Betweenness centrality formulaFormalization of the shortest path algorithm to a linear programShortest path that passes through specific node(s)Betweenness centrality and least average shortest pathSort graph nodes by densityFind hamilton cycle in a directed graph reduced to sat problemWhat does a ball of center v and radius r with at most r hops away mean?Polynomial LP-based algorithm for cost minimization of DAG weights modificationDjikstra's shortest path vs Brandes algorithm for betweeness centralityFind all the cumulative sums in a DAGMinimum path cover— Disjointed paths with minimum total number of edges

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Betweenness centrality formula


Formalization of the shortest path algorithm to a linear programShortest path that passes through specific node(s)Betweenness centrality and least average shortest pathSort graph nodes by densityFind hamilton cycle in a directed graph reduced to sat problemWhat does a ball of center v and radius r with at most r hops away mean?Polynomial LP-based algorithm for cost minimization of DAG weights modificationDjikstra's shortest path vs Brandes algorithm for betweeness centralityFind all the cumulative sums in a DAGMinimum path cover— Disjointed paths with minimum total number of edges













3












$begingroup$



Betweenness centrality is defined as the number of shortest paths that go through a node in the graph.The formula is:



$$sum_s neq v neq t fracsigma_st(v)sigma_st$$



Where $sigma_st$ is the total number of shortest paths from node $s$ to node $t$ and $sigma _st(v)$ is the number of those paths that pass through $v$.




However it doesn't seem to me that the formula calculates what is defined. Why do we divide by the total number of shortest paths between $s$ and $t$ each time? Shouldn't we just divide by $2$ to compensate the fact that $s$ and $t$ will appear twice in different orders?










share|cite









$endgroup$
















    3












    $begingroup$



    Betweenness centrality is defined as the number of shortest paths that go through a node in the graph.The formula is:



    $$sum_s neq v neq t fracsigma_st(v)sigma_st$$



    Where $sigma_st$ is the total number of shortest paths from node $s$ to node $t$ and $sigma _st(v)$ is the number of those paths that pass through $v$.




    However it doesn't seem to me that the formula calculates what is defined. Why do we divide by the total number of shortest paths between $s$ and $t$ each time? Shouldn't we just divide by $2$ to compensate the fact that $s$ and $t$ will appear twice in different orders?










    share|cite









    $endgroup$














      3












      3








      3





      $begingroup$



      Betweenness centrality is defined as the number of shortest paths that go through a node in the graph.The formula is:



      $$sum_s neq v neq t fracsigma_st(v)sigma_st$$



      Where $sigma_st$ is the total number of shortest paths from node $s$ to node $t$ and $sigma _st(v)$ is the number of those paths that pass through $v$.




      However it doesn't seem to me that the formula calculates what is defined. Why do we divide by the total number of shortest paths between $s$ and $t$ each time? Shouldn't we just divide by $2$ to compensate the fact that $s$ and $t$ will appear twice in different orders?










      share|cite









      $endgroup$





      Betweenness centrality is defined as the number of shortest paths that go through a node in the graph.The formula is:



      $$sum_s neq v neq t fracsigma_st(v)sigma_st$$



      Where $sigma_st$ is the total number of shortest paths from node $s$ to node $t$ and $sigma _st(v)$ is the number of those paths that pass through $v$.




      However it doesn't seem to me that the formula calculates what is defined. Why do we divide by the total number of shortest paths between $s$ and $t$ each time? Shouldn't we just divide by $2$ to compensate the fact that $s$ and $t$ will appear twice in different orders?







      graph-theory






      share|cite













      share|cite











      share|cite




      share|cite










      asked 3 hours ago









      ElooEloo

      515




      515




















          2 Answers
          2






          active

          oldest

          votes


















          2












          $begingroup$


          However it doesn't seem to me that the formula calculates what is defined.




          The formula is right. The betweenness centrality is a value in an interval $[0, ldots, 1]$. Thus, if the betweenness centrality of node $v$ is equal to $1$, then all shortest paths between two nodes of this graph pass through $v$. I will explain the correctness of this summation below.





          Why do we divide by the total number of shortest paths between s and t each time?




          You are developing a summation of the percentages. This is needed to ensure that this sum will never exceed $1$. Suppose that you have $m$ different $s$-$t$ pairs of vertices in your graph. Thus, $sigma_st = m$ and your summation goes through all $m$ $s$-$t$ pairs.

