calculate hop count in a graph using python

The following program finds out the hop count of the nodes in a connected network. Hop Count means the number of point to point connections in a graph of network. For example, in the graph shown below the hop count from node a to node b is one, node b to node c is two, etc.

This program assumes the graph given above.
usage: getHopCount(dictNeigh, node1,node2)
Where: 
dictNeigh: dictionary of list of neighbor of each node in the network
node1 and node2 are the nodes for which hop count is calculated by the program
Exception: if the nodes are not connected then it returns Zero

import copy dictNeigh = dict() # dictNeigh defines the neighbors dictNeigh['a'] = ['b','c','d'] # neighbors of 'a' dictNeigh['b'] = ['a']# neighbors of 'b' dictNeigh['c'] = ['a','d'] dictNeigh['d'] = ['a','c','e'] dictNeigh['e'] = ['d','f'] dictNeigh['f'] = ['e','g','h'] dictNeigh['g'] = ['f'] dictNeigh['h'] = ['f'] def getHopCount(dictNeigh, node1, node2): hopCount = 0 listNodes = copy.deepcopy(dictNeigh[node1]) listNodeParsed = copy.deepcopy(dictNeigh[node1]) listNodeParsed.append(node1) # put self node as parsed listPrevNodes = copy.deepcopy(dictNeigh[node1]) for i in range (0, len(dictNeigh.keys())): if node2 in listNodes: hopCount = i +1 return hopCount else: for node in listPrevNodes: # copy all 1 hop neighbor of immediate neighbor listNodes = copy.deepcopy(list( set(listNodes) | set(dictNeigh[node]) )) listNodes = copy.deepcopy(list(set(listNodes) - set(listNodeParsed))) # remove the nodes already seen listNodeParsed = copy.deepcopy(list(set(listNodeParsed) | set(listNodes))) listPrevNodes = copy.deepcopy(listNodes) return 0 print 'Hop:' +str( getHopCount(dictNeigh, 'b','d'))