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b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -40,7 +40,7 @@ class Status(Enum): 'directly vaccinated': 'green', 'default' : "#00FFD0" } -logger = logging.getLogger('firefighter_problem_main') +logger = logging.getLogger('firefighter_problem') # ============================ Validation Functions ============================ From 181f519ab946622a68e5ad4b155ca4fee9a2fed8 Mon Sep 17 00:00:00 2001 From: Yuval Bubnovsky Date: Fri, 30 Aug 2024 16:58:28 +0300 Subject: [PATCH 2/7] updated README.md with algorithms description and examples --- README.md | 37 ++++++++++++++++++++++++++++++++++++- 1 file changed, 36 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index f60430d..26001c1 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ This installs the latest version of networkx, and the new algorithms added in ne ### Rank Maximal Matching A rank-maximal matching is a matching that maximizes the number of agents who are matched to their 1st priority; subject to that, it maximizes the number of agents matched to their 2nd priority; and so on. -``` +```python import networkz as nx G = nx.Graph() G.add_nodes_from(["agent1", "agent2"], bipartite=0) @@ -47,6 +47,41 @@ F = nx.maximum_weight_fractional_matching(G) print(F) ``` +### Approximating The Fire-Fighter Problem +The Firefighter problem models the case where a diffusive process such as an infection (or an idea, a computer virus, a fire) is spreading through a network, and our goal is to contain this infection by using targeted vaccinations. + +Networkz implements several algorithms to approximate solutions for the fire-fighter problems in 2 models: spreading & non-spreading, where the virus/fire always spreads but the spread of vaccination is dependant on the model. + +Under each such model, we are intrested in two problem types: MaxSave (save as many nodes from the target list given a budget) and MinBudget (What is the minimum budget needed to save all of the node target list) + +``` +import networkz as nx +G = nx.DiGraph() +G.add_nodes_from([0,1,2,3,4,5,6], status="vulnerable") +G.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) + +strategy, saved_nodes = nx.spreading_maxsave(G, budget = 1, source = 0, targets = [1,2,3,4,5,6]) +# Will return ([(2, 1), (4, 2)], {2, 3, 4, 5, 6}) + +min_budget, strategy = nx.spreading_minbudget(G2, source = 0,targets = [1,2,3,4,5,6]) +min_budget, strategy = nx.non_spreading_minbudget(G, source = 0,targets = [1,2,3,4,5,6]) +# Both will return (2, [(1, 1), (2, 1)]) +``` + +Another algorithm which is implemented in networkz is MinBudget in a non-spreading model (vaccine doesn't spread), running on a directed-layered network graph: +``` +import networkz as nx +G = nx.DiGraph() +G.add_nodes_from([0,1,2,3,4,5], status="vulnerable") +G.add_edges_from([(0,1),(0,2),(1,3),(1,4),(1,5),(2,3),(2,4),(2,5),(3,5),(4,5)]) + +# The algorithm will check if this is actually a directed-layered network graph +min_budget, strategy = nx.non_spreading_dirlaynet_minbudget(G, source = 0,targets = [1,2,3,4,5]) +# Will return (2, [(1, 1), (2, 1)]) +``` + +See the [algorithms in actions](https://the-firefighters.github.io/WebsiteGit/) for more information and a virtual sandbox to run and observe the algirithms. + ### Social-Aware Coalition Formation (TODO) From a66e2cf8c6b978429349d857b18e795752c4b5f7 Mon Sep 17 00:00:00 2001 From: Yuval Bubnovsky Date: Fri, 30 Aug 2024 17:00:43 +0300 Subject: [PATCH 3/7] added correct python syntax highlighting to code examples --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 26001c1..57b5786 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ See [demo website](https://rmm.csariel.xyz/) for more information. ### Maximum-Weight Fractional Matching Maximum-weight fractional matching is a graph optimization problem where the goal is to find a set of edges with maximum total weight, allowing for fractional