def get_graph():
response_builder = ResponseBuilder()
nodes = response_builder.return_node_list(d_social_network_graph)
edges = response_builder.return_edge_list(d_social_network_graph)
response = dict()
response["nodes"] = nodes
response['edges'] = edges
return jsonify(response)
def Switch(self, ps):
#Objekt vom ResponseBuilder erstellen
rb = ResponseBuilder()
#Auf verwendete Methode testen und entsprechende ResponseBuilder-Methode aufrufen
if ps.Method == "GET":
response = rb.Getresponse(ps)
elif ps.Method == "PUT":
response = rb.Putresponse(ps)
else:
response = rb.Errorresponse(ps)
#Vom ResponseBuilder erstellte Response an MainDatei weitergeben
return response
def get_school_communities():
multi_school_community_graph = louvain.find_partition(d_social_network_graph, louvain.CPMVertexPartition,
resolution_parameter=0.0005)
for idx, community in enumerate(multi_school_community_graph):
for node in community:
v = d_social_network_graph.vs[node]
v["groupId"] = idx
response_builder = ResponseBuilder()
nodes = response_builder.return_node_list(d_social_network_graph)
edges = response_builder.return_edge_list(d_social_network_graph)
response = dict()
response["nodes"] = nodes
response['edges'] = edges
return jsonify(response)
def get_interest_based_communities():
target_school = request.args.get('school')
interests = request.args.get('interests').split(',')
# Level 1 - School Community Detection
multi_school_community_graph = louvain.find_partition(d_social_network_graph, louvain.CPMVertexPartition,
resolution_parameter=0.0005)
school_community_id_map = {}
for community_id,community in enumerate(multi_school_community_graph):
min_check = len(community)*0.1
if min_check >= 1:
for i,node in enumerate(community):
school_name = d_social_network_graph.vs[node]['school']
if school_name in school_community_id_map:
school_community_id_map[school_name] += ","+str(community_id)
else:
school_community_id_map[school_name] = str(community_id)
i += 1
if i > min_check:
break
for school, community_id in school_community_id_map.iteritems():
community_ids = community_id.split(",")
school_community_id_map[school] = max(set(community_ids), key=community_ids.count)
target_community_id = school_community_id_map.get(target_school)
school_nodes = multi_school_community_graph[int(target_community_id)]
# Level 2 Interest-based Community Detection
school_community_graph = Graph()
school_community_graph.to_directed()
interest_based_community_graph = Graph()
interest_based_community_graph.to_directed()
updated_school_nodes = []
for node in school_nodes:
school_community_graph.add_vertex(d_social_network_graph.vs[node].index)
updated_school_nodes.append(d_social_network_graph.vs[node])
updated_school_node_ids=[]
for i,v in enumerate(school_community_graph.vs):
v["id"] = updated_school_nodes[i]["id"]
v["label"] = updated_school_nodes[i]["label"]
v["groupId"] = -1
v['interestedNode']=False
v['influentialNode']=False
v["interest"] = updated_school_nodes[i]["interest"]
updated_school_node_ids.append(updated_school_nodes[i]["id"])
for e in d_social_network_graph.es:
src = e.source
dest = e.target
if d_social_network_graph.vs[src]['id'] in updated_school_node_ids \
and d_social_network_graph.vs[dest]['id'] in updated_school_node_ids:
id_src = d_social_network_graph.vs[src]['id']
id_dest = d_social_network_graph.vs[dest]['id']
sg_src = school_community_graph.vs.find(id=id_src).index
sg_dest = school_community_graph.vs.find(id=id_dest).index
school_community_graph.add_edge(sg_src, sg_dest)
interested_nodes = []
for v in school_community_graph.vs:
is_interested = False
for interest in interests:
if interest in v["interest"]:
is_interested = True
else:
is_interested = False
break
if is_interested:
v["interestedNode"] = True
interested_nodes.append(v)
# Compute shortest paths among sub-community nodes usinf Dijkstra's shortest path algorithm
influenceFactorAdjGrid = d_social_network_graph.shortest_paths_dijkstra(interested_nodes, interested_nodes, None, OUT)
interest_based_community_graph = Graph.Weighted_Adjacency(influenceFactorAdjGrid, ADJ_DIRECTED, "weight", False)
interest_based_community_graph.simplify(combine_edges='sum')
i = 0
for v in interest_based_community_graph.vs:
node = interested_nodes[i]
v["id"] = node["id"]
i += 1
influentials_object = get_influential_node(interest_based_community_graph, school_community_graph)
if len(influentials_object) > 0:
for node_id in influentials_object[len(influentials_object)-1][2]:
node_id["influentialNode"] = True
response_builder = ResponseBuilder()
nodes = response_builder.return_node_list(school_community_graph)
edges = response_builder.return_edge_list(school_community_graph)
# Json object for coverage graph
nodes_coverage = []
for item in influentials_object:
temp_json = dict()
temp_json["no_of_influencers"] = item[0]
temp_json["coverage"] = item[1]
temp_json["node_info"] = []
for node in item[2]:
curr_node = dict()
curr_node["id"] = node["id"]
curr_node["interest"] = node["interest"]
curr_node["influentialNode"] = node["influentialNode"]
curr_node["groupId"] = node["groupId"]
curr_node["influence"] = node["influence"]
curr_node["interestedNode"] = node["interestedNode"]
curr_node["name"] = node["name"]
temp_json["node_info"].append(curr_node)
nodes_coverage.append(temp_json)
response = dict()
response["nodes"] = nodes
response['edges'] = edges
response['coverageObject'] = nodes_coverage
return jsonify(response)