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第1行： − 此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。<br> − 由CecileLi初步审校，有的术语如“signed and bi directed graphs”等不确定实在抱歉（详见词条末尾） − 于2020.11.19再次审校，该词条专业性较强，修改过程中还是以文本为主，若有遗漏敬请谅解。 − 本词条由信白初步翻译<br> − In [[mathematics]], an '''incidence matrix''' is a [[Matrix (mathematics)|matrix]] that shows the relationship between two classes of objects. If the first class is ''X'' and the second is ''Y'', the matrix has one row for each element of ''X'' and one column for each element of ''Y''. The entry in row ''x'' and column ''y'' is 1 if ''x'' and ''y'' are related (called ''incident'' in this context) and 0 if they are not. There are variations; see below.+ − In mathematics, an incidence matrix is a matrix that shows the relationship between two classes of objects. If the first class is X and the second is Y, the matrix has one row for each element of X and one column for each element of Y. The entry in row x and column y is 1 if x and y are related (called incident in this context) and 0 if they are not. There are variations; see below. − 在数学中，'''<font color="#ff8000">关联矩阵 Incidence Matrix</font>'''是表示两类对象之间关系的矩阵。如果自变量是 x，因变量是 y，那么这个矩阵对于 x 的每个元素存在一行，对于 y 的每个元素存在一列。如果 x 和 y 是相关的(在本文中称为 incident) ，则行 x 和列 y 中的条目为1，如果它们不是相关的，则结果为0。但也有一些例外，请看下文。 + + − ==Graph theory 图论== − '''<font color="#ff8000">图论 Graph Theory</font>'''<br> − Incidence matrices are frequently used in [[graph theory]]. − Incidence matrices are frequently used in graph theory.+ + − 关联矩阵是图论中经常使用的一种表示方法。+ + − ==Undirected and directed graphs 无向图和有向图== − '''<font color="#ff8000">无向图和有向图 Undirected And Directed Graphs</font>''' − [[Image:Labeled undirected graph.svg|thumb|250px|An undirected graph.]] − 图：An undirected graph. 一个无向图。 − In graph theory an [[undirected graph]] has two kinds of incidence matrices: unoriented and oriented. + − − In graph theory an undirected graph has two kinds of incidence matrices: unoriented and oriented. − − 在图论中，'''<font color="#ff8000">无向图 Undirected Graph</font>'''有两种关联矩阵: 无向关联矩阵和有向关联矩阵。 − − − − The ''unoriented incidence matrix'' (or simply ''incidence matrix'') of an undirected graph is a {{nowrap|''n'' × ''m''}} [[Matrix (math)|matrix]] ''B'', where ''n'' and ''m'' are the numbers of [[Vertex (graph theory)|vertices]] and [[Edge (graph theory)|edge]]s respectively, such that {{nowrap|1=''B''<sub>''i'',''j''</sub> = 1}} if the vertex ''v''<sub>''i''</sub> and edge ''e''<sub>''j''</sub> are incident and 0 otherwise. − − The unoriented incidence matrix (or simply incidence matrix) of an undirected graph is a matrix B, where n and m are the numbers of vertices and edges respectively, such that if the vertex v<sub>i</sub> and edge e<sub>j</sub> are incident and 0 otherwise. − − 无向图的无向关联矩阵（也称为简单的关联矩阵）''B''，其中 ''n'' 和 ''m'' 分别是顶点和边的数目，如果顶点 v<sub>i</sub> 和边 e<sub>j</sub>是关联的，则结果为1，否则为0。 − − − − For example the incidence matrix of the undirected graph shown on the right is a matrix consisting of 4 rows (corresponding to the four vertices, 1–4) and 4 columns (corresponding to the four edges, e1–e4): − − For example the incidence matrix of the undirected graph shown on the right is a matrix consisting of 4 rows (corresponding to the four vertices, 1–4) and 4 columns (corresponding to the four edges, e1–e4): − − 如图右所示的无向图，它的关联矩阵是一个4行(分别对应4个顶点，1-4)4列(各自对应4条边，e<sub>1</sub>-e<sub>4</sub>)组成的矩阵： − − {| − − {| − − | − − | − − − {| align=left class=wikitable − − − { | align = left class = wikable − − − |- − − |- − − ! !! e<sub>1</sub> !! e<sub>2</sub> !! e<sub>3</sub> !! e<sub>4</sub> − − − !!!