inclusion of edges. -``` +```python import networkz as nx G = nx.Graph() G.add_nodes_from(["a1", "a2"]) @@ -54,7 +54,7 @@ Networkz implements several algorithms to approximate solutions for the fire-fig Under each such model, we are intrested in two problem types: MaxSave (save as many nodes from the target list given a budget) and MinBudget (What is the minimum budget needed to save all of the node target list) -``` +```python import networkz as nx G = nx.DiGraph() G.add_nodes_from([0,1,2,3,4,5,6], status="vulnerable") @@ -69,7 +69,7 @@ min_budget, strategy = nx.non_spreading_minbudget(G, source = 0,targets = [1,2,3 ``` Another algorithm which is implemented in networkz is MinBudget in a non-spreading model (vaccine doesn't spread), running on a directed-layered network graph: -``` +```python import networkz as nx G = nx.DiGraph() G.add_nodes_from([0,1,2,3,4,5], status="vulnerable") From f00261202437f9e4e7ee0864d8fd528573d007fb Mon Sep 17 00:00:00 2001 From: Yuval Bubnovsky Date: Fri, 30 Aug 2024 17:01:44 +0300 Subject: [PATCH 4/7] G2 type fix --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 57b5786..0c0605c 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ G.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) strategy, saved_nodes = nx.spreading_maxsave(G, budget = 1, source = 0, targets = [1,2,3,4,5,6]) # Will return ([(2, 1), (4, 2)], {2, 3, 4, 5, 6}) -min_budget, strategy = nx.spreading_minbudget(G2, source = 0,targets = [1,2,3,4,5,6]) +min_budget, strategy = nx.spreading_minbudget(G, source = 0,targets = [1,2,3,4,5,6]) min_budget, strategy = nx.non_spreading_minbudget(G, source = 0,targets = [1,2,3,4,5,6]) # Both will return (2, [(1, 1), (2, 1)]) ``` From 888cfec1e65d8dd878940c4041aa0596875042f0 Mon Sep 17 00:00:00 2001 From: Yuval Bubnovsky Date: Fri, 30 Aug 2024 17:59:05 +0300 Subject: [PATCH 5/7] removed redundent setup_logger() function; Added README.md examples for heuristic functions --- README.md | 15 ++++++++++++--- .../firefighter_problem/Firefighter_Problem.py | 14 +------------- 2 files changed, 13 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 0c0605c..b25d3f0 100644 --- a/README.md +++ b/README.md @@ -50,9 +50,7 @@ print(F) ### Approximating The Fire-Fighter Problem The Firefighter problem models the case where a diffusive process such as an infection (or an idea, a computer virus, a fire) is spreading through a network, and our goal is to contain this infection by using targeted vaccinations. -Networkz implements several algorithms to approximate solutions for the fire-fighter problems in 2 models: spreading & non-spreading, where the virus/fire always spreads but the spread of vaccination is dependant on the model. - -Under each such model, we are intrested in two problem types: MaxSave (save as many nodes from the target list given a budget) and MinBudget (What is the minimum budget needed to save all of the node target list) +Networkz implements several algorithms to approximate solutions for the fire-fighter problems in 2 models: spreading & non-spreading, where the virus/fire always spreads but the spread of vaccination is dependant on the model. Under each such model, we are intrested in two problem types: MaxSave (save as many nodes from the target list given a budget) and MinBudget (What is the minimum budget needed to save all of the node target list) ```python import networkz as nx @@ -80,6 +78,17 @@ min_budget, strategy = nx.non_spreading_dirlaynet_minbudget(G, source = 0,target # Will return (2, [(1, 1), (2, 1)]) ``` +The library also implements two local-search based heuristic algorithms (MaxSave & MinBudget), which are applicable to both spreading models and may return better results under certain conditions: MaxSave may save more nodes on dense graph (>0.5 edge probability), and MinBudget may return a smaller budget for sparser graphs (<0.5 edge probability) + +```python +import networkz as nx +G = nx.DiGraph() +G.add_nodes_from([0, 1, 2, 3], status="vulnerable") +G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) +strategy, saved_nodes = nx.heuristic_maxsave(G, budget = 1, source = 0, targets = [1, 2, 3], spreading = False) # ([(1, 1)], {1, 3}) +min_budget, strategy = nx.heuristic_minbudget(G, source = 0, targets = [1, 2, 3], spreading = True) # (2, [(1, 1), (2, 1)]) +``` + See the [algorithms in actions](https://the-firefighters.github.io/WebsiteGit/) for more information and a virtual sandbox to run and observe the algirithms. ### Social-Aware Coalition Formation diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index 42706ef..c65b162 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -26,20 +26,8 @@ from networkz.algorithms.max_flow_with_node_capacity import min_cut_with_node_capacity from networkz.algorithms.approximation.firefighter_problem.Utils import * -def setup_logger(logger): - logger.setLevel(logging.DEBUG) - - console_handler = logging.StreamHandler() - console_handler.setLevel(logging.DEBUG) - - formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') - console_handler.setFormatter(formatter) - - logger.addHandler(console_handler) - return logger - -logger = logging.getLogger('firefighter_problem') +logger = logging.getLogger(__name__) def spreading_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, stop_condition=None) -> tuple[list, set]: """ From d4b6bfaae0d149929b62b9a354db03e965dd0133 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Sat, 31 Aug 2024 20:09:07 +0300 Subject: [PATCH 6/7] removed display_graph function --- .../Firefighter_Problem.py | 4 --- .../firefighter_problem/Utils.py | 30 ------------------- 2 files changed, 34 deletions(-) diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py index c65b162..6d728e4 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py +++ b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py @@ -327,12 +327,10 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, source:int, targets:list logger.error("The graph is not a DAG graph, thus cannot run the algorithm") return - #display_graph(Graph) logger.info(f"Starting the non_spreading_dirlaynet_minbudget function with source node {source} and targets: {targets}") layers = adjust_nodes_capacity(Graph, source) G = create_st_graph(Graph, targets, 't') - #display_graph(G) G_reduction_min_cut = min_cut_with_node_capacity(G, source=source, target='t') N_groups = min_cut_N_groups(G_reduction_min_cut,layers) vacc_matrix = calculate_vaccine_matrix(layers, N_groups) @@ -388,7 +386,6 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp logger.info(f"Starting the heuristic_maxsave function with source node {source}, budget {budget}, targets: {targets}, and spreading: {spreading}") clean_graph(Graph) - #display_graph(Graph) local_targets = targets.copy() infected_nodes = [] vaccinated_nodes = [] @@ -397,7 +394,6 @@ def heuristic_maxsave(Graph:nx.DiGraph, budget:int, source:int, targets:list, sp can_spread = True Graph.nodes[source]['status'] = Status.INFECTED.value infected_nodes.append(source) - #display_graph(Graph) time_step = 1 while can_spread: diff --git a/networkz/algorithms/approximation/firefighter_problem/Utils.py b/networkz/algorithms/approximation/firefighter_problem/Utils.py index 10344a4..40dd340 100644 --- a/networkz/algorithms/approximation/firefighter_problem/Utils.py +++ b/networkz/algorithms/approximation/firefighter_problem/Utils.py @@ -32,14 +32,6 @@ class Status(Enum): VACCINATED = "vaccinated" DIRECTLY_VACCINATED = "directly vaccinated" - -node_colors = { - 'vulnerable': 'gray', - 'infected': 'red', - 'vaccinated': 'blue', - 'directly vaccinated': 'green', - 'default' : "#00FFD0" -} logger = logging.getLogger('firefighter_problem') # ============================ Validation Functions ============================ @@ -368,7 +360,6 @@ def spread_virus(graph:nx.DiGraph, infected_nodes:list) -> bool: logger.debug("SPREAD