<｜begin▁of▁sentence｜>### Instruction:
How can you generate a directed graph using igraph where the likelihood of a node gaining a new edge is influenced by the edges gained in the past 5 steps, with 100 nodes and each node initially having 3 outgoing edges following a linear model (power = 1)?
### Response:
import igraph as ig

# Parameters
n = 100  # number of vertices
m = 3    # number of outgoing edges for each vertex
window = 5  # size of the time window
outpref = False  # out-degree does not alter citation probability
directed = True  # generate a directed graph
power = 1  # linear model

# Generate the graph
g = ig.Graph.Recent_Degree(n, m, window=window, outpref=outpref, directed=directed, power=power)

# Plot the graph
ig.plot(g, bbox=(300, 300), vertex_size=5, edge_arrow_size=0.5)
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