电子邮件！2! ！3 </sub > ！E < sub > 4 </sub > − − |- − − |- − − |- − − !1 − − − − |1||1||1||0 − − |1||1||1||0 − − − − |- − − |- − − !2 − − − !2 − − |1||0||0||0 − − − |1||0||0||0 − − |- − − |- − − − !3 − − !3 − − − − |0||1||0||1 − − |0||1||0||1 − − − |- − − |- − − − !4 − − !4 − − |0||0||1||1 − − |0||0||1||1 − − − |} − − − |} − − | = − − − | = − − − | − − | − − − <math> − − 《数学》 − − \begin{pmatrix} − − − 开始{ pmatrix } − − 1 & 1 & 1 & 0 \\ − − 1 & 1 & 1 & 0 \\ − − − 1 & 0 & 0 & 0 \\ − − 1 & 0 & 0 & 0 \\ − − − − 0 & 1 & 0 & 1 \\ − − 0 & 1 & 0 & 1 \\ − − 0 & 0 & 1 & 1 \\ − − − 0 & 0 & 1 & 1 \\ − − \end{pmatrix}. − − − 结束{ pmatrix }。 − − </math> − − − 数学 − − |} − − |} − − − − If we look at the incidence matrix, we see that the sum of each column is equal to 2. This is because each edge has a vertex connected to each end. − − If we look at the incidence matrix, we see that the sum of each column is equal to 2. This is because each edge has a vertex connected to each end. 第242行：
第64行： − − − The ''oriented incidence matrix'' of an undirected graph is the incidence matrix, in the sense of directed graphs, of any [[Orientation (graph theory)|orientation]] of the graph. That is, in the column of edge ''e'', there is one 1 in the row corresponding to one vertex of ''e'' and one −1 in the row corresponding to the other vertex of ''e'', and all other rows have 0. The oriented incidence matrix is unique [[up to]] negation of any of the columns, since negating the entries of a column corresponds to reversing the orientation of an edge. − − The oriented incidence matrix of an undirected graph is the incidence matrix, in the sense of directed graphs, of any orientation of the graph. That is, in the column of edge e, there is one 1 in the row corresponding to one vertex of e and one −1 in the row corresponding to the other vertex of e, and all other rows have 0. The oriented incidence matrix is unique up to negation of any of the columns, since negating the entries of a column corresponds to reversing the orientation of an edge. − − The unoriented incidence matrix of a graph ''G'' is related to the [[adjacency matrix]] of its [[line graph]] ''L''(''G'') by the following theorem: − − The unoriented incidence matrix of a graph G is related to the adjacency matrix of its line graph L(G) by the following theorem: 第259行：
第72行： − <math>A(L(G)) = B(G)^\textsf{T}B(G) - 2I_m.</math> − − A (l (g)) = b (g) ^ textsf { t } b (g)-2I _ m. </math > − + − − where ''A''(''L''(''G'')) is the adjacency matrix of the line graph of ''G'', ''B''(''G'') is the incidence matrix, and I<sub>''m''</sub> is the [[identity matrix]] of dimension ''m''. − − where A(L(G)) is the adjacency matrix of the line graph of G, B(G) is the incidence matrix, and I<sub>m</sub> is the identity matrix of dimension m. − − 第277行：
