graph_cases.py

from collections import defaultdict

import backend as F
import dgl
import networkx as nx
import numpy as np
import scipy.sparse as ssp

case_registry = defaultdict(list)


def register_case(labels):
    def wrapper(fn):
        for lbl in labels:
            case_registry[lbl].append(fn)
        fn.__labels__ = labels
        return fn

    return wrapper


def get_cases(labels=None, exclude=[]):
    """Get all graph instances of the given labels."""
    cases = set()
    if labels is None:
        # get all the cases
        labels = case_registry.keys()
    for lbl in labels:
        for case in case_registry[lbl]:
            if not any([l in exclude for l in case.__labels__]):
                cases.add(case)
    return [fn() for fn in cases]


@register_case(["bipartite", "zero-degree"])
def bipartite1():
    return dgl.heterograph(
        {("_U", "_E", "_V"): ([0, 0, 0, 2, 2, 3], [0, 1, 4, 1, 4, 3])}
    )


@register_case(["bipartite"])
def bipartite_full():
    return dgl.heterograph(
        {
            ("_U", "_E", "_V"): (
                [0, 0, 0, 0, 1, 1, 1, 1],
                [0, 1, 2, 3, 0, 1, 2, 3],
            )
        }
    )


@register_case(["homo"])
def graph0():
    return dgl.graph(
        (
            [0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 9],
            [4, 5, 1, 2, 4, 7, 9, 8, 6, 4, 1, 0, 1, 0, 2, 3, 5],
        )
    )


@register_case(["homo", "zero-degree", "homo-zero-degree"])
def bipartite1():
    return dgl.graph(([0, 0, 0, 2, 2, 3], [0, 1, 4, 1, 4, 3]))


@register_case(["homo", "has_feature"])
def graph1():
    g = dgl.graph(
        (
            [0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 9],
            [4, 5, 1, 2, 4, 7, 9, 8, 6, 4, 1, 0, 1, 0, 2, 3, 5],
        ),
        device=F.cpu(),
    )
    g.ndata["h"] = F.copy_to(F.randn((g.number_of_nodes(), 2)), F.cpu())
    g.edata["w"] = F.copy_to(F.randn((g.number_of_edges(), 3)), F.cpu())
    return g


@register_case(["homo", "has_scalar_e_feature"])
def graph1():
    g = dgl.graph(
        (
            [0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 9],
            [4, 5, 1, 2, 4, 7, 9, 8, 6, 4, 1, 0, 1, 0, 2, 3, 5],
        ),
        device=F.cpu(),
    )
    g.ndata["h"] = F.copy_to(F.randn((g.number_of_nodes(), 2)), F.cpu())
    g.edata["scalar_w"] = F.copy_to(
        F.abs(F.randn((g.number_of_edges(),))), F.cpu()
    )
    return g


@register_case(["homo", "row_sorted"])
def graph2():
    return dgl.graph(
        (
            [0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 9],
            [4, 5, 1, 2, 4, 7, 9, 8, 6, 4, 1, 0, 1, 0, 2, 3, 5],
        ),
        row_sorted=True,
    )


@register_case(["homo", "row_sorted", "col_sorted"])
def graph3():
    return dgl.graph(
        (
            [0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 9],
            [1, 4, 5, 2, 4, 7, 8, 9, 1, 4, 6, 0, 0, 1, 2, 3, 5],
        ),
        row_sorted=True,
        col_sorted=True,
    )


