Python Graph.is_connected方法代码示例

# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import is_connected [as 别名]
    def __findNegativeCut(self,debug=False):
        """Best negative cut heuristic.

        Heuristic to find the best cut value to construct the Gamma Model (RMgamma).

        Args:
            debug (bool,optional): Show debug information. 

        Returns:
            A Heuristic object that contains all the relevant info about the heuristic.
        
        """
        
        time_total = time.time()

        # Graph and unique set construction
        time_graph_construction = time.time()

        graph_negative = Graph()
        graph_negative.add_vertices(self.__n)
        unique_negative_weights = set()
        for i in range(self.__n):
            for j in range (i+1,self.__n):
                if self.__S[i][j] <= 0:
                    graph_negative.add_edge(i,j,weight=self.__S[i][j])
                    unique_negative_weights.add(self.__S[i][j])
        time_graph_construction = time.time() - time_graph_construction

        # Sort unique weights and start heuristic to find the best cut value
        time_find_best_cut = time.time()
        
        unique_negative_weights = sorted(unique_negative_weights)

        # Test different cuts and check connected
        best_negative_cut = 0
        for newCut in unique_negative_weights:
            edges_to_delete = graph_negative.es.select(weight_lt=newCut)
            graph_negative.delete_edges(edges_to_delete)
            if graph_negative.is_connected():
                best_negative_cut = newCut
            else:
                break

        time_find_best_cut = time.time() - time_find_best_cut
        time_total = time.time() - time_total

        if debug==True:
            print ("Time Graph Construction: %f"         %(time_graph_construction))
            print ("Time Heuristic to find best cut: %f" %(time_find_best_cut))
            print ("Total Time: %f"                      %(time_total))
            print ("NEW (Best cut-): %d"                 %(best_negative_cut))

        heuristic={}
        heuristic['cut'] = best_negative_cut
        heuristic['time_total']=time_total
        heuristic['time_graph_construction']=time_graph_construction
        heuristic['time_find_best_cut']=time_find_best_cut

        return heuristic

# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import is_connected [as 别名]
def get_igraph_graph(network):
    print 'load %s users into igraph' % len(network)
    g = Graph(directed=True)
    keys_set = set(network.keys())
    g.add_vertices(network.keys())
    print 'iterative load into igraph'
    edges = []
    for source in network:
        for target in network[source].intersection(keys_set):
            edges.append((source, target))
    g.add_edges(edges)
    g = g.simplify()
    print 'make sure graph is connected'
    connected_clusters = g.clusters()
    connected_cluster_lengths = [len(x) for x in connected_clusters]
    connected_cluster_max_idx = connected_cluster_lengths.index(max(connected_cluster_lengths))
    g = connected_clusters.subgraph(connected_cluster_max_idx)
    if g.is_connected():
        print 'graph is connected'
    else:
        print 'graph is not connected'
    return g

# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import is_connected [as 别名]
from igraph import Graph

foster = Graph().LCF(90, [17, -9, 37, -37, 9, -17], 15)
foster.to_directed()

print "Is Directed? " + str(foster.is_directed())

for start in range(0, 90):
    for end in range(0, 90):
        # Don't delete this. Delete opposite direction edge
        if start + 1 == end:
            opposite_ID = foster.get_eid(end, start, True, False)
            if opposite_ID != 1:
                foster.delete_edges([opposite_ID])
        else:
            opposite_ID = foster.get_eid(end, start, True, False)
            if opposite_ID != -1:
                current_ID = foster.get_eid(start, end, True, False)
                if current_ID != -1:
                    foster.delete_edges([current_ID])

print "Number of Edges: " + str(len(foster.get_edgelist()))
print (foster.is_connected())

foster_list = foster.get_adjacency()

for sublist in foster_list:
    sublist = map(str, sublist)
    sublist_string = " ".join(sublist)
    print (sublist_string)

# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import is_connected [as 别名]
def reduce_and_save_communities(root_user, distance=10, return_graph_for_inspection=False):

    print 'starting reduce_and_save_communities'
    print 'root_user: %s,  following_in_our_db: %s, distance: %s' % (
        root_user.screen_name, len(root_user.following), distance)
    network = TwitterUser.get_rooted_network(root_user, postgres_handle, distance=distance)

    print 'load %s users into igraph' % len(network)
    g = Graph(directed=True)
    keys_set = set(network.keys())
    g.add_vertices(network.keys())
    g.vs["id"] = network.keys() #need this for pajek format
    print 'iterative load into igraph'
    edges = []
    for source in network:
        for target in network[source].intersection(keys_set):
            edges.append((source, target))
    g.add_edges(edges)
    g = g.simplify()
    print 'make sure graph is connected'
    connected_clusters = g.clusters()
    connected_cluster_lengths = [len(x) for x in connected_clusters]
    connected_cluster_max_idx = connected_cluster_lengths.index(max(connected_cluster_lengths))
    g = connected_clusters.subgraph(connected_cluster_max_idx)
    if g.is_connected():
        print 'graph is connected'
    else:
        print 'graph is not connected'

    if return_graph_for_inspection:
        return g

    print 'write to pajek format'
    root_file_name = root_user.screen_name
    f = open('io/%s.net' % root_file_name, 'w')
    g.write(f, format='pajek')

    print 'run infomap'
    #infomap_command = 'infomap_dir/infomap 345234 io/%s.net 10'
    #infomap_command = 'conf-infomap_dir/conf-infomap 344 io/%s.net 10 10 0.50'
    infomap_command = 'infohiermap_dir/infohiermap 345234 io/%s.net 30'
    os.system(infomap_command % root_file_name)

    print 'read into memory'
    f = open('io/%s.smap' % root_file_name)

    section_header = ''
    communities = defaultdict(lambda: ([], [], []))
    for line in f:
        if line.startswith('*Modules'):
            section_header = 'Modules'
            continue
        if line.startswith('*Insignificants'):
            section_header = 'Insignificants'
            continue
        if line.startswith('*Nodes'):
            section_header = 'Nodes'
            continue
        if line.startswith('*Links'):
            section_header = 'Links'
            continue

        if section_header == 'Modules':
            #looks like this:
            #1 "26000689,..." 0.130147 0.0308866
            #The names under *Modules are derived from the node with the highest 
            #flow volume within the module, and 0.25 0.0395432 represent, respectively, 
            #the aggregated flow volume of all nodes within the module and the per 
            #step exit flow from the module.
            continue

        if section_header == 'Nodes':
            #looks like this: 
            #1:10 "2335431" 0.00365772
            #or w/ a semicolon instead, semicolon means not significant
            #see http://www.tp.umu.se/~rosvall/code.html
            if ';' in line:
                continue
            community_idx = line.split(':')[0]
            node_id = line.split('"')[1]
            final_volume = float(line.split(' ')[2])
            communities[community_idx][1].append(node_id)
            communities[community_idx][2].append(final_volume)

        if section_header == 'Links':
            #community_edges
            #looks like this:
            #1 4 0.0395432
            community_idx = line.split(' ')[0]
            target_community_idx = line.split(' ')[1]
            edge_weight = line.split(' ')[2]
            communities[community_idx][0].append('%s:%s' % (target_community_idx, edge_weight))