          One can note that the term $sigma_st(v)$ on this equation is binary (the shortest $s$-$t$ path passes through $v$ or not). Thus, if all $s$-$t$ paths go through $v$, you will have $m cdot frac1m = 1$.





          Shouldn't we just divide by 2 to compensate the fact that s and t will appear twice in different orders?




          Indirectly, you're right. This formula measures the percentage of the shortest $s$-$t$ paths that pass through node $v$. In fact, a simple optimization of this algorithm for undirected graphs is to consider only $s$-$t$ paths where $s < t$. However, you can't divide it by $2$.




          Curiosity: The only graph topology who has a node with betweenness centrality equal to $1$ is a star graph, like the examples shown in the figure below.



          Examples of star graphs






          share|cite|improve this answer











          $endgroup$












          • $begingroup$
            It looks like you confuse betweenness centrality of a node in a graph with the betweenness of a node between two nodes. The former might be greater than 1 before normalization.
            $endgroup$
            – Apass.Jack
            32 mins ago



















          2












          $begingroup$

          Suppose we want to quantify the extent to which $v$ is between $s$ and $t$. There could be a few ways.



          One way to describe that extent is the probability of passing through $v$ if we want to reach from $s$ to $t$ by a randomly-selected shortest path. Assume each shortest is selected with equal probability, we will get $fracsigma_st(v)sigma_st$, where $sigma_st$ is the total number of shortest paths from node $s$ to node $t$ and $sigma _st(v)$ is the number of those paths that pass through $v$.



          Assigning the same weight to each pair of starting vertex and destination vertex, we can see that $sum_s neq v neq t fracsigma_st(v)sigma_st$ measure the extent in which $v$ is the center of betweenness.



          enter image description hereThe graph is created by https://graphonline.ru/



          If you use $fracsigma_st(v)2$ to quantify the extent to which $v$ is between $s$ and $t$, there is no problem if you just care about $v$ considering $s$ and $t$ as fixed. However, take a look at the above graph.



          • How much is $v_3$ between $v_0$ and $v_4$? There are 3 shortest paths from $v_0$ to $v_4$, 2 of which pass through $v_3$. We get $fracsigma_v_0v_4(V_3)2 = 2/2=1$.

          • How much is $v_5$ between $v_0$ and $v_6$? There is only 1 shortest path from $v_0$ to $v_6$, which passes $v_5$. We get $fracsigma_v_0v_6(v_5)2 = 1/2=0.5$.

          Since $1>0.5$, we would like to conclude that $v_3$ is more between $v_0$ and $v_4$ than $v_5$ is between $v_0$ and $v_6$. However, we can go to $v_4$ without passing $v_3$ while we must pass $v_5$ to reach $v_6$ by shortest path. So $v_3$ should be less between $v_0$ and $v_4$ than $v_5$ is between $v_0$ and $v_6$. This simple example show that dividing by 2 is not the right way to normalize the measurement.








          share|cite|improve this answer









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            $begingroup$


            However it doesn't seem to me that the formula calculates what is defined.




            The formula is right. The betweenness centrality is a value in an interval $[0, ldots, 1]$. Thus, if the betweenness centrality of node $v$ is equal to $1$, then all shortest paths between two nodes of this graph pass through $v$. I will explain the correctness of this summation below.





            Why do we divide by the total number of shortest paths between s and t each time?




            You are developing a summation of the percentages. This is needed to ensure that this sum will never exceed $1$. Suppose that you have $m$ different $s$-$t$ pairs of vertices in your graph. Thus, $sigma_st = m$ and your summation goes through all $m$ $s$-$t$ pairs.

            One can note that the term $sigma_st(v)$ on this equation is binary (the shortest $s$-$t$ path passes through $v$ or not). Thus, if all $s$-$t$ paths go through $v$, you will have $m cdot frac1m = 1$.





            Shouldn't we just divide by 2 to compensate the fact that s and t will appear twice in different orders?




            Indirectly, you're right. This formula measures the percentage of the shortest $s$-$t$ paths that pass through node $v$. In fact, a simple optimization of this algorithm for undirected graphs is to consider only $s$-$t$ paths where $s < t$. However, you can't divide it by $2$.




            Curiosity: The only graph topology who has a node with betweenness centrality equal to $1$ is a star graph, like the examples shown in the figure below.



            Examples of star graphs






            share|cite|improve this answer











            $endgroup$












            • $begingroup$
              It looks like you confuse betweenness centrality of a node in a graph with the betweenness of a node between two nodes. The former might be greater than 1 before normalization.
              $endgroup$
              – Apass.Jack
              32 mins ago
















            2












            $begingroup$


            However it doesn't seem to me that the formula calculates what is defined.




