networkx maximal_matching() does not return maximum matching

I'm learning to use the networkx python module to do some matchings of a bipartite graph. There are two functions in the module that give the maximum cardinality matching of a graph:

1. nx.maximal_matching()
2. nx.bipartite.maxmum_matching()

Note that although with the name of maximal_matching, its doc does state that it "Find a maximal cardinality matching in the graph."

Since my graph is a bipartite one, I assume these 2 would give same results, at least both with the same number of edges. However my code seems to suggest that the nx.maximal_matching() gives the wrong answer: it is possible to have one more edge, as the nx.bipartite.maxmum_matching() suggests.

Below is my working code:

import networkx as nx
from networkx import bipartite    

def plotGraph(graph,ax,title):    
    pos=[(ii[1],ii[0]) for ii in graph.nodes()]
    pos_dict=dict(zip(graph.nodes(),pos))
    nx.draw(graph,pos=pos_dict,ax=ax,with_labels=True)
    ax.set_title(title)
    return   

if __name__=='__main__':    
    #---------------Construct the graph---------------
    g=nx.Graph()
    edges=[
            [(1,0), (0,0)],
            [(1,0), (0,1)],
            [(1,0), (0,2)],
            [(1,1), (0,0)],
            [(1,2), (0,2)],
            [(1,2), (0,5)],
            [(1,3), (0,2)],
            [(1,3), (0,3)],
            [(1,4), (0,3)],
            [(1,5), (0,2)],
            [(1,5), (0,4)],
            [(1,5), (0,6)],
            [(1,6), (0,1)],
            [(1,6), (0,4)],
            [(1,6), (0,6)]
            ]

    for ii in edges:
        g.add_node(ii[0],bipartite=0)
        g.add_node(ii[1],bipartite=1)

    g.add_edges_from(edges)

    #---------------Use maximal_matching---------------
    match=nx.maximal_matching(g)    
    g_match=nx.Graph()
    for ii in match:
        g_match.add_edge(ii[0],ii[1])

    #----------Use bipartite.maximum_matching----------
    match2=bipartite.maximum_matching(g)    
    g_match2=nx.Graph()
    for kk,vv in match2.items():
        g_match2.add_edge(kk,vv)

    #-----------------------Plot-----------------------
    import matplotlib.pyplot as plt
    fig=plt.figure(figsize=(10,8))

    ax1=fig.add_subplot(2,2,1)
    plotGraph(g,ax1,'Graph')

    ax2=fig.add_subplot(2,2,2)
    plotGraph(g_match,ax2,'nx.maximal_matching()')

    ax3=fig.add_subplot(2,2,3)
    plotGraph(g_match2,ax3,'bipartite.maximum_matching()')

    plt.show()

And here is the generated plot. As is shown subplot-2 has 6 edges while 3 has 7. Is this a bug in the networkx's implementation or I'm doing anything wrong here?

PS: my networkx is version 1.11