draw_networkx_edges(G, pos, edgelist=None, width=1.0, edge_color='k', style='solid', alpha=None, arrowstyle=None, arrowsize=10, edge_cmap=None, edge_vmin=None, edge_vmax=None, ax=None, arrows=None, label=None, node_size=300, nodelist=None, node_shape='o', connectionstyle='arc3', min_source_margin=0, min_target_margin=0, hide_ticks=True)[source]#

Draw the edges of the graph G.

This draws only the edges of the graph G.


A networkx graph


A dictionary with nodes as keys and positions as values. Positions should be sequences of length 2.

edgelistcollection of edge tuples (default=G.edges())

Draw only specified edges

widthfloat or array of floats (default=1.0)

Line width of edges

edge_colorcolor or array of colors (default=’k’)

Edge color. Can be a single color or a sequence of colors with the same length as edgelist. Color can be string or rgb (or rgba) tuple of floats from 0-1. If numeric values are specified they will be mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.

stylestring or array of strings (default=’solid’)

Edge line style e.g.: ‘-’, ‘–’, ‘-.’, ‘:’ or words like ‘solid’ or ‘dashed’. Can be a single style or a sequence of styles with the same length as the edge list. If less styles than edges are given the styles will cycle. If more styles than edges are given the styles will be used sequentially and not be exhausted. Also, (offset, onoffseq) tuples can be used as style instead of a strings. (See matplotlib.patches.FancyArrowPatch: linestyle)

alphafloat or array of floats (default=None)

The edge transparency. This can be a single alpha value, in which case it will be applied to all specified edges. Otherwise, if it is an array, the elements of alpha will be applied to the colors in order (cycling through alpha multiple times if necessary).

edge_cmapMatplotlib colormap, optional

Colormap for mapping intensities of edges

edge_vmin,edge_vmaxfloats, optional

Minimum and maximum for edge colormap scaling

axMatplotlib Axes object, optional

Draw the graph in the specified Matplotlib axes.

arrowsbool or None, optional (default=None)

If None, directed graphs draw arrowheads with FancyArrowPatch, while undirected graphs draw edges via LineCollection for speed. If True, draw arrowheads with FancyArrowPatches (bendable and stylish). If False, draw edges using LineCollection (linear and fast).

Note: Arrowheads will be the same color as edges.

arrowstylestr (default=’-|>’ for directed graphs)

For directed graphs and arrows==True defaults to ‘-|>’, For undirected graphs default to ‘-‘.

See matplotlib.patches.ArrowStyle for more options.

arrowsizeint (default=10)

For directed graphs, choose the size of the arrow head’s length and width. See matplotlib.patches.FancyArrowPatch for attribute mutation_scale for more info.

connectionstylestring or iterable of strings (default=”arc3”)

Pass the connectionstyle parameter to create curved arc of rounding radius rad. For example, connectionstyle=’arc3,rad=0.2’. See matplotlib.patches.ConnectionStyle and matplotlib.patches.FancyArrowPatch for more info. If Iterable, index indicates i’th edge key of MultiGraph

node_sizescalar or array (default=300)

Size of nodes. Though the nodes are not drawn with this function, the node size is used in determining edge positioning.

nodelistlist, optional (default=G.nodes())

This provides the node order for the node_size array (if it is an array).

node_shapestring (default=’o’)

The marker used for nodes, used in determining edge positioning. Specification is as a matplotlib.markers marker, e.g. one of ‘so^>v<dph8’.

labelNone or string

Label for legend

min_source_marginint (default=0)

The minimum margin (gap) at the beginning of the edge at the source.

min_target_marginint (default=0)

The minimum margin (gap) at the end of the edge at the target.

hide_ticksbool, optional

Hide ticks of axes. When True (the default), ticks and ticklabels are removed from the axes. To set ticks and tick labels to the pyplot default, use hide_ticks=False.

matplotlib.collections.LineCollection or a list of matplotlib.patches.FancyArrowPatch

If arrows=True, a list of FancyArrowPatches is returned. If arrows=False, a LineCollection is returned. If arrows=None (the default), then a LineCollection is returned if G is undirected, otherwise returns a list of FancyArrowPatches.


For directed graphs, arrows are drawn at the head end. Arrows can be turned off with keyword arrows=False or by passing an arrowstyle without an arrow on the end.

Be sure to include node_size as a keyword argument; arrows are drawn considering the size of nodes.

Self-loops are always drawn with FancyArrowPatch regardless of the value of arrows or whether G is directed. When arrows=False or arrows=None and G is undirected, the FancyArrowPatches corresponding to the self-loops are not explicitly returned. They should instead be accessed via the Axes.patches attribute (see examples).


>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> G = nx.DiGraph()
>>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
>>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> alphas = [0.3, 0.4, 0.5]
>>> for i, arc in enumerate(arcs):  # change alpha values of arcs
...     arc.set_alpha(alphas[i])

The FancyArrowPatches corresponding to self-loops are not always returned, but can always be accessed via the patches attribute of the matplotlib.Axes object.

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> G = nx.Graph([(0, 1), (0, 0)])  # Self-loop at node 0
>>> edge_collection = nx.draw_networkx_edges(G, pos=nx.circular_layout(G), ax=ax)
>>> self_loop_fap = ax.patches[0]

Also see the NetworkX drawing examples at https://networkx.org/documentation/latest/auto_examples/index.html