            The formula is right. The betweenness centrality is a value in an interval $[0, ldots, 1]$. Thus, if the betweenness centrality of node $v$ is equal to $1$, then all shortest paths between two nodes of this graph pass through $v$. I will explain the correctness of this summation below.





            Why do we divide by the total number of shortest paths between s and t each time?




            You are developing a summation of the percentages. This is needed to ensure that this sum will never exceed $1$. Suppose that you have $m$ different $s$-$t$ pairs of vertices in your graph. Thus, $sigma_st = m$ and your summation goes through all $m$ $s$-$t$ pairs.

            One can note that the term $sigma_st(v)$ on this equation is binary (the shortest $s$-$t$ path passes through $v$ or not). Thus, if all $s$-$t$ paths go through $v$, you will have $m cdot frac1m = 1$.





            Shouldn't we just divide by 2 to compensate the fact that s and t will appear twice in different orders?




            Indirectly, you're right. This formula measures the percentage of the shortest $s$-$t$ paths that pass through node $v$. In fact, a simple optimization of this algorithm for undirected graphs is to consider only $s$-$t$ paths where $s < t$. However, you can't divide it by $2$.




            Curiosity: The only graph topology who has a node with betweenness centrality equal to $1$ is a star graph, like the examples shown in the figure below.



            Examples of star graphs






            share|cite|improve this answer











            $endgroup$












            • $begingroup$
              It looks like you confuse betweenness centrality of a node in a graph with the betweenness of a node between two nodes. The former might be greater than 1 before normalization.
              $endgroup$
              – Apass.Jack
              32 mins ago














            2












            2








            2





            $begingroup$


            However it doesn't seem to me that the formula calculates what is defined.




            The formula is right. The betweenness centrality is a value in an interval $[0, ldots, 1]$. Thus, if the betweenness centrality of node $v$ is equal to $1$, then all shortest paths between two nodes of this graph pass through $v$. I will explain the correctness of this summation below.





            Why do we divide by the total number of shortest paths between s and t each time?




            You are developing a summation of the percentages. This is needed to ensure that this sum will never exceed $1$. Suppose that you have $m$ different $s$-$t$ pairs of vertices in your graph. Thus, $sigma_st = m$ and your summation goes through all $m$ $s$-$t$ pairs.

            One can note that the term $sigma_st(v)$ on this equation is binary (the shortest $s$-$t$ path passes through $v$ or not). Thus, if all $s$-$t$ paths go through $v$, you will have $m cdot frac1m = 1$.





            Shouldn't we just divide by 2 to compensate the fact that s and t will appear twice in different orders?




            Indirectly, you're right. This formula measures the percentage of the shortest $s$-$t$ paths that pass through node $v$. In fact, a simple optimization of this algorithm for undirected graphs is to consider only $s$-$t$ paths where $s < t$. However, you can't divide it by $2$.




            Curiosity: The only graph topology who has a node with betweenness centrality equal to $1$ is a star graph, like the examples shown in the figure below.



            Examples of star graphs






            share|cite|improve this answer











            $endgroup$




            However it doesn't seem to me that the formula calculates what is defined.




            The formula is right. The betweenness centrality is a value in an interval $[0, ldots, 1]$. Thus, if the betweenness centrality of node $v$ is equal to $1$, then all shortest paths between two nodes of this graph pass through $v$. I will explain the correctness of this summation below.





            Why do we divide by the total number of shortest paths between s and t each time?




            You are developing a summation of the percentages. This is needed to ensure that this sum will never exceed $1$. Suppose that you have $m$ different $s$-$t$ pairs of vertices in your graph. Thus, $sigma_st = m$ and your summation goes through all $m$ $s$-$t$ pairs.

            One can note that the term $sigma_st(v)$ on this equation is binary (the shortest $s$-$t$ path passes through $v$ or not). Thus, if all $s$-$t$ paths go through $v$, you will have $m cdot frac1m = 1$.





            Shouldn't we just divide by 2 to compensate the fact that s and t will appear twice in different orders?




            Indirectly, you're right. This formula measures the percentage of the shortest $s$-$t$ paths that pass through node $v$. In fact, a simple optimization of this algorithm for undirected graphs is to consider only $s$-$t$ paths where $s < t$. However, you can't divide it by $2$.




            Curiosity: The only graph topology who has a node with betweenness centrality equal to $1$ is a star graph, like the examples shown in the figure below.