第81行： − + − <math>B(G) B(G)^\textsf{T}.</math> − − B (g) b (g) ^ textsf { t } − − − − The integral [[cycle space]] of a graph is equal to the [[null space]] of its oriented incidence matrix, viewed as a matrix over the [[integers]] or [[Real numbers|real]] or [[complex numbers]]. The binary cycle space is the null space of its oriented or unoriented incidence matrix, viewed as a matrix over the two-element [[Field (mathematics)|field]]. − The integral cycle space of a graph is equal to the null space of its oriented incidence matrix, viewed as a matrix over the integers or real or complex numbers. The binary cycle space is the null space of its oriented or unoriented incidence matrix, viewed as a matrix over the two-element field. − + − '''<font color="#ff8000">有符号双向图 Signed And Bidirected Graphs</font>'''<br> − The incidence matrix of a [[signed graph]] is a generalization of the oriented incidence matrix. It is the incidence matrix of any [[bidirected graph]] that orients the given signed graph. The column of a positive edge has a 1 in the row corresponding to one endpoint and a −1 in the row corresponding to the other endpoint, just like an edge in an ordinary (unsigned) graph. The column of a negative edge has either a 1 or a −1 in both rows. The line graph and Kirchhoff matrix properties generalize to signed graphs. − The incidence matrix of a signed graph is a generalization of the oriented incidence matrix. It is the incidence matrix of any bidirected graph that orients the given signed graph. The column of a positive edge has a 1 in the row corresponding to one endpoint and a −1 in the row corresponding to the other endpoint, just like an edge in an ordinary (unsigned) graph. The column of a negative edge has either a 1 or a −1 in both rows. The line graph and Kirchhoff matrix properties generalize to signed graphs.+ − '''<font color="#ff8000">有符号图 Signed Graph</font>'''的关联矩阵是有向关联矩阵的推广。它是任意双向图的关联矩阵，并为给定的有符号图定向。正边的列在对应一个端点的行有1，在对应于另一个端点的行中有 -1，就像普通'''<font color="#ff8000">(无符号)图 Unsigned Graph</font>'''中的边一样。负边的列在两行中都有1或 -1。线图和 Kirchhoff 矩阵性质都能推广到符号图中。+ − ==Multigraphs 多重图== − '''<font color="#ff8000">多重图 Multigraphs</font>'''<br> − The definitions of incidence matrix apply to graphs with [[loop (graph theory)|loops]] and [[multiple edges]]. The column of an oriented incidence matrix that corresponds to a loop is all zero, unless the graph is signed and the loop is negative; then the column is all zero except for ±2 in the row of its incident vertex.+ − The definitions of incidence matrix apply to graphs with loops and multiple edges. The column of an oriented incidence matrix that corresponds to a loop is all zero, unless the graph is signed and the loop is negative; then the column is all zero except for ±2 in the row of its incident vertex. − − '''<font color="#ff8000">多重关联图 Multigraphs</font>'''的定义：多重关联图与循环相对应的定向关联矩阵的列均为零，除了图有符号且循环为负的情况外，该列除其入射顶点行中的±2外均为零。 − + − − Because the edges of ordinary graphs can only have two vertices (one at each end), the column of an incidence matrix for graphs can only have two non-zero entries. By contrast, a [[hypergraph]] can have multiple vertices assigned to one edge; thus, a general matrix of non-negative integers describes a hypergraph. − − Because the edges of ordinary graphs can only have two vertices (one at each end), the column of an incidence matrix for graphs can only have two non-zero entries. By contrast, a hypergraph can have multiple vertices assigned to one edge; thus, a general matrix of non-negative integers describes a hypergraph. − ==Incidence structures 关联结构== − '''<font color="#ff8000">关联结构 Incidence Structures</font>'''<br> − The ''incidence matrix'' of an [[incidence structure]] ''C'' is a {{nowrap|''p'' × ''q''}} matrix ''B'' (or its transpose), where ''p'' and ''q'' are the number of ''points'' and ''lines'' respectively, such that {{nowrap|1=''B''<sub>''i'',''j''</sub> = 1}} if the point ''p''<sub>i</sub> and line ''L''<sub>''j''</sub> are incident and 0 otherwise. In this case, the incidence matrix is also a [[biadjacency matrix]] of the [[Levi graph]] of the structure. As there is a [[hypergraph]] for every Levi graph, and ''vice versa'', the incidence matrix of an incidence structure describes a hypergraph.