code: def calc_eigenvector(graph_file):
      dataset_path = graph_file.get_path()
      G = graph_file.get_graph(
          download=True,
          create_using=cugraph.Graph(directed=True),
          ignore_weights=True,
      )
  
      k_df = cugraph.eigenvector_centrality(G, max_iter=1000)
      k_df = k_df.sort_values("vertex").reset_index(drop=True)
  
      NM = utils.read_csv_for_nx(dataset_path)
      Gnx = nx.from_pandas_edgelist(
          NM, create_using=nx.DiGraph(), source="0", target="1"
      )
      nk = nx.eigenvector_centrality(Gnx)
      pdf = [nk[k] for k in sorted(nk.keys())]
      k_df["nx_eigen"] = pdf
      k_df = k_df.rename(
          columns={"eigenvector_centrality": "cu_eigen"}, copy=False
      )
      return k_df
  
file_path: cugraph/python/cugraph/cugraph/tests/centrality/test_eigenvector_centrality.py
imports: import gc
  import pytest
  import networkx
  import cugraph
  from cugraph.testing import utils, UNDIRECTED_DATASETS, DEFAULT_DATASETS
  from cugraph.datasets import toy_graph, karate
label_desc: Use image node_id 3 for calling a global function with example usage: calc_eigenvector(graph_file) and returns: k_df