proactive strategy 2 based on graph firm prod
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model.py
154
model.py
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@ -48,6 +48,26 @@ class Model(ap.Model):
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firm_labels_dict[code] = Firm_attr.loc[code].to_dict()
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nx.set_node_attributes(G_Firm, firm_labels_dict)
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# init graph firm prod
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Firm_Prod = pd.read_csv("Firm_amended.csv")
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# Firm['Code'] = Firm['Code'].astype('string')
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Firm_Prod.fillna(0, inplace=True)
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# Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]]
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# Firm_attr.set_index('Code', inplace=True)
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firm_prod = pd.DataFrame({'bool': Firm_Prod.loc[:, '1':].stack()})
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firm_prod = firm_prod[firm_prod['bool'] == 1].reset_index()
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firm_prod.drop('bool', axis=1, inplace=True)
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firm_prod.rename({'level_0': 'Firm_Code',
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'level_1': 'Product_Code'}, axis=1, inplace=True)
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firm_prod['Firm_Code'] = firm_prod['Firm_Code'].astype('string')
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G_FirmProd = nx.MultiDiGraph()
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G_FirmProd.add_nodes_from(firm_prod.index)
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firm_prod_labels_dict = {}
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for code in firm_prod.index:
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firm_prod_labels_dict[code] = firm_prod.loc[code].to_dict()
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nx.set_node_attributes(G_FirmProd, firm_prod_labels_dict)
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# add edge to G_firm according to G_bom
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for node in nx.nodes(G_Firm):
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for product_code in G_Firm.nodes[node]['Product_Code']:
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@ -82,17 +102,26 @@ class Model(ap.Model):
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share * len(lst_succ_firm)) > 0 else 1 # at least one
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lst_choose_firm = self.nprandom.choice(lst_succ_firm,
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n_succ_firm,
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replace=False,
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p=lst_prob)
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lst_choose_firm = list(
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set(lst_choose_firm
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)) # nprandom.choice may have duplicates
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lst_add_edge = [(node, succ_firm, {
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'Product': product_code
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}) for succ_firm in lst_choose_firm]
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G_Firm.add_edges_from(lst_add_edge)
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# graph firm prod
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pred_node = [n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == node and
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v['Product_Code'] == product_code][0]
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for succ_firm in lst_choose_firm:
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succ_node = [n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == succ_firm and
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v['Product_Code'] == succ_product_code][0]
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G_FirmProd.add_edge(pred_node, succ_node)
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self.sample.g_firm = json.dumps(nx.adjacency_data(G_Firm))
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self.firm_network = ap.Network(self, G_Firm)
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self.firm_prod_network = G_FirmProd
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# init product
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for ag_node, attr in self.product_network.graph.nodes(data=True):
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@ -145,6 +174,112 @@ class Model(ap.Model):
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f"product {product.code} not in firm {firm.code}"
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firm.a_lst_product_removed.append(product)
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# proactive strategy
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proactive_ratio = 1.0
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# get all the firm prod affected
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for firm, a_lst_product in self.dct_lst_remove_firm_prod.items():
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for product in a_lst_product:
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init_node = \
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[n for n, v in
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self.firm_prod_network.nodes(data=True)
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if v['Firm_Code'] == firm.code and
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v['Product_Code'] == product.code][0]
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dct_affected = \
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nx.dfs_successors(self.firm_prod_network,
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init_node)
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lst_affected = set()
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for i, (u, vs) in enumerate(dct_affected.items()):
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# at least 2 hops away
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if i > 0:
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pred_node = self.firm_prod_network.nodes[u]
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for v in vs:
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succ_node = self.firm_prod_network.nodes[v]
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lst_affected.add((succ_node['Firm_Code'],
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succ_node['Product_Code']))
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lst_affected = list(lst_affected)
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lst_firm_proactive = \
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[lst_affected[i] for i in
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self.nprandom.choice(range(len(lst_affected)),
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round(len(lst_affected) *
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proactive_ratio))]
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print(lst_firm_proactive)
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for firm_code, prod_code in lst_firm_proactive:
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pro_firm_prod_code = \
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[n for n, v in
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self.firm_prod_network.nodes(data=True)
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if v['Firm_Code'] == firm_code and
