def create_connectivity(agents, p, type='undirected_random'):
properties = {'graph_type' : type, 'connection_probability' : p}
conn = gg.create_graph_type(agents, properties)[0]
for agent1 in conn.nodes():
agent1.connect_to(conn.neighbors(agent1))
if type in ['hierarchy', 'collaborative']:
return (1, len(agents))
else:
cc_conn = nx.connected_components(conn)
## return the number of connected components and
## the size of the largest connected component
return (len(cc_conn), len(cc_conn[0]))
if __name__ =="__main__":
p = {}
gtypes = [('erdos_renyi_graph', 20, 0.15, 3)]
#gtypes = [('random', 20, 0.15, 3), \
#('random', 200, 0.03, 3), \
#('watts_strogatz_graph', 20, 0.1, 5), \
#('watts_strogatz_graph', 200, 0.01, 8), \
#('barabasi_albert_graph', 20, 0.1, 10), \
#('barabasi_albert_graph', 200, 0.01, 20), \
#]
#gtypes = [('watts_strogatz_graph', 40, 0.1, 3), \
#('watts_strogatz_graph', 40, 0.1, 8), \
#('watts_strogatz_graph', 40, 0.1, 15), \
#]
for (g,x,y,z) in gtypes:
p['graph_type'] = g
p['num_agents'] = x
p['connection_probability'] = y
p['num_nodes_to_attach'] = z
ag = range(p['num_agents'])
(conn, stats) = gg.create_graph_type(ag,p)
print (p)
print ("Connected components:", stats['num_cc'])
histogram(stats, 20*(1+int(p['num_agents']/60)))
def run_simulation_one_graph(properties, outfile):
facts = range(properties["num_facts"] + properties["num_noise"])
agents = []
for i in xrange(properties["num_agents"]):
agents.append(
Agent.Agent(
properties["willingness"],
properties["competence"],
properties["num_facts"],
properties["num_noise"],
properties["spamminess"],
properties["selfishness"],
properties["trust_used"],
properties["inbox_trust_used"],
properties["trust_filter_on"],
)
)
## Create agent graph
conn, stats = gg.create_graph_type(agents, properties)
for agent1 in conn.nodes():
agent1.connect_to(conn.neighbors(agent1))
nodes_to_try = top_nodes(stats)
# print "Nodes", nodes_to_try
for current_agent in nodes_to_try:
# print "New node", current_agent
agents[current_agent].capacity = 50 ##set one agent to high capacity
node_stats = current_agent_stats(current_agent, stats)
## Distribute facts to agents
for i in facts:
for j in xrange(properties["agent_per_fact"]):
## find a random agent, and distribute fact i
k = random.randint(0, properties["num_agents"] - 1)
agents[k].add_fact(i)
## Initialize agents to send everything that they think is valuable
## in their outbox
for agent in agents:
agent.init_outbox()
action_list = []
all_stats = ss.SimulationStats(
properties["num_facts"], properties["num_noise"], stats["num_cc"], stats["largest_cc"]
)
##actual simulation starts here
for i in xrange(properties["num_steps"]):
x = one_step_simulation(agents)
action_list.append(x)
if i % 100 == 0:
all_stats.update_stats(agents, i)
summary_results = all_stats.process_sa()
results = {}
results["setup"] = properties
results["total_filtered"] = summary_results["total_filtered"]
results["num_cc"] = summary_results["num_cc"]
results["size_lcc"] = summary_results["size_lcc"]
results["summary_results"] = summary_results
results["all_sa"] = all_stats.sa
results["all_comm"] = all_stats.comm
results["all_sa0"] = all_stats.sa0
results["all_comm0"] = all_stats.comm0
results["steps"] = all_stats.steps
results["node_stats"] = node_stats
add_to_output(results, outfile)
for agent in agents:
agent.clear()
agent.capacity = 1
def run_simulation_one_graph(properties, outfile):
facts = range(properties['num_facts']+properties['num_noise'])
agents = []
all_results = []
for i in xrange(properties['num_agents']):
agents.append ( SimpleAgent.SimpleAgent(properties['willingness'],\
properties['competence'],\
properties['num_facts'],\
properties['num_noise'],\
properties['spamminess'],\
properties['selfishness'],\
properties['trust_used'],\
properties['inbox_trust_used'],\
properties['trust_filter_on'],
twitter_model = properties['twitter_model']))
## Create agent graph
conn, stats = gg.create_graph_type(agents, properties)
for agent1 in conn.nodes():
agent1.connect_to(conn.neighbors(agent1))
nodes_to_try = top_nodes (stats)
#print "Nodes", nodes_to_try
for current_agent in nodes_to_try:
#print "New node", current_agent
if current_agent != -1:
agents[current_agent].capacity = 10 ##set one agent to high capacity
node_stats = current_agent_stats (current_agent, stats)
agent_to_track = current_agent
else:
node_stats = {}
agent_to_track = 0
## Distribute facts to agents
for i in facts:
for j in xrange(properties['agent_per_fact']):