            Examples of star graphs







            share|cite|improve this answer














            share|cite|improve this answer



            share|cite|improve this answer








            edited 1 hour ago

























            answered 2 hours ago









            Iago CarvalhoIago Carvalho

            17017




            17017











            • $begingroup$
              It looks like you confuse betweenness centrality of a node in a graph with the betweenness of a node between two nodes. The former might be greater than 1 before normalization.
              $endgroup$
              – Apass.Jack
              32 mins ago

















            • $begingroup$
              It looks like you confuse betweenness centrality of a node in a graph with the betweenness of a node between two nodes. The former might be greater than 1 before normalization.
              $endgroup$
              – Apass.Jack
              32 mins ago
















            $begingroup$
            It looks like you confuse betweenness centrality of a node in a graph with the betweenness of a node between two nodes. The former might be greater than 1 before normalization.
            $endgroup$
            – Apass.Jack
            32 mins ago





            $begingroup$
            It looks like you confuse betweenness centrality of a node in a graph with the betweenness of a node between two nodes. The former might be greater than 1 before normalization.
            $endgroup$
            – Apass.Jack
            32 mins ago












            2












            $begingroup$

            Suppose we want to quantify the extent to which $v$ is between $s$ and $t$. There could be a few ways.



            One way to describe that extent is the probability of passing through $v$ if we want to reach from $s$ to $t$ by a randomly-selected shortest path. Assume each shortest is selected with equal probability, we will get $fracsigma_st(v)sigma_st$, where $sigma_st$ is the total number of shortest paths from node $s$ to node $t$ and $sigma _st(v)$ is the number of those paths that pass through $v$.



            Assigning the same weight to each pair of starting vertex and destination vertex, we can see that $sum_s neq v neq t fracsigma_st(v)sigma_st$ measure the extent in which $v$ is the center of betweenness.



            enter image description hereThe graph is created by https://graphonline.ru/



            If you use $fracsigma_st(v)2$ to quantify the extent to which $v$ is between $s$ and $t$, there is no problem if you just care about $v$ considering $s$ and $t$ as fixed. However, take a look at the above graph.



            • How much is $v_3$ between $v_0$ and $v_4$? There are 3 shortest paths from $v_0$ to $v_4$, 2 of which pass through $v_3$. We get $fracsigma_v_0v_4(V_3)2 = 2/2=1$.

            • How much is $v_5$ between $v_0$ and $v_6$? There is only 1 shortest path from $v_0$ to $v_6$, which passes $v_5$. We get $fracsigma_v_0v_6(v_5)2 = 1/2=0.5$.

            Since $1>0.5$, we would like to conclude that $v_3$ is more between $v_0$ and $v_4$ than $v_5$ is between $v_0$ and $v_6$. However, we can go to $v_4$ without passing $v_3$ while we must pass $v_5$ to reach $v_6$ by shortest path. So $v_3$ should be less between $v_0$ and $v_4$ than $v_5$ is between $v_0$ and $v_6$. This simple example show that dividing by 2 is not the right way to normalize the measurement.








            share|cite|improve this answer









            $endgroup$

















              2












              $begingroup$

              Suppose we want to quantify the extent to which $v$ is between $s$ and $t$. There could be a few ways.



              One way to describe that extent is the probability of passing through $v$ if we want to reach from $s$ to $t$ by a randomly-selected shortest path. Assume each shortest is selected with equal probability, we will get $fracsigma_st(v)sigma_st$, where $sigma_st$ is the total number of shortest paths from node $s$ to node $t$ and $sigma _st(v)$ is the number of those paths that pass through $v$.



              Assigning the same weight to each pair of starting vertex and destination vertex, we can see that $sum_s neq v neq t fracsigma_st(v)sigma_st$ measure the extent in which $v$ is the center of betweenness.



              enter image description hereThe graph is created by https://graphonline.ru/



              If you use $fracsigma_st(v)2$ to quantify the extent to which $v$ is between $s$ and $t$, there is no problem if you just care about $v$ considering $s$ and $t$ as fixed. However, take a look at the above graph.



              • How much is $v_3$ between $v_0$ and $v_4$? There are 3 shortest paths from $v_0$ to $v_4$, 2 of which pass through $v_3$. We get $fracsigma_v_0v_4(V_3)2 = 2/2=1$.

              • How much is $v_5$ between $v_0$ and $v_6$? There is only 1 shortest path from $v_0$ to $v_6$, which passes $v_5$. We get $fracsigma_v_0v_6(v_5)2 = 1/2=0.5$.