VIRUS: Node " + f'{neighbor}' + " has been infected from node " + f'{node}') infected_nodes.clear() - #display_graph(graph) for node in new_infected_nodes: infected_nodes.append(node) return bool(infected_nodes) @@ -404,7 +395,6 @@ def spread_vaccination(graph:nx.DiGraph, vaccinated_nodes:list) -> None: logger.debug("SPREAD VACCINATION: Node " + f'{neighbor}' + " has been vaccinated from node " + f'{node}') vaccinated_nodes.clear() - #display_graph(graph) for node in new_vaccinated_nodes: vaccinated_nodes.append(node) return @@ -430,7 +420,6 @@ def vaccinate_node(graph:nx.DiGraph, node:int) -> None: """ graph.nodes[node]['status'] = Status.DIRECTLY_VACCINATED.value logger.info("Node " + f'{node}' + " has been directly vaccinated") - #display_graph(graph) return def clean_graph(graph:nx.DiGraph) -> None: @@ -535,7 +524,6 @@ def create_st_graph(graph:nx.DiGraph, targets:list, new_target:str) -> nx.DiGrap G.add_node(new_target, status = Status.VULNERABLE.value) for node in targets: G.add_edge(node, new_target) - #display_graph(G) logger.info(f"Done creating a s-t graph") return G @@ -816,24 +804,6 @@ def find_best_neighbor(graph: nx.DiGraph, infected_nodes: list, remaining_target # =========================== End Heuristic Utilities ================================ # =========================== General Utilities ====================================== -def display_graph(graph:nx.DiGraph) -> None: - """ - Display the graph using Matplotlib. - - Parameters - ---------- - graph : nx.DiGraph - Directed graph. - """ - pos = nx.shell_layout(graph) - colors = [node_colors.get(data.get('status', 'default'), 'default') for node, data in graph.nodes(data=True)] - nx.draw(graph, pos, node_color=colors, with_labels=True, font_weight='bold') - - if nx.get_edge_attributes(graph, 'weight'): - edge_labels = nx.get_edge_attributes(graph, 'weight') - nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels) - plt.show() - return def parse_json_to_networkx(json_data): """ From 56968b457b0c1200e1f9debe13b4272b2b08b390 Mon Sep 17 00:00:00 2001 From: Almog-David Date: Sat, 31 Aug 2024 20:39:08 +0300 Subject: [PATCH 7/7] minor changes in readme --- README.md | 32 ++++++++++++++------------------ 1 file changed, 14 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index b25d3f0..187da5c 100644 --- a/README.md +++ b/README.md @@ -2,11 +2,11 @@ NetworkZ is a library of graph algorithms in Python. It is an extension of the [NetworkX](https://github.com/networkx/networkx). It contains (by import) everything that is in NetworkX, plus some additional algorithms that were submitted into NetworkX but not merged yet. Currently, NetworkZ contains the following additional algorithms: -* [Rank-maximal matching](networkz/algorithms/bipartite/rank_maximal_matching.py): by Oriya Alperin, Liel Vaknin and Amiel Lejzor. -* [Maximum-weight fractional matching](networkz/algorithms/max_weight_fractional_matching.py): by Oriya Alperin, Liel Vaknin and Amiel Lejzor. -* [Social-aware coalition formation](networkz/algorithms/approximation/coalition_formation.py) - by Victor Kushnir. -* [Minimum cut on a graph with node capacity](networkz/algorithms/max_flow_with_node_capacity.py): by Yuval Bubnovsky, Almog David and Shaked Levi. -* [Several approximate solutions to the Firefighter problem](networkz/algorithms/approximation/firefighter_problem): by Yuval Bubnovsky, Almog David and Shaked Levi. +- [Rank-maximal matching](networkz/algorithms/bipartite/rank_maximal_matching.py): by Oriya Alperin, Liel Vaknin and Amiel Lejzor. +- [Maximum-weight fractional matching](networkz/algorithms/max_weight_fractional_matching.py): by Oriya Alperin, Liel Vaknin and Amiel Lejzor. +- [Social-aware coalition formation](networkz/algorithms/approximation/coalition_formation.py) - by Victor Kushnir. +- [Minimum cut on a graph with node capacity](networkz/algorithms/max_flow_with_node_capacity.py): by Yuval Bubnovsky, Almog