def iterative_deepening_dfs(g: nx.DiGraph, source: str, destination: str):
Adj = dict(g.adjacency())
Adj = {i: list(Adj[i]) for i in Adj}
Level = get_level(g, source)
max_depth = max(map(int, Level.values()))
for depth in range(max_depth + 1):
Result, Path = dfs(Adj, Level, source, destination, depth)
if Result:
break
if Result:
print("\n ~: Path Found :~ \n")
print(" -> ".join(Path))
else:
print("\n ~: Path Not Found :~ ")
def bfs(g: nx.DiGraph, start_node: str):
Adj = dict(g.adjacency())
Adj = {i: list(Adj[i]) for i in Adj}
Visited = {i: False for i in Adj}
Queue = [start_node]
Visited[start_node] = True
Result = []
while Queue:
s = Queue.pop(0)
Result.append(s)
for node in Adj[s]:
if not Visited[node]:
Queue.append(node)
Visited[s] = True
print("\n", " -> ".join(Result))
def dfs(g: nx.DiGraph, start_node: str):
Adj = dict(g.adjacency())
Adj = {i: list(Adj[i]) for i in Adj}
Visited = {i: False for i in Adj}
Stack = [start_node]
Result = []
while len(Stack):
s = Stack[-1]
Stack.pop()
if not Visited[s]:
Result.append(s)
Visited[s] = True
for node in Adj[s]:
if not Visited[node]:
Stack.append(node)
print("\n", " -> ".join(Result))
def draw(G: DiGraph, ranks: List[float]):
print("Plotting...")
pos = nx.spring_layout(G)
for i in range(len(G.nodes)):
G.nodes[i]['pos'] = pos[i]
G.nodes[i]['rank'] = ranks[i]
edge_trace = Scatter(x=[],
y=[],
line=Line(width=0.5, color='#888'),
hoverinfo='none',
mode='arrows')
for edge in G.edges():
x0, y0 = G.node[edge[0]]['pos']
x1, y1 = G.node[edge[1]]['pos']
edge_trace['x'] += [x0, x1, None]
edge_trace['y'] += [y0, y1, None]
sizes = helpers.ranks_to_int(ranks)
percents = helpers.ranks_to_percent(ranks)
node_trace = Scatter(
x=[],
y=[],
text=[],
mode='markers',
hoverinfo='text',
marker=Marker(
showscale=False,
# colorscale options
# 'Greys' | 'Greens' | 'Bluered' | 'Hot' | 'Picnic' | 'Portland' |
# Jet' | 'RdBu' | 'Blackbody' | 'Earth' | 'Electric' | 'YIOrRd' | 'YIGnBu'
color=[(float(i[:-1]) * 255)**3 for i in percents],
size=sizes,
line=dict(width=2)))
for node in G.nodes():
x, y = G.node[node]['pos']
node_trace['x'].append(x)
node_trace['y'].append(y)
for node, adjacency in enumerate(G.adjacency()):
node_trace['marker']['color'].append(len(adjacency))
node_info = 'PageRank: ' + percents[node]
node_trace['text'].append(node_info)
# node_trace[node]['size'] = G.nodes[node]['size']
annot = [(dict(showarrow=True,
arrowhead=200,
arrowsize=100,
arrowwidth=200,
arrowcolor='#636363',
x=edge[0],
y=edge[1],
xref='x',
yref='y')) for edge in G.edges()]
annot.append(
dict(showarrow=False, xref="paper", yref="paper", x=0.005, y=-0.002))
fig = Figure(data=Data([edge_trace, node_trace]),
layout=Layout(title='Regular PageRank',
titlefont=dict(size=16),
showlegend=False,
hovermode='closest',
margin=dict(b=20, l=5, r=5, t=40),
annotations=annot,
xaxis=XAxis(showgrid=False,
zeroline=False,
showticklabels=False),
yaxis=YAxis(showgrid=False,
zeroline=False,
showticklabels=False)))
py.iplot(fig, filename='networkx')
if args.embeddings_file is not None:
# If a similarity embeddings were provided, link the k nearest neighbours to each node
kv = KeyedVectors.load_word2vec_format(args.embeddings_file)
for node in trange(num_nodes):
potential_links = [
int(pair[0])
for pair in kv.most_similar(positive=[str(node)], topn=args.k)
]
for adj in potential_links:
G.add_edge(node, adj)
if args.tfidf_file is not None:
with open(args.tfidf_file, "rb") as f:
embeddings = pickle.load(f)
dist_matrix = cosine_similarity(embeddings)
for node in trange(num_nodes):
potential_links = np.argpartition(dist_matrix[node],
-args.k)[-args.k:]
for adj in potential_links:
G.add_edge(node, adj)
# Save the adjacency list
with open(args.output_file, "w") as f:
adjlist = G.adjacency()
for node, adj in adjlist:
line = " ".join([str(node)] + list(map(str, sorted(adj.keys()))))
f.write(line + '\n')
print("Done.")