## find a random agent, and distribute fact i
k = random.randint(0,properties['num_agents']-1)
agents[k].knowledge.add(i)
## Initialize agents to send everything that they think is valuable
## in their outbox
for agent in agents:
agent.init_outbox()
#action_list = []
all_stats = ss.SimpleSimulationStats(properties['num_facts'],\
properties['num_noise'],\
stats['num_cc'],
stats['largest_cc'], \
properties['sa_increment'],\
agent_to_track)
##actual simulation starts here
for i in xrange(properties['num_steps']):
x = one_step_simulation(agents)
#action_list.append(x)
if i%properties['statistic_taking_frequency'] == 0:
all_stats.update_stats(agents,i)
summary_results = all_stats.process_sa()
results = {}
results['setup'] = properties
results['graph_type'] = properties['graph_type']
results['total_filtered'] = summary_results['total_filtered']
results['num_cc'] = summary_results['num_cc']
results['size_lcc'] = summary_results['size_lcc']
results['summary_results'] = summary_results
results['all_sa'] = all_stats.sa
results['all_comm'] = all_stats.comm
results['all_sa0'] = all_stats.sa0
results['all_comm0'] = all_stats.comm0
results['steps'] = all_stats.steps
results['node_stats'] = node_stats
add_to_output(all_results, results, outfile)
for agent in agents:
agent.clear()
agent.capacity = 1
flush_results(all_results, outfile)
def run_simulation_one_graph(properties, outfile):
facts = range(properties['num_facts'] + properties['num_noise'])
agents = []
all_results = []
for i in xrange(properties['num_agents']):
agents.append ( SimpleAgent.SimpleAgent(properties['willingness'],\
properties['competence'],\
properties['num_facts'],\
properties['num_noise'],\
properties['spamminess'],\
properties['selfishness'],\
properties['trust_used'],\
properties['inbox_trust_used'],\
properties['trust_filter_on'],
twitter_model = properties['twitter_model']))
## Create agent graph
conn, stats = gg.create_graph_type(agents, properties)
for agent1 in conn.nodes():
agent1.connect_to(conn.neighbors(agent1))
nodes_to_try = top_nodes(stats)
#print "Nodes", nodes_to_try
for current_agent in nodes_to_try:
#print "New node", current_agent
if current_agent != -1:
agents[
current_agent].capacity = 10 ##set one agent to high capacity
node_stats = current_agent_stats(current_agent, stats)
agent_to_track = current_agent
else:
node_stats = {}
agent_to_track = 0
## Distribute facts to agents
for i in facts:
for j in xrange(properties['agent_per_fact']):
## find a random agent, and distribute fact i
k = random.randint(0, properties['num_agents'] - 1)
agents[k].knowledge.add(i)
## Initialize agents to send everything that they think is valuable
## in their outbox
for agent in agents:
agent.init_outbox()
#action_list = []
all_stats = ss.SimpleSimulationStats(properties['num_facts'],\
properties['num_noise'],\
stats['num_cc'],
stats['largest_cc'], \
properties['sa_increment'],\
agent_to_track)
##actual simulation starts here
for i in xrange(properties['num_steps']):
x = one_step_simulation(agents)
#action_list.append(x)
if i % properties['statistic_taking_frequency'] == 0:
all_stats.update_stats(agents, i)
summary_results = all_stats.process_sa()
results = {}
results['setup'] = properties
results['graph_type'] = properties['graph_type']
results['total_filtered'] = summary_results['total_filtered']
results['num_cc'] = summary_results['num_cc']
results['size_lcc'] = summary_results['size_lcc']
results['summary_results'] = summary_results
results['all_sa'] = all_stats.sa
results['all_comm'] = all_stats.comm
results['all_sa0'] = all_stats.sa0
results['all_comm0'] = all_stats.comm0
results['steps'] = all_stats.steps
results['node_stats'] = node_stats
add_to_output(all_results, results, outfile)
for agent in agents:
agent.clear()
agent.capacity = 1
flush_results(all_results, outfile)
plt.show()
if __name__ == "__main__":
p = {}
gtypes = [('random', 20, 0.15, 3), \
('random', 200, 0.03, 3), \
('watts_strogatz_graph', 20, 0.1, 5), \
('watts_strogatz_graph', 200, 0.01, 8), \
('barabasi_albert_graph', 20, 0.1, 10), \
('barabasi_albert_graph', 200, 0.01, 20), \
]
gtypes = [('watts_strogatz_graph', 40, 0.1, 3), \
('watts_strogatz_graph', 40, 0.1, 8), \
('watts_strogatz_graph', 40, 0.1, 15), \
]
for (g, x, y, z) in gtypes:
p['graph_type'] = g
p['num_agents'] = x
p['connection_probability'] = y
p['num_nodes_to_attach'] = z
ag = range(p['num_agents'])
(conn, stats) = gg.create_graph_type(ag, p)
print p
print "Connected components:", stats['num_cc']
histogram(stats, 20 * (1 + int(p['num_agents'] / 60)))
