R网络包：高效管理关系数据与可视化工具

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R语言是一种强大的编程语言，特别在统计计算和数据科学领域广泛应用。其中，network包是一个专注于处理和管理关系数据的关键工具，它旨在提供高效的数据结构来表示各种复杂的关系网络，包括但不限于无向/有向图、值/无值边、二元/多元关系等。这个包的核心是其内置的网络类，它可以有效地存储大量边，其存储需求主要取决于实际的边数量，这在处理大规模数据时具有显著的优势。 network包提供了多种构造函数，使得用户能够轻松创建和初始化不同类型的网络结构。它还设计了一套直观的接口，使得用户可以方便地操作和查询网络中的节点和边，执行基本的网络分析任务，如添加、删除或修改边，查找路径，以及度中心性等测量。为了提高开发者的工作效率，network包还支持C语言API，允许开发人员直接在后台代码中操作网络对象，从而实现更高级的功能定制和性能优化。此外，包中还包括了可视化功能，用户可以通过这些工具生成网络图，以便于理解和分析关系模式。学习network包时，关键知识点包括： 1. **网络数据结构**: 包括网络类的设计与实现，如何存储和访问节点（vertices）和边（edges）。 2. **网络类型**: 了解不同类型的网络（无向/有向、值/无值、二元/多元）及其应用场景。 3. **构建网络**: 使用构造函数创建各种网络，并理解如何添加和删除节点和边。 4. **网络操作**：熟悉基础操作如连接、断开、合并、拆分网络，以及度中心性、聚类系数等网络分析指标。 5. **接口与API**：掌握如何通过R语言和C语言接口进行交互，提高代码灵活性。 6. **可视化工具**: 学习如何使用包中的图形生成器，展示网络结构并进行解释。通过学习和实践network包，用户将能更好地处理和理解复杂的关联数据，这对于数据挖掘、社交网络分析、社区检测等领域具有重要意义。同时，理解并掌握这个工具也将增强你在R语言开发中的竞争力。

Carter T. Butts 7

Wasserman & Faust 1994); and emon, a set of interorganizational networks from search and

rescue operations collected by

Drabek, et al. (1981). flo consists of a single adjacency matrix,

and is useful for illustrating the process of converting data from adjacency matrix to network

form. emon, on the other hand, consists of a list of seven network objects with vertex and

edge metadata. emon is thus especially useful for illustrating the use of network objects for

rich d ata storage (in addition to being an interesting data set in its own right). Loading these

data sets is as simple as invoking the data command, like so:

> data("flo")

> data("emon")

Further information on each of these data sets is given in the network manual. We shall also

use these data sets as illustrative examples at various points within this paper.

3.2. Creating and viewing network objects

While importation is sometimes possible, in other cases we must create our own network

objects. network supports two basic approaches to this task: create the object from sc ratch,

or build it from existing relational data via coercion. Both methods are useful, and we

illustrate each here.

In the most minimal case, we begin by creating an empty network to which edges may

be added. This task is performed by the network.initialize routine, which serves as a

constructor for the network class. network.initialize takes the order of the desired graph

(i.e., n) as a required argument, and the required network attributes discussed in Section

2.2.2

may be passed as well. In the event that these are unspeciﬁed, it is assumed that a simple

digraph (d irected, no loops, hyperedges, multiplexity, or bipartitions) is desired. For example,

one may create and print an empty digraph like so:

> net <- network.initialize(5)

> net

Network attributes:

vertices = 5

directed = TRUE

hyper = FALSE

loops = FALSE

multiple = FALSE

bipartite = FALSE

total edges= 0

missing edges= 0

non-missing edges= 0

Vertex attribute names:

vertex.names

No edge attributes

8 network: Managing Relational Data in R

network has default print and summary methods, as well as low-level operators for assignment

and related operations. These do not show much in the above case, since the network in

question caries little information. To create a network along with a speciﬁed set of edges,

the preferred high-level constructor is the eponymous network. Like network.initialize,

this function returns a newly allocated network object having speciﬁed properties. Unlike

the former, however, network may be called with adjacency and/or attribute information.

Adjacency information may be passed by using a full or bipartite adjacency matrix, incidence

matrix, or edgelist as the function’s ﬁrst argument. These input types are deﬁned as follows:

Adjacency matrix: This must consist of a square matrix or two-dimensional array, whose

i, jth cell contains the value of the edge from i to j; as such, adjacency matrices

may only be used to specify dyadic networks. By default, edges are assumed to ex-

ist for all non-zero matrix values, and are constructed accordingly. Edge values may

be retained by passing ignore.eval = FALSE, as described in the manual page for

the network.adjacency constructor. The matrix.type for an adjacency matrix is

"adjacency".

Bipartite adjacency matrix: This must consist of a rectangular matrix or two-dimensional

array whose row and column elements reﬂect vertices belonging to the lower and up-

per sets of a bipartition (r espectively). Otherwise, the matrix is interpreted as per a

standard adjacency matrix. (Thus, a bipartite adjacency matrix is simply the upper

oﬀ-diagonal block of the full adjacency matrix for a bipartite graph, where vertices

have been ordered by partition membership. See also

Doreian, et al. (2005).) The

matrix.type for a bipartite adjacency matrix is "bipartite".

Incidence matrix: This must consist of a rectangular matrix or two-dimensional array

whose row elements represent vertices, and whose column elements represent edges. A

non-zero value is placed in the i, jth cell if vertex i is an endpoint of edge j. In the

directed case, negative values signify membership in the tail set of the corresponding

edge, while positive values signify membership in the edge’s head set. Unlike adjacency

matrices, incidence matrices can thus be used to describe hypergraphic edges (directed

or otherwise). Note, however, that an undirected hypergraph composed of two-endpoint

edges is not the same as a simple graph, since the edges of the former are necessarily

loop-like. When loops, hyper, and directed are all FALSE, therefore, the two positive

row-elements of an incidence matrix for each column are taken to signify the head and

tail elements of a dyadic edge. (This is without loss of generality, since such an incidence

matrix would otherwise be inadmissible.) When specifying that an incidence matrix is

to be used, matrix.type should be set to "incidence".

Edge list: This must consist of a rectangular matrix or two-dimensional array whose row

elements represent edges. The i, 1st cell of this structure is taken to be the ID of the

tail vertex for the edge with ID i, with the i, 2st cell containing the ID of the edge’s

head vertex. (Only dyadic networks may be input in this fashion.) Additional columns,

if present, are taken to contain edge attribute values. The matrix.type for an edge list

is "edgelist".

As one might suspect, the network function actually operates by ﬁrst calling

network.initialize to create the required object, and then calling an appropriate edge

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R网络包：高效管理关系数据与可视化工具

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