              Since $1>0.5$, we would like to conclude that $v_3$ is more between $v_0$ and $v_4$ than $v_5$ is between $v_0$ and $v_6$. However, we can go to $v_4$ without passing $v_3$ while we must pass $v_5$ to reach $v_6$ by shortest path. So $v_3$ should be less between $v_0$ and $v_4$ than $v_5$ is between $v_0$ and $v_6$. This simple example show that dividing by 2 is not the right way to normalize the measurement.








              share|cite|improve this answer









              $endgroup$















                2












                2








                2





                $begingroup$

                Suppose we want to quantify the extent to which $v$ is between $s$ and $t$. There could be a few ways.



                One way to describe that extent is the probability of passing through $v$ if we want to reach from $s$ to $t$ by a randomly-selected shortest path. Assume each shortest is selected with equal probability, we will get $fracsigma_st(v)sigma_st$, where $sigma_st$ is the total number of shortest paths from node $s$ to node $t$ and $sigma _st(v)$ is the number of those paths that pass through $v$.



                Assigning the same weight to each pair of starting vertex and destination vertex, we can see that $sum_s neq v neq t fracsigma_st(v)sigma_st$ measure the extent in which $v$ is the center of betweenness.



                enter image description hereThe graph is created by https://graphonline.ru/



                If you use $fracsigma_st(v)2$ to quantify the extent to which $v$ is between $s$ and $t$, there is no problem if you just care about $v$ considering $s$ and $t$ as fixed. However, take a look at the above graph.



                • How much is $v_3$ between $v_0$ and $v_4$? There are 3 shortest paths from $v_0$ to $v_4$, 2 of which pass through $v_3$. We get $fracsigma_v_0v_4(V_3)2 = 2/2=1$.

                • How much is $v_5$ between $v_0$ and $v_6$? There is only 1 shortest path from $v_0$ to $v_6$, which passes $v_5$. We get $fracsigma_v_0v_6(v_5)2 = 1/2=0.5$.

                Since $1>0.5$, we would like to conclude that $v_3$ is more between $v_0$ and $v_4$ than $v_5$ is between $v_0$ and $v_6$. However, we can go to $v_4$ without passing $v_3$ while we must pass $v_5$ to reach $v_6$ by shortest path. So $v_3$ should be less between $v_0$ and $v_4$ than $v_5$ is between $v_0$ and $v_6$. This simple example show that dividing by 2 is not the right way to normalize the measurement.








                share|cite|improve this answer









                $endgroup$



                Suppose we want to quantify the extent to which $v$ is between $s$ and $t$. There could be a few ways.



                One way to describe that extent is the probability of passing through $v$ if we want to reach from $s$ to $t$ by a randomly-selected shortest path. Assume each shortest is selected with equal probability, we will get $fracsigma_st(v)sigma_st$, where $sigma_st$ is the total number of shortest paths from node $s$ to node $t$ and $sigma _st(v)$ is the number of those paths that pass through $v$.



                Assigning the same weight to each pair of starting vertex and destination vertex, we can see that $sum_s neq v neq t fracsigma_st(v)sigma_st$ measure the extent in which $v$ is the center of betweenness.



                enter image description hereThe graph is created by https://graphonline.ru/



                If you use $fracsigma_st(v)2$ to quantify the extent to which $v$ is between $s$ and $t$, there is no problem if you just care about $v$ considering $s$ and $t$ as fixed. However, take a look at the above graph.



                • How much is $v_3$ between $v_0$ and $v_4$? There are 3 shortest paths from $v_0$ to $v_4$, 2 of which pass through $v_3$. We get $fracsigma_v_0v_4(V_3)2 = 2/2=1$.

                • How much is $v_5$ between $v_0$ and $v_6$? There is only 1 shortest path from $v_0$ to $v_6$, which passes $v_5$. We get $fracsigma_v_0v_6(v_5)2 = 1/2=0.5$.

                Since $1>0.5$, we would like to conclude that $v_3$ is more between $v_0$ and $v_4$ than $v_5$ is between $v_0$ and $v_6$. However, we can go to $v_4$ without passing $v_3$ while we must pass $v_5$ to reach $v_6$ by shortest path. So $v_3$ should be less between $v_0$ and $v_4$ than $v_5$ is between $v_0$ and $v_6$. This simple example show that dividing by 2 is not the right way to normalize the measurement.









                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered 38 mins ago









                Apass.JackApass.Jack

                14.6k1940




                14.6k1940



























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