@register_case(["hetero", "has_feature"])
def heterograph0():
    g = dgl.heterograph(
        {
            ("user", "plays", "game"): ([0, 1, 1, 2], [0, 0, 1, 1]),
            ("developer", "develops", "game"): ([0, 1], [0, 1]),
        },
        device=F.cpu(),
    )
    g.nodes["user"].data["h"] = F.copy_to(
        F.randn((g.number_of_nodes("user"), 3)), F.cpu()
    )
    g.nodes["game"].data["h"] = F.copy_to(
        F.randn((g.number_of_nodes("game"), 2)), F.cpu()
    )
    g.nodes["developer"].data["h"] = F.copy_to(
        F.randn((g.number_of_nodes("developer"), 3)), F.cpu()
    )
    g.edges["plays"].data["h"] = F.copy_to(
        F.randn((g.number_of_edges("plays"), 1)), F.cpu()
    )
    g.edges["develops"].data["h"] = F.copy_to(
        F.randn((g.number_of_edges("develops"), 5)), F.cpu()
    )
    return g


@register_case(["batched", "homo"])
def batched_graph0():
    g1 = dgl.add_self_loop(dgl.graph(([0, 1, 2], [1, 2, 3])))
    g2 = dgl.add_self_loop(dgl.graph(([1, 1], [2, 0])))
    g3 = dgl.add_self_loop(dgl.graph(([0], [1])))
    return dgl.batch([g1, g2, g3])


@register_case(["block", "bipartite", "block-bipartite"])
def block_graph0():
    g = dgl.graph(([2, 3, 4], [5, 6, 7]), num_nodes=100)
    g = g.to(F.cpu())
    return dgl.to_block(g)


@register_case(["block"])
def block_graph1():
    g = dgl.heterograph(
        {
            ("user", "plays", "game"): ([0, 1, 2], [1, 1, 0]),
            ("user", "likes", "game"): ([1, 2, 3], [0, 0, 2]),
            ("store", "sells", "game"): ([0, 1, 1], [0, 1, 2]),
        },
        device=F.cpu(),
    )
    return dgl.to_block(g)


@register_case(["clique"])
def clique():
    g = dgl.graph(([0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]))
    return g


def random_dglgraph(size):
    return dgl.DGLGraph(nx.erdos_renyi_graph(size, 0.3))


def random_graph(size):
    return dgl.from_networkx(nx.erdos_renyi_graph(size, 0.3))


def random_bipartite(size_src, size_dst):
    return dgl.bipartite_from_scipy(
        ssp.random(size_src, size_dst, 0.1),
        utype="_U",
        etype="_E",
        vtype="V",
    )


def random_block(size):
    g = dgl.from_networkx(nx.erdos_renyi_graph(size, 0.1))
    return dgl.to_block(g, np.unique(F.zerocopy_to_numpy(g.edges()[1])))


@register_case(["two_hetero_batch"])
def two_hetero_batch():
    g1 = dgl.heterograph(
        {
            ("user", "follows", "user"): ([0, 1], [1, 2]),
            ("user", "follows", "developer"): ([0, 1], [1, 2]),
            ("user", "plays", "game"): ([0, 1, 2, 3], [0, 0, 1, 1]),
        }
    )
    g2 = dgl.heterograph(
        {
            ("user", "follows", "user"): ([0, 1], [1, 2]),
            ("user", "follows", "developer"): ([0, 1], [1, 2]),
            ("user", "plays", "game"): ([0, 1, 2], [0, 0, 1]),
        }
    )
    return [g1, g2]


@register_case(["two_hetero_batch"])
def two_hetero_batch_with_isolated_ntypes():
    g1 = dgl.heterograph(
        {
            ("user", "follows", "user"): ([0, 1], [1, 2]),
            ("user", "follows", "developer"): ([0, 1], [1, 2]),
            ("user", "plays", "game"): ([0, 1, 2, 3], [0, 0, 1, 1]),
        },
        num_nodes_dict={"user": 4, "game": 2, "developer": 3, "platform": 2},
    )
    g2 = dgl.heterograph(
        {
            ("user", "follows", "user"): ([0, 1], [1, 2]),
            ("user", "follows", "developer"): ([0, 1], [1, 2]),
            ("user", "plays", "game"): ([0, 1, 2], [0, 0, 1]),
        },
        num_nodes_dict={"user": 3, "game": 2, "developer": 3, "platform": 3},
    )
    return [g1, g2]