+ − − The incidence matrix of an incidence structure C is a matrix B (or its transpose), where p and q are the number of points and lines respectively, such that if the point p<sub>i</sub> and line L<sub>j</sub> are incident and 0 otherwise. In this case, the incidence matrix is also a biadjacency matrix of the Levi graph of the structure. As there is a hypergraph for every Levi graph, and vice versa, the incidence matrix of an incidence structure describes a hypergraph. − ==Finite geometries 有限几何== − '''<font color="#ff8000">有限几何 Finite Geometries</font>'''<br> − An important example is a [[finite geometry]]. For instance, in a finite plane, ''X'' is the set of points and ''Y'' is the set of lines. In a finite geometry of higher dimension, ''X'' could be the set of points and ''Y'' could be the set of subspaces of dimension one less than the dimension of the entire space (hyperplanes); or, more generally, ''X'' could be the set of all subspaces of one dimension ''d'' and ''Y'' the set of all subspaces of another dimension ''e'', with incidence defined as containment.+ − An important example is a finite geometry. For instance, in a finite plane, X is the set of points and Y is the set of lines. In a finite geometry of higher dimension, X could be the set of points and Y could be the set of subspaces of dimension one less than the dimension of the entire space (hyperplanes); or, more generally, X could be the set of all subspaces of one dimension d and Y the set of all subspaces of another dimension e, with incidence defined as containment.+ − 举一个有限几何的重要例子。例如，在有限平面中,''X'' 是点的集合,''Y'' 是线的集合。在高维有限几何中，''X'' 仍可以是点的集合，''Y'' 可以是低于整个空间维数的一维子空间(超平面)的集合; 或者，更一般地，''X'' 可以是一维子空间 ''d'' 的所有子空间的集合，''Y'' 可以是另一维子空间 ''e'' 的所有子空间的集合，关联度定义也包含在内。 − + − '''<font color="#ff8000">多面体 Polytopes</font>'''<br> − In a similar manner, the relationship between cells whose dimensions differ by one in a polytope, can be represented by an incidence matrix.<ref>{{citation|first=H.S.M.|last=Coxeter|author-link=Coxeter|title=[[Regular Polytopes (book)|Regular Polytopes]]|year=1973|edition=3rd|origyear=1963|publisher=Dover|isbn=0-486-61480-8|pages=[https://archive.org/details/regularpolytopes0000coxe/page/166 166-167]}}</ref>+ − In a similar manner, the relationship between cells whose dimensions differ by one in a polytope, can be represented by an incidence matrix. − 以类似的方式，在多面体中尺寸相差一个的细胞之间的关系可以由关联矩阵表示。+ − ==Block designs 区组设计/块设计== − '''<font color="#ff8000">区组设计/块设计 Block Designs</font>'''<br> − Another example is a [[block design]]. Here ''X'' is a finite set of "points" and ''Y'' is a class of subsets of ''X'', called "blocks", subject to rules that depend on the type of design. The incidence matrix is an important tool in the theory of block designs. For instance, it can be used to prove [[Fisher's inequality]], a fundamental theorem of balanced incomplete 2-designs (BIBDs), that the number of blocks is at least the number of points.