Add local clustering coefficient to output. (45756293) · Commits · Victor Zimmermann / Absinth - A Small World of Semantic Similarity

src/absinth.py

+30 −12

Original line number	Diff line number	Diff line
		@@ -891,23 +891,20 @@ def disambiguate_mst(graph: nx.Graph, root_hub_list: list,
		##############################


		def global_clustering_coefficient(graph: nx.Graph) -> float:
		"""Calculates global clustering coefficient from graph.
		def local_clustering_coefficient(graph: nx.Graph, node: str) -> float:
		"""Calculates local clustering coefficient from node.

		Iterates over every node and calculates the global coefficient as a mean
		of every local clustering coefficient.
		Local Clustering Coefficient is defined as number of edges between
		neighbors of a node, divided by the number of possible nodes.

		Args:
		graph: Undirected graph.
		node: Node in graph.

		Returns:
		Global coefficient.
		Local coefficient.
		"""

		local_coefficient_list = list()

		for node in graph.nodes:

		neighbor_list = graph.adj[node]

		neighbor_edge_list = [(x,y) for x in neighbor_list
		@@ -915,7 +912,7 @@ def global_clustering_coefficient(graph: nx.Graph) -> float:

		if len(neighbor_edge_list) == 0:

		local_coefficient_list.append(0)
		return 0

		else:

		@@ -924,7 +921,28 @@ def global_clustering_coefficient(graph: nx.Graph) -> float:
		if graph.has_edge(x,y):
		edge_count += 1

		local_coefficient_list.append(edge_count/len(neighbor_edge_list))
		return edge_count/len(neighbor_edge_list)



		def global_clustering_coefficient(graph: nx.Graph) -> float:
		"""Calculates global clustering coefficient from graph.

		Iterates over every node and calculates the global coefficient as a mean
		of every local clustering coefficient.

		Args:
		graph: Undirected graph.

		Returns:
		Global coefficient.
		"""

		local_coefficient_list = list()

		for node in graph.nodes:

		local_coefficient_list.append(local_clustering_coefficient(graph,node))

		return np.mean(local_coefficient_list)

		@@ -983,7 +1001,7 @@ def print_stats(stat_dict: dict, graph: nx.Graph) -> None:
		stat_string.append('Tuples gained through merging: {}.'.format(stat_dict['pipe_gain']))
		stat_string.append('Sense inventory:')
		for hub in stat_dict['hubs'].keys():
		stat_string.append(' -> {} ({}): {}.'.format(hub,graph.degree[hub], ", ".join(stat_dict['hubs'][hub])))
		stat_string.append(' -> {} ({}): {}.'.format(hub,local_clustering_coefficient(graph,hub), ", ".join(stat_dict['hubs'][hub])))

		print('\n[A] '+'\n[A] '.join(stat_string)+'\n')