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v['Product_Code'] == prod_code][0]
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pro_firm_prod_node = \
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self.firm_prod_network.nodes[pro_firm_prod_code]
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pro_firm = \
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self.a_lst_total_firms.select(
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[firm.code == pro_firm_prod_node['Firm_Code']
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for firm in self.a_lst_total_firms])[0]
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# print(list(self.firm_prod_network.predecessors(firm_prod_node)))
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lst_shortest_path = \
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list(nx.all_shortest_paths(self.firm_prod_network,
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source=init_node,
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target=pro_firm_prod_code))
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dct_drs = {}
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for di_supp_code in self.firm_prod_network.predecessors(
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pro_firm_prod_code):
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di_supp_node = \
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self.firm_prod_network.nodes[di_supp_code]
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di_supp_prod = \
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self.a_lst_total_products.select(
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[product.code == di_supp_node['Product_Code']
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for product in self.a_lst_total_products])[0]
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di_supp_firm = \
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self.a_lst_total_firms.select(
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[firm.code == di_supp_node['Firm_Code']
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for firm in self.a_lst_total_firms])[0]
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lst_cand = self.model.a_lst_total_firms.select([
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di_supp_prod in firm.a_lst_product
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and di_supp_prod not in firm.a_lst_product_removed
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for firm in self.model.a_lst_total_firms
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])
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n2n_betweenness = \
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sum([True if di_supp_code in path else False
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for path in lst_shortest_path]) \
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/ len(lst_shortest_path)
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# print(lst_shortest_path)
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# print(sum([True if di_supp_code in path else False
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# for path in lst_shortest_path]), len(lst_shortest_path))
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drs = n2n_betweenness / \
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(len(lst_cand) * di_supp_firm.revenue_log)
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# print(drs)
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dct_drs[di_supp_code] = drs
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dct_drs = dict(sorted(
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dct_drs.items(), key=lambda kv: kv[1], reverse=True))
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print(dct_drs)
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for di_supp_code in dct_drs.keys():
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di_supp_node = \
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self.firm_prod_network.nodes[di_supp_code]
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di_supp_prod = \
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self.a_lst_total_products.select(
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[product.code == di_supp_node['Product_Code']
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for product in self.a_lst_total_products])[0]
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# find a dfferent firm can produce the same product
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lst_cand = self.model.a_lst_total_firms.select([
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di_supp_prod in firm.a_lst_product
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and di_supp_prod not in firm.a_lst_product_removed
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and firm.code != di_supp_node['Firm_Code']
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for firm in self.model.a_lst_total_firms
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])
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if len(lst_cand) > 0:
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select_cand = self.nprandom.choice(lst_cand)
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self.firm_network.graph.add_edges_from([
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(self.firm_network.positions[select_cand],
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self.firm_network.positions[pro_firm], {
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'Product': di_supp_prod.code
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})
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])
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select_cand.dct_prod_capacity[di_supp_prod] -= 1
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# draw network
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self.draw_network()
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@ -165,7 +300,18 @@ class Model(ap.Model):
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print(list(self.dfs_edges_prod(self.firm_network.graph,
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self.a_lst_total_firms[133],
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self.a_lst_total_firms[133].a_lst_product[0])))
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print(self.betweenness_centrality_firm_prod(self.firm_network.graph))
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# print(self.betweenness_centrality_firm_prod(self.firm_network.graph))
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t = nx.dfs_successors(self.firm_prod_network, [n for n, v in self.firm_prod_network.nodes(data=True)
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if v['Firm_Code'] == self.a_lst_total_firms[133].code and
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v['Product_Code'] == self.a_lst_total_firms[133].a_lst_product[0].code][0])
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print(t)
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for key, values in t.items():
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pred_node = self.firm_prod_network.nodes[key]
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for value in values:
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succ_node = self.firm_prod_network.nodes[value]
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print(pred_node['Firm_Code'], pred_node['Product_Code'],
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succ_node['Firm_Code'], succ_node['Product_Code'])
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print()
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def update(self):
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network.png
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network.png
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