<ref>{{citation|page=99|first=Herbert John|last=Ryser|title=Combinatorial Mathematics|series=The Carus Mathematical Monographs #14|publisher=The Mathematical Association of America|year=1963}}</ref> Considering the blocks as a system of sets, the [[Permanent (mathematics)|permanent]] of the incidence matrix is the number of [[system of distinct representatives|systems of distinct representatives]] (SDRs). − Another example is a block design. Here X is a finite set of "points" and Y is a class of subsets of X, called "blocks", subject to rules that depend on the type of design. The incidence matrix is an important tool in the theory of block designs. For instance, it can be used to prove Fisher's inequality, a fundamental theorem of balanced incomplete 2-designs (BIBDs), that the number of blocks is at least the number of points. Considering the blocks as a system of sets, the permanent of the incidence matrix is the number of systems of distinct representatives (SDRs).+ − 另一个例子是区块设计。这里 ''X'' 是一个有限的“点”集合，而 ''Y'' 是 ''X'' 的一类子集，称为“块” ，它受设计类型规则的制约。关联矩阵是块设计理论中的一个重要工具。例如，它可以用来证明'''<font color="#ff8000">Fisher不等式 Fisher's inequality</font>'''，一个平衡不完全2- 设计(BIBDs)的基本定理，块的数目至少是点的数目。将块看作一个集合系统，关联矩阵的常数是不同代表系统的个数（SDRs）。+ − − − ==Further reading 拓展阅读== + − + − − ==External links 外部链接== − {{Commons category|Incidence matrices of graphs}} − {{Wiktionary}}+ + + + − * {{mathworld | urlname = IncidenceMatrix | title = Incidence matrix}}+ + + − + − − {{Graph representations}} − − [[Category:Algebraic graph theory]] − − Category:Algebraic graph theory − − 范畴: 代数图论 − − [[Category:Combinatorics]] − − Category:Combinatorics − − 分类: 组合数学 − − [[Category:Matrices]] − − Category:Matrices − − 分类: 矩阵 − − [[Category:Graph data structures]] − − Category:Graph data structures − − 类别: 图形数据结构 − − − − <small>This page was moved from [[wikipedia:en:Incidence matrix]]. Its edit history can be viewed at [[关联矩阵/edithistory]]</small></noinclude> − − [[Category:待整理页面]] − − 一个文科生的独白：抱歉各位，这次这个链接是关于矩阵的，我高中文科没学过，最近线性代数学校体系先讲行列式后才刚刚开矩阵板块，可能如果翻译part有些术语不精准有错误的话不能很好的识别实在对不起……有的术语我查了查像什么“signed and bi directed graphs”好像也没有中文翻译的词条...我看的主要还是句子连接上有没有问题，要是还是有不精准or错误的地方实在抱歉啦
无编辑摘要
在数学中，'''<font color="#ff8000">关联矩阵 Incidence Matrix</font>'''是表示两类对象之间关系的矩阵。如果自变量是 x，因变量是 y，那么这个矩阵对于 x 的每个元素存在一行，对于 y 的每个元素存在一列。如果 x 和 y 是相关的 ，则行 x 和列 y 中的条目为1，如果它们不是相关的，则结果为0。但也有一些例外，请看下文。
==图论==
关联矩阵是[[图论 Graph theory]]中经常使用的一种表示方法。
==无向图和有向图 Undirected and directed graphs ==
[[Image:Labeled undirected graph.svg|thumb|250px|一个无向图。]]
在图论中，'''无向图 Undirected Graph'''有两种关联矩阵：'''无向关联矩阵'''和'''有向关联矩阵'''。
无向图的'''无向关联矩阵 unoriented incidence matrix'''（也称为'''简单关联矩阵 incidence matrix'''）''B''，其中 ''n'' 和 ''m'' 分别是顶点和边的数目，如果顶点 v<sub>i</sub> 和边 e<sub>j</sub>是关联的，则结果为1，否则为0。
如图右所示的无向图，它的关联矩阵是一个4行（分别对应4个顶点，1-4）4列（各自对应4条边，e<sub>1</sub>-e<sub>4</sub>）组成的矩阵：
{|
{|
|
|
{| align=left class=wikitable
{| align=left class=wikitable
|-
|-
! !! e<sub>1</sub> !! e<sub>2</sub> !! e<sub>3</sub> !! e<sub>4</sub>
! !! e<sub>1</sub> !! e<sub>2</sub> !! e<sub>3</sub> !! e<sub>4</sub>
!1
!1
!1
!1
|1||1||1||0
|1||1||1||0
|-
|-
!2
!2
|1||0||0||0
|1||0||0||0
|-
|-
!3
!3
|0||1||0||1
|0||1||0||1
|-
|-
!4
!4
|0||0||1||1
|0||0||1||1
|}
|}
| =
| =
|
|
<math>
<math>
\begin{pmatrix}
\begin{pmatrix}
1 & 1 & 1 & 0 \\
1 & 1 & 1 & 0 \\
1 & 0 & 0 & 0 \\
1 & 0 & 0 & 0 \\
0 & 1 & 0 & 1 \\
0 & 1 & 0 & 1 \\
0 & 0 & 1 & 1 \\
0 & 0 & 1 & 1 \\
\end{pmatrix}.
