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→Clustering coefficient
Let <math>G = (V,E)</math> be an undirected simple graph (i.e., a graph having no self-loops or multiple edges) with V the set of vertices and E the set of edges. Also, let <math>N = |V|</math> and <math>M = |E|</math> denote the number of vertices and edges in G, respectively, and let <math>d_i</math> be the degree of vertex i.
Let <math>G = (V,E)</math> be an undirected simple graph (i.e., a graph having no self-loops or multiple edges) with V the set of vertices and E the set of edges. Also, let <math>N = |V|</math> and <math>M = |E|</math> denote the number of vertices and edges in G, respectively, and let <math>d_i</math> be the degree of vertex i.
令<math>G =（V，E）</math>是无向的简单图（即没有自环或多个边的图），其中V为顶点集，E为边集。 另外，令<math>N = |V|</math>和<math>M = |E|</math>分别表示G中顶点和边的数量，并令<math>d_i</math> 是顶点的度i。
令<math>G =（V，E）</math>是无向的'''<font color="#FF8000">简单图 Simple Graph </font>'''（即没有自环或多个边的图），其中V为顶点集，E为边集。 另外，令<math>N = |V|</math>和<math>M = |E|</math>分别表示G中顶点和边的数量，并令<math>d_i</math> 是顶点的度i。
Now, for a vertex <math>i</math> with <math>d_i \ge 2</math>, the clustering coefficient <math>c(i)</math> of vertex <math>i</math> is the fraction of triples for vertex <math>i</math> that are closed, and can be measured as <math>\frac{\delta (i)}{\tau (i)}</math>. Thus, the clustering coefficient <math>C(G)</math> of graph <math>G</math> is given by <math>C(G) = \frac {1}{N_2} \sum_{i \in V, d_i \ge 2} c(i)</math>, where <math>N_2</math> is the number of nodes with degree at least 2.
Now, for a vertex <math>i</math> with <math>d_i \ge 2</math>, the clustering coefficient <math>c(i)</math> of vertex <math>i</math> is the fraction of triples for vertex <math>i</math> that are closed, and can be measured as <math>\frac{\delta (i)}{\tau (i)}</math>. Thus, the clustering coefficient <math>C(G)</math> of graph <math>G</math> is given by <math>C(G) = \frac {1}{N_2} \sum_{i \in V, d_i \ge 2} c(i)</math>, where <math>N_2</math> is the number of nodes with degree at least 2.
现在，对于具有<math> d_i \ ge 2 </ math>的顶点<math> i </ math>，顶点<math> i </ math>的聚类系数<math> c（i）</ math> 是封闭的顶点<math> i </ math>的三元组分数，可以测量为<math> \ frac {\ delta（i）} {\ tau（i）} </ math>。 因此，图<math> G </ math>的聚类系数<math> C（G）</ math>由<math> C（G）= \ frac {1} {N_2} \ sum_ {i \ 在V中，d_i \ ge 2} c（i）</ math>，其中<math> N_2 </ math>是度数至少为2的节点数。
现在，对于具有<math>d_i\ge 2</math>的顶点<math>i</ math>，顶点<math>i</math>的聚类系数<math>c（i)</math> 是封闭的顶点<math>i</math>的三元组分数，可以测量为<math>\frac{\delta（i）}{\tau（i）}</math>。 因此，图<math> G </math>的聚类系数<math> C（G）</math>由<math> C（G）=\frac {1}{N_2} \sum_{i \in V,d_i \ge 2}c（i）</math>，其中<math>N_2</math>是度数至少为2的节点数。
==Transitivity==
传递性<br>
Another measure for the presence of triadic closure is transitivity, defined as <math>T(G) = \frac{3\delta (G)}{\tau (G)}</math>.
Another measure for the presence of triadic closure is transitivity, defined as <math>T(G) = \frac{3\delta (G)}{\tau (G)}</math>.
Another measure for the presence of triadic closure is transitivity, defined as <math>T(G) = \frac{3\delta (G)}{\tau (G)}</math>.
Another measure for the presence of triadic closure is transitivity, defined as <math>T(G) = \frac{3\delta (G)}{\tau (G)}</math>.
关于三元闭包的另一种度量是可传递性，定义为<math>T（G）= \frac{3\delta（G）}{\tau（G）}</math>。
==Causes and effects==
==Causes and effects==