses by

comparing populations. A second, and equally important way that network studies expand their

scope is by the inclusion of multiple levels of analyis, or modalities.

Return to the table of contents of this page

Modality and levels of analysis

The network analyst tends to see individual people nested within networks of face-to-face

relations with other persons. Often these networks of interpersonal relations become "social

facts" and take on a life of their own. A family, for example, is a network of close relations

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