\end{pmatrix}.
</math>
</math>
|}
|}
观察关联矩阵，我们就会发现，因为每条边都有一个顶点连接到每个端点，所以每一列的和总是等于2。
观察关联矩阵，我们就会发现，因为每条边都有一个顶点连接到每个端点，所以每一列的和总是等于2。
'''<font color="#32CD32">有向图的关联矩阵是一个矩阵''B''，其中 ''n'' 和 ''m'' 分别是顶点和边的数目，这样当边 e<sub>j</sub> 离开顶点 v<sub>i</sub>，时为1，当它进入顶点 v<sub>i</sub> ，时为0(许多作者使用相反的符号约定)。</font>The incidence matrix of a directed graph is a matrix B where n and m are the number of vertices and edges respectively, such that if the edge e<sub>j</sub> leaves vertex v<sub>i</sub>, 1 if it enters vertex v<sub>i</sub> and 0 otherwise (many authors use the opposite sign convention).
'''<font color="#32CD32">有向图的关联矩阵是一个矩阵''B''，其中 ''n'' 和 ''m'' 分别是顶点和边的数目，这样当边 e<sub>j</sub> 离开顶点 v<sub>i</sub>，时为1，当它进入顶点 v<sub>i</sub> ，时为0(许多作者使用相反的符号约定)。</font>The incidence matrix of a directed graph is a matrix B where n and m are the number of vertices and edges respectively, such that if the edge e<sub>j</sub> leaves vertex v<sub>i</sub>, 1 if it enters vertex v<sub>i</sub> and 0 otherwise (many authors use the opposite sign convention).
无向图的有向关联矩阵在有向图的意义上是关于图的任任意方向的关联矩阵。也就是说，在边e的列中，e对应的一个顶点的行中有一个1，而另一个顶点的行中有一个−1，所有其他行都有0。定向关联矩阵是唯一的，因为对于任何列取反，列中的条目求逆向对应于反转边的方向。
无向图的有向关联矩阵在有向图的意义上是关于图的任任意方向的关联矩阵。也就是说，在边e的列中，e对应的一个顶点的行中有一个1，而另一个顶点的行中有一个−1，所有其他行都有0。定向关联矩阵是唯一的，因为对于任何列取反，列中的条目求逆向对应于反转边的方向。
图 ''G'' 的无向关联矩阵与其'''<font color="#ff8000">线图 Line Graph</font>''' ''L''(''G'')的邻接矩阵有以下定理关系:
图 ''G'' 的无向关联矩阵与其'''<font color="#ff8000">线图 Line Graph</font>''' ''L''(''G'')的邻接矩阵有以下定理关系:
: <math>A(L(G)) = B(G)^\textsf{T}B(G) - 2I_m.</math>
: <math>A(L(G)) = B(G)^\textsf{T}B(G) - 2I_m.</math>
其中 ''A''(''L''(''G'')) 是 ''G'' 的线图的'''邻接矩阵 Adjacency Matrix'''，''B''(''G'')是关联矩阵，I<sub>m</sub> 是维数为m的'''单位矩阵 Identity Matrix'''。
其中 ''A''(''L''(''G'')) 是 ''G'' 的线图的'''<font color="#ff8000">邻接矩阵 Adjacency Matrix</font>'''，''B''(''G'')是关联矩阵，I<sub>m</sub> 是维数为m的'''<font color="#ff8000">单位矩阵 Identity Matrix</font>'''。
The discrete Laplacian (or Kirchhoff matrix) is obtained from the oriented incidence matrix B(G) by the formula
