SMP2024大模型图（Graph）分析挑战赛

        "Section ID": "maximum_branching",        "Description": [            "Returns a maximum branching from G."        ],        "Field List": {            "Parameters:": {                "G : (multi)digraph-like": "The graph to be searched.",                "attr : str": "The edge attribute used to in determining optimality.",                "default : float": "The value of the edge attribute used if an edge does not have\nthe attributeattr.",                "preserve_attrs : bool": "If True, preserve the other attributes of the original graph (that are not\npassed toattr)",                "partition : str": "The key for the edge attribute containing the partition\ndata on the graph. Edges can be included, excluded or open using theEdgePartitionenum."            },            "Returns:": {                "B : (multi)digraph-like": "A maximum branching."            },            "Methods": []        },        "Rubrics": {}    }

{        "Section_id": "RectangleDrawer",        "Description": "Static class which draws rectangular vertices",        "Field List": {            "Methods": {                "draw_path": {                    "Description": "overrides igraph.drawing.shapes.ShapeDrawer.draw_path\nDraws a rectangle-shaped path on the Cairo context without stroking or filling it.",                    "Paramters": {},                    "Return": [],                    "References": [],                    "Rasises": {},                    "See Also": "ShapeDrawer.draw_path",                    "example": []                },                "intersection_point": {                    "Description": "overrides igraph.drawing.shapes.ShapeDrawer.intersection_point\nDetermines where the rectangle centered at (center_x, center_y) having the given width and height intersects with a line drawn from (source_x, source_y) to (center_x, center_y).",                    "Paramters": {},                    "Return": [],                    "References": [],                    "Rasises": {},                    "See Also": "ShapeDrawer.intersection_point",                    "example": []                }            },            "property": {}        },        "Rubric": {            "Example": []        }}

{        "Section_id": "EgoNetSplitter",        "Description": "An implementation of \u201cEgo-Splitting\u201dfrom the KDD \u201817 paper \u201cEgo-Splitting Framework: from Non-Overlapping to Overlapping Clusters\u201d. The tool first createsthe ego-nets of nodes. A persona-graph is created which is clustered by the Louvain method. The resulting overlappingcluster memberships are stored as a dictionary.",        "Field List": {            "Parameters": {                "resolution(float)": "Resolution parameter of Python Louvain. Default 1.0.",                "seed(int)": "Random seed value. Default is 42.",                "weight(str)": "the key in the graph to use as weight. Default to \u2018weight\u2019. Specify None to force using an unweighted version of the graph."            },            "Methods": [                {                    "fit": {                        "Description": "Fitting an Ego-Splitter clustering model.",                        "Arg types:": {                            "graph(NetworkX graph)": "The graph to be clustered."                        }                    }                },                {                    "get_memberships": {                        "Description": "Getting the cluster membership of nodes.",                        "Return types:": {                            "memberships(dictionary of lists)": "Cluster memberships."                        }                    }                }            ]        }}

{        "Section_id": "RandomNodeSampler",        "Description": "An implementation of random node sampling. Nodes are sampled with uniform probability. For details about the algorithm see this paper.",        "Field List": {            "Parameters": {                "number_of_nodes": "Number of nodes. Default is 100.",                "seed": "Random seed. Default is 42."            },            "Methods": {                "sample": {                    "Description": "Sampling nodes randomly.",                    "Arg types": {                        "graph": "NetworkX or NetworKit graph - The graph to be sampled from."                    },                    "Return types": {                        "new_graph": "NetworkX or NetworKit graph - The graph of sampled nodes."                    }                }            }        }    }

{        "Section_id": "read_community_json",        "Description": "Read community list from JSON file.\n",        "Parameters": {            "path": "input filename",            "compress": "wheter the file is in a copress format, default False"        },        "Return": [            "a Clustering object\n"        ],        "Example": [            "import networkx as nx\nfrom cdlib import algorithms, readwrite\ng = nx.karate_club_graph()\ncoms = algorithms.louvain(g)\nreadwrite.write_community_json(coms, \"communities.json\")\nreadwrite.read_community_json(coms, \"communities.json\")"        ],        "References": []    }

{"ID": 1, "question": "Imagine we're constructing a new activity scheduling system for our community rehabilitation center, aimed at promoting social interaction for our clients through various group activities. The activities are represented by nodes, and the direct pairwise overlaps in schedulingdue to shared participants or resourcesare represented by edges between them. Our current activity network is comprised of the following connections: [(0, 1), (0, 2), (1, 2), (1, 3), (1, 4), (4, 5), (3, 6), (5, 7), (3, 8), (5, 9), (3, 10)].To enhance the effectiveness of our program, we want to ensure that our activity schedule is conflict-free, enabling a seamless flow without overloading our clients or our resources. In other words, we're looking for an "Asteroidal Triple-free" (AT-free) structure within our activity network, a condition that ensures a more manageable and stress-free experience for participants as they transition from one activity to another.Could we utilize the 'is_at_free' feature of NetworkX to verify whether our planned activity network maintains the AT-free property? This will assist us in confirming that our activity schedule is optimally structured for the well-being of our clients.?"}

{"ID": 2, "question": "Captain, imagine you're tasked with reviewing the flight network efficiency for a new regional airline with a modest fleet. Presently, they have only three destinations, labeled as 1, 2, and 3. The airline operates direct flights resembling a simplified network: Flight 1 directly connects to both Flight 2 and Flight 3, yet there is no direct flight between Flight 2 and Flight 3. In aviation terms, the 'density' of this network measures the proportion of possible direct connections that are operational, between the trio of destinations.
To calculate the operational efficiency of this network or its 'density', you would be provided with the current route graph of the airline. The node set acknowledging the destinations would be [1, 2, 3], and the edge set that represents the direct connections would be [(1, 2), (1, 3)]. Captain, could you kindly compute the density of this graph using the density function in order to assess how effectively the airline is utilizing its potential for direct connections? This information would be pivotal for optimizing routing and ensuring the most streamlined service for your passengers. Please print the resulting network density as a part of your report." }