code: def weisfeiler_lehman_subgraph_hashes(
      G, edge_attr=None, node_attr=None, iterations=3, digest_size=16
  ):
      """
      Return a dictionary of subgraph hashes by node.
  
      Dictionary keys are nodes in `G`, and values are a list of hashes.
      Each hash corresponds to a subgraph rooted at a given node u in `G`.
      Lists of subgraph hashes are sorted in increasing order of depth from
      their root node, with the hash at index i corresponding to a subgraph
      of nodes at most i edges distance from u. Thus, each list will contain
      ``iterations + 1`` elements - a hash for a subgraph at each depth, and
      additionally a hash of the initial node label (or equivalently a
      subgraph of depth 0)
  
      The function iteratively aggregates and hashes neighborhoods of each node.
      This is achieved for each step by replacing for each node its label from
      the previous iteration with its hashed 1-hop neighborhood aggregate.
      The new node label is then appended to a list of node labels for each
      node.
  
      To aggregate neighborhoods at each step for a node $n$, all labels of
      nodes adjacent to $n$ are concatenated. If the `edge_attr` parameter is set,
      labels for each neighboring node are prefixed with the value of this attribute
      along the connecting edge from this neighbor to node $n$. The resulting string
      is then hashed to compress this information into a fixed digest size.
  
      Thus, at the $i$-th iteration, nodes within $i$ hops influence any given
      hashed node label. We can therefore say that at depth $i$ for node $n$
      we have a hash for a subgraph induced by the $2i$-hop neighborhood of $n$.
  
      The output can be used to to create general Weisfeiler-Lehman graph kernels,
      or generate features for graphs or nodes - for example to generate 'words' in
      a graph as seen in the 'graph2vec' algorithm.
      See [1]_ & [2]_ respectively for details.
  
      Hashes are identical for isomorphic subgraphs and there exist strong
      guarantees that non-isomorphic graphs will get different hashes.
      See [1]_ for details.
  
      If no node or edge attributes are provided, the degree of each node
      is used as its initial label.
      Otherwise, node and/or edge labels are used to compute the hash.
  
      Parameters
      ----------
      G: graph
          The graph to be hashed.
          Can have node and/or edge attributes. Can also have no attributes.
      edge_attr: string, default=None
          The key in edge attribute dictionary to be used for hashing.
          If None, edge labels are ignored.
      node_attr: string, default=None
          The key in node attribute dictionary to be used for hashing.
          If None, and no edge_attr given, use the degrees of the nodes as labels.
      iterations: int, default=3
          Number of neighbor aggregations to perform.
          Should be larger for larger graphs.
      digest_size: int, default=16
          Size (in bits) of blake2b hash digest to use for hashing node labels.
          The default size is 16 bits
  
      Returns
      -------
      node_subgraph_hashes : dict
          A dictionary with each key given by a node in G, and each value given
          by the subgraph hashes in order of depth from the key node.
  
      Examples
      --------
      Finding similar nodes in different graphs:
  
      >>> G1 = nx.Graph()
      >>> G1.add_edges_from([
      ...     (1, 2), (2, 3), (2, 4), (3, 5), (4, 6), (5, 7), (6, 7)
      ... ])
      >>> G2 = nx.Graph()
      >>> G2.add_edges_from([
      ...     (1, 3), (2, 3), (1, 6), (1, 5), (4, 6)
      ... ])
      >>> g1_hashes = nx.weisfeiler_lehman_subgraph_hashes(G1, iterations=3, digest_size=8)
      >>> g2_hashes = nx.weisfeiler_lehman_subgraph_hashes(G2, iterations=3, digest_size=8)
  
      Even though G1 and G2 are not isomorphic (they have different numbers of edges),
      the hash sequence of depth 3 for node 1 in G1 and node 5 in G2 are similar:
  
      >>> g1_hashes[1]
      ['a93b64973cfc8897', 'db1b43ae35a1878f', '57872a7d2059c1c0']
      >>> g2_hashes[5]
      ['a93b64973cfc8897', 'db1b43ae35a1878f', '1716d2a4012fa4bc']
  
      The first 2 WL subgraph hashes match. From this we can conclude that it's very
      likely the neighborhood of 4 hops around these nodes are isomorphic: each
      iteration aggregates 1-hop neighborhoods meaning hashes at depth $n$ are influenced
      by every node within $2n$ hops.
  
      However the neighborhood of 6 hops is no longer isomorphic since their 3rd hash does
      not match.
  
      These nodes may be candidates to be classified together since their local topology
      is similar.
  
      Notes
      -----
      To hash the full graph when subgraph hashes are not needed, use
      `weisfeiler_lehman_graph_hash` for efficiency.
  
      Similarity between hashes does not imply similarity between graphs.
  
      References
      ----------
      .. [1] Shervashidze, Nino, Pascal Schweitzer, Erik Jan Van Leeuwen,
         Kurt Mehlhorn, and Karsten M. Borgwardt. Weisfeiler Lehman
         Graph Kernels. Journal of Machine Learning Research. 2011.
         http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf
      .. [2] Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan,
         Lihui Chen, Yang Liu and Shantanu Jaiswa. graph2vec: Learning
         Distributed Representations of Graphs. arXiv. 2017
         https://arxiv.org/pdf/1707.05005.pdf
  
      See also
      --------
      weisfeiler_lehman_graph_hash
      """
  
      def weisfeiler_lehman_step(
          G, labels, node_subgraph_hashes, edge_attr=None
      ):
          """
          Apply neighborhood aggregation to each node
          in the graph.
          Computes a dictionary with labels for each node.
          Appends the new hashed label to the dictionary of subgraph hashes
          originating from and indexed by each node in G
          """
          new_labels = {}
          for node in G.nodes():
              label = _neighborhood_aggregate(
                  G, node, labels, edge_attr=edge_attr
              )
              hashed_label = _hash_label(label, digest_size)
              new_labels[node] = hashed_label
              node_subgraph_hashes[node].append(hashed_label)
          return new_labels
  
      node_labels = _init_node_labels(G, edge_attr, node_attr)
  
      node_subgraph_hashes = defaultdict(list)
      for _ in range(iterations):
          node_labels = weisfeiler_lehman_step(
              G, node_labels, node_subgraph_hashes, edge_attr
          )
  
      return dict(node_subgraph_hashes)
  
file_path: networkx/networkx/algorithms/graph_hashing.py
imports: from collections import Counter, defaultdict
  from hashlib import blake2b
  import networkx
label_desc: Use image node_id 5 for calling a global function with example usage: weisfeiler_lehman_subgraph_hashes(G, edge_attr, node_attr, iterations, digest_size) and returns: dict, new_labels