The discrete Laplacian (or Kirchhoff matrix) is obtained from the oriented incidence matrix B(G) by the formula
离散的'''<font color="#ff8000">拉普拉斯矩阵(或基尔霍夫矩阵) Laplacian (or Kirchhoff Matrix)</font>'''是由定向的关联矩阵''B''(''G'')通过公式得到的
离散的'''拉普拉斯矩阵 Kirchhoff matrix（或基尔霍夫矩阵 Kirchhoff Matrix）'''是由定向的关联矩阵''B''(''G'')通过公式得到的
: <math>B(G) B(G)^\textsf{T}.</math>
: <math>B(G) B(G)^\textsf{T}.</math>
图的'''<font color="#ff8000">圈空间 Cycle Space</font>'''等价于其有向关联矩阵的零空间，可以看作是整数或实数或复数上的矩阵。二元循环空间是有向或无向关联矩阵的零空间，也可以看作是二元域上的矩阵。
图的'''<font color="#ff8000">圈空间 Cycle Space</font>'''等价于其有向关联矩阵的零空间，可以看作是整数或实数或复数上的矩阵。二元循环空间是有向或无向关联矩阵的零空间，也可以看作是二元域上的矩阵。
==Signed and bidirected graphs 有符号双向图 ==
== 有符号双向图 Signed and bidirected graphs==
'''有符号图 Signed Graph'''的关联矩阵是有向关联矩阵的推广。它是任意双向图的关联矩阵，并为给定的有符号图定向。正边的列在对应一个端点的行有1，在对应于另一个端点的行中有 -1，就像普通'''<font color="#ff8000">(无符号)图 Unsigned Graph</font>'''中的边一样。负边的列在两行中都有1或 -1。线图和 Kirchhoff 矩阵性质都能推广到符号图中。
== 多重图 Multigraphs==
'''<font color="#ff8000">多重关联图 Multigraphs</font>'''的定义：多重关联图与循环相对应的定向关联矩阵的列均为零，除了图有符号且循环为负的情况外，该列除其入射顶点行中的±2外均为零。
==Hypergraphs 超图==
==超图 Hypergraphs==
'''<font color="#ff8000">超图 Hypergraphs</font>'''<br>
'''<font color="#ff8000">超图 Hypergraphs</font>'''<br>
因为一般图的边只能有两个顶点（每端一个），所以图的关联矩阵列只能有两个非零项。相比之下，超图却可以有多个顶点指定给一条边。因此，一般的非负整数矩阵才能用来描述超图。
因为一般图的边只能有两个顶点（每端一个），所以图的关联矩阵列只能有两个非零项。相比之下，超图却可以有多个顶点指定给一条边。因此，一般的非负整数矩阵才能用来描述超图。
==关联结构 Incidence structures ==
关联结构 ''C'' 的关联矩阵是一个矩阵''B'' (或其转置) ，其中 ''p'' 和 ''q'' 分别是点和线的数目，如果点 p<sub>i</sub>和线L<sub>j</sub> 是关联的，就为1，否则为0。在这种情况下，关联矩阵也是'''<font color="#ff8000">Levi图 Levi Graph</font>''' 的双邻接矩阵的结构。每个Levi图都有一个超图，反之亦然，因此关联结构的关联矩阵就可以描述一个超图。
关联结构 ''C'' 的关联矩阵是一个矩阵''B'' (或其转置) ，其中 ''p'' 和 ''q'' 分别是点和线的数目，如果点 p<sub>i</sub>和线L<sub>j</sub> 是关联的，就为1，否则为0。在这种情况下，关联矩阵也是'''<font color="#ff8000">Levi图 Levi Graph</font>''' 的双邻接矩阵的结构。每个Levi图都有一个超图，反之亦然，因此关联结构的关联矩阵就可以描述一个超图。
==有限几何 Finite geometries==
举一个有限几何的重要例子。例如，在有限平面中,''X'' 是点的集合,''Y'' 是线的集合。在高维有限几何中，''X'' 仍可以是点的集合，''Y'' 可以是低于整个空间维数的一维子空间（超平面）的集合；或者，更一般地，''X'' 可以是一维子空间 ''d'' 的所有子空间的集合，''Y'' 可以是另一维子空间 ''e'' 的所有子空间的集合，关联度定义也包含在内。
