(
        self,
        vertices=None,
        directed=True,
        damping=0.85,
        weights=None,
        arpack_options=None,
        implementation="prpack",
        niter=1000,
        eps=0.001,
    )
return self.personalized_pagerank(
            vertices,
            directed,
            damping,
            None,
            None,
            weights,
            arpack_options,
            implementation,
            niter,
            eps,
        )
Calculates the PageRank values of a graph.

        @param vertices: the indices of the vertices being queried.
          C{None} means all of the vertices.
        @param directed: whether to consider directed paths.
        @param damping: the damping factor. M{1-damping} is the PageRank value
          for nodes with no incoming links. It is also the probability of
          resetting the random walk to a uniform distribution in each step.
        @param weights: edge weights to be used. Can be a sequence or iterable
          or even an edge attribute name.
        @param arpack_options: an L{ARPACKOptions} object used to fine-tune
          the ARPACK eigenvector calculation. If omitted, the module-level
          variable called C{arpack_options} is used. This argument is
          ignored if not the ARPACK implementation is used, see the
          I{implementation} argument.
        @param implementation: which implementation to use to solve the
          PageRank eigenproblem. Possible values are:
            - C{"prpack"}: use the PRPACK library. This is a new
              implementation in igraph 0.7
            - C{"arpack"}: use the ARPACK library. This implementation
              was used from version 0.5, until version 0.7.
            - C{"power"}: use a simple power method. This is the
              implementation that was used before igraph version 0.5.
        @param niter: The number of iterations to use in the power method
          implementation. It is ignored in the other implementations
        @param eps: The power method implementation will consider the
          calculation as complete if the difference of PageRank values between
          iterations change less than this value for every node. It is
          ignored by the other implementations.
        @return: a list with the Google PageRank values of the specified
          vertices.
Calculates the PageRank values of a graph.
Calculates the PageRank values of a graph .
def pagerank(
        self,
        vertices=None,
        directed=True,
        damping=0.85,
        weights=None,
        arpack_options=None,
        implementation="prpack",
        niter=1000,
        eps=0.001,
    ):
        """Calculates the PageRank values of a graph.

        @param vertices: the indices of the vertices being queried.
          C{None} means all of the vertices.
        @param directed: whether to consider directed paths.
        @param damping: the damping factor. M{1-damping} is the PageRank value
          for nodes with no incoming links. It is also the probability of
          resetting the random walk to a uniform distribution in each step.
        @param weights: edge weights to be used. Can be a sequence or iterable
          or even an edge attribute name.
        @param arpack_options: an L{ARPACKOptions} object used to fine-tune
          the ARPACK eigenvector calculation. If omitted, the module-level
          variable called C{arpack_options} is used. This argument is
          ignored if not the ARPACK implementation is used, see the
          I{implementation} argument.
        @param implementation: which implementation to use to solve the
          PageRank eigenproblem. Possible values are:
            - C{"prpack"}: use the PRPACK library. This is a new
              implementation in igraph 0.7
            - C{"arpack"}: use the ARPACK library. This implementation
              was used from version 0.5, until version 0.7.
            - C{"power"}: use a simple power method. This is the
              implementation that was used before igraph version 0.5.
        @param niter: The number of iterations to use in the power method
          implementation. It is ignored in the other implementations
        @param eps: The power method implementation will consider the
          calculation as complete if the difference of PageRank values between
          iterations change less than this value for every node. It is
          ignored by the other implementations.
        @return: a list with the Google PageRank values of the specified
          vertices."""
        if arpack_options is None:
            arpack_options = default_arpack_options
        return self.personalized_pagerank(
            vertices,
            directed,
            damping,
            None,
            None,
            weights,
            arpack_options,
            implementation,
            niter,
            eps,
        )
def pagerank ( self , vertices = None , directed = True , damping = 0.85 , weights = None , arpack_options = None , implementation = "prpack" , niter = 1000 , eps = 0.001 , ) : if arpack_options is None : arpack_options = default_arpack_options return self . personalized_pagerank ( vertices , directed , damping , None , None , weights , arpack_options , implementation , niter , eps , )