David and Shaked Levi. +- [Several approximate solutions to the Firefighter problem](networkz/algorithms/approximation/firefighter_problem): by Yuval Bubnovsky, Almog David and Shaked Levi. ## Installation @@ -16,10 +16,10 @@ pip install networkz This installs the latest version of networkx, and the new algorithms added in networkz. - ## Usage ### Rank Maximal Matching + A rank-maximal matching is a matching that maximizes the number of agents who are matched to their 1st priority; subject to that, it maximizes the number of agents matched to their 2nd priority; and so on. ```python @@ -34,8 +34,8 @@ print(matching) See [demo website](https://rmm.csariel.xyz/) for more information. - ### Maximum-Weight Fractional Matching + Maximum-weight fractional matching is a graph optimization problem where the goal is to find a set of edges with maximum total weight, allowing for fractional inclusion of edges. ```python @@ -48,6 +48,7 @@ print(F) ``` ### Approximating The Fire-Fighter Problem + The Firefighter problem models the case where a diffusive process such as an infection (or an idea, a computer virus, a fire) is spreading through a network, and our goal is to contain this infection by using targeted vaccinations. Networkz implements several algorithms to approximate solutions for the fire-fighter problems in 2 models: spreading & non-spreading, where the virus/fire always spreads but the spread of vaccination is dependant on the model. Under each such model, we are intrested in two problem types: MaxSave (save as many nodes from the target list given a budget) and MinBudget (What is the minimum budget needed to save all of the node target list) @@ -55,22 +56,23 @@ Networkz implements several algorithms to approximate solutions for the fire-fig ```python import networkz as nx G = nx.DiGraph() -G.add_nodes_from([0,1,2,3,4,5,6], status="vulnerable") +G.add_nodes_from([0,1,2,3,4,5,6]) G.add_edges_from([(0,1),(0,2),(1,2),(1,4),(2,3),(2,6),(3,5)]) -strategy, saved_nodes = nx.spreading_maxsave(G, budget = 1, source = 0, targets = [1,2,3,4,5,6]) +strategy, saved_nodes = nx.spreading_maxsave(G, budget = 1, source = 0, targets = [1,2,3,4,5,6]) # Will return ([(2, 1), (4, 2)], {2, 3, 4, 5, 6}) -min_budget, strategy = nx.spreading_minbudget(G, source = 0,targets = [1,2,3,4,5,6]) +min_budget, strategy = nx.spreading_minbudget(G, source = 0,targets = [1,2,3,4,5,6]) min_budget, strategy = nx.non_spreading_minbudget(G, source = 0,targets = [1,2,3,4,5,6]) # Both will return (2, [(1, 1), (2, 1)]) ``` Another algorithm which is implemented in networkz is MinBudget in a non-spreading model (vaccine doesn't spread), running on a directed-layered network graph: + ```python import networkz as nx G = nx.DiGraph() -G.add_nodes_from([0,1,2,3,4,5], status="vulnerable") +G.add_nodes_from([0,1,2,3,4,5]) G.add_edges_from([(0,1),(0,2),(1,3),(1,4),(1,5),(2,3),(2,4),(2,5),(3,5),(4,5)]) # The algorithm will check if this is actually a directed-layered network graph @@ -83,7 +85,7 @@ The library also implements two local-search based heuristic algorithms (MaxSave ```python import networkz as nx G = nx.DiGraph() -G.add_nodes_from([0, 1, 2, 3], status="vulnerable") +G.add_nodes_from([0, 1, 2, 3]) G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3)]) strategy, saved_nodes = nx.heuristic_maxsave(G, budget = 1, source = 0, targets = [1, 2, 3], spreading = False) # ([(1, 1)], {1, 3}) min_budget, strategy = nx.heuristic_minbudget(G, source = 0, targets = [1, 2, 3], spreading = True) # (2, [(1, 1), (2, 1)]) @@ -95,14 +97,8 @@ See the [algorithms in actions](https://the-firefighters.github.io/WebsiteGit/) (TODO) - ## Contribution Any additions or bug-fixes to `networkx` should first be submitted there, according to the [NetworkX Contributor Guide](https://github.com/networkx/networkx/blob/main/CONTRIBUTING.rst). If the pull-request is not handled, you are welcome to submit it here too. - - - - -