==Polytopes 多面体==
==多面体 Polytopes==
以类似的方式，在多面体中尺寸相差一个的细胞之间的关系可以由关联矩阵表示。<ref>{{citation|first=H.S.M.|last=Coxeter|author-link=Coxeter|title=[[Regular Polytopes (book)|Regular Polytopes]]|year=1973|edition=3rd|origyear=1963|publisher=Dover|isbn=0-486-61480-8|pages=[https://archive.org/details/regularpolytopes0000coxe/page/166 166-167]}}</ref>
== 区组设计/块设计 Block designs==
另一个例子是区块设计。这里 ''X'' 是一个有限的“点”集合，而 ''Y'' 是 ''X'' 的一类子集，称为“块” ，它受设计类型规则的制约。关联矩阵是块设计理论中的一个重要工具。例如，它可以用来证明'''<font color="#ff8000">Fisher不等式 Fisher's inequality</font>'''，一个平衡不完全2- 设计（BIBDs）的基本定理，块的数目至少是点的数目。<ref>{{citation|page=99|first=Herbert John|last=Ryser|title=Combinatorial Mathematics|series=The Carus Mathematical Monographs #14|publisher=The Mathematical Association of America|year=1963}}</ref>将块看作一个集合系统，关联矩阵的常数是不同代表系统的个数（SDRs）。
==参考文献==
==References 参考资料==
{{reflist}}
{{reflist}}
==其他阅读==
* {{citation | last=Diestel | first=Reinhard | title=Graph Theory | series=Graduate Texts in Mathematics | volume=173 | edition=3rd | date=2005 | publisher=Springer-Verlag | isbn=3-540-26183-4}}
* {{citation | last=Diestel | first=Reinhard | title=Graph Theory | series=[[Graduate Texts in Mathematics]] | volume=173 | edition=3rd | date=2005 | publisher=Springer-Verlag | isbn=3-540-26183-4}}
* Jonathan L Gross, Jay Yellen, ''Graph Theory and its applications'', second edition, 2006 (p 97, Incidence Matrices for undirected graphs; p 98, incidence matrices for digraphs)
* Jonathan L Gross, Jay Yellen, ''Graph Theory and its applications'', second edition, 2006 (p 97, Incidence Matrices for undirected graphs; p 98, incidence matrices for digraphs)
[[Category:代数图论]]
[[Category:组合数学]]
[[Category:矩阵]]
[[Category:图形数据结构]]
此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。<br>
由CecileLi初步审校，有的术语如“signed and bi directed graphs”等不确定实在抱歉（详见词条末尾）
于2020.11.19再次审校，该词条专业性较强，修改过程中还是以文本为主，若有遗漏敬请谅解。
本词条由信白初步翻译<br>
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