Category: A1; Team name: louisvl; Dataset: GraphUniverse

README.md

@@ -375,13 +375,17 @@ Specially useful in pre-processing steps, these are the general data manipulatio
 | IMDB-BIN | Classification | Graph-level classification. | [Source](https://dl.acm.org/doi/10.1145/2783258.2783417) |
 | IMDB-MUL | Classification | Graph-level classification. | [Source](https://dl.acm.org/doi/10.1145/2783258.2783417) |
 | REDDIT | Classification | Graph-level classification. | [Source](https://proceedings.neurips.cc/paper_files/paper/2017/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf) |
+| GraphUniverse-IND | Classification | Synthetic Generator Inductive Node Classification. | [Source](https://openreview.net/forum?id=jRWxvQnqUt&noteId=jRWxvQnqUt) |
+| GraphUniverse-TRA | Classification | Synthetic Generator Transductive Node Classification. | [Source](https://openreview.net/forum?id=jRWxvQnqUt&noteId=jRWxvQnqUt) |
 | Amazon | Classification | Heterophilic dataset. | [Source](https://arxiv.org/pdf/1205.6233) |
 | Minesweeper | Classification | Heterophilic dataset. | [Source](https://arxiv.org/pdf/2302.11640) |
 | Empire | Classification | Heterophilic dataset. | [Source](https://arxiv.org/pdf/2302.11640) |
 | Tolokers | Classification | Heterophilic dataset. | [Source](https://arxiv.org/pdf/2302.11640) |
 | US-county-demos | Regression | In turn each node attribute is used as the target label. | [Source](https://arxiv.org/pdf/2002.08274) |
 | ZINC | Regression | Graph-level regression. | [Source](https://pubs.acs.org/doi/10.1021/ci3001277) |
 
+**Remark:** GraphUniverse is a synthetic graph generator for community-structured data, enabling control over graph properties like homophily, feature-signal and degree structure. Live Demo: [Demo](https://graphuniverse.streamlit.app/). Package release: [PyPi](https://pypi.org/project/graph-universe/0.1.2/). GitHub repository: [Repo](https://github.com/LouisVanLangendonck/GraphUniverse).
+
 
 ### Simplicial
 | Dataset | Task | Description | Reference |

configs/dataset/graph/graphuniverse_inductive.yaml

@@ -0,0 +1,51 @@
+
+loader:
+  _target_: topobench.data.loaders.GraphUniverseDatasetLoader
+  parameters: 
+    data_domain: graph
+    data_type: GraphUniverse
+    data_name: GraphUniverse
+    data_dir: ${paths.data_dir}/${dataset.loader.parameters.data_domain}/${dataset.loader.parameters.data_type}
+    generation_parameters:
+      task: community_detection
+      universe_parameters:
+        K: 20
+        feature_dim: 15
+        center_variance: 0.2
+        cluster_variance: 0.4
+        edge_propensity_variance: 1.0
+        seed: 42
+      family_parameters:
+        n_graphs: 1000
+        n_nodes_range: [50, 200]
+        n_communities_range: [3, 7]
+        homophily_range: [0.4, 0.8]
+        avg_degree_range: [1.0, 2.0]
+        degree_separation_range: [0.5, 1.0]
+        power_law_exponent_range: [1.5, 2.5]
+        seed: ${dataset.loader.parameters.generation_parameters.universe_parameters.seed}
+
+
+# Dataset parameters
+parameters:
+  num_features: ${dataset.loader.parameters.generation_parameters.universe_parameters.feature_dim}
+  num_classes: ${dataset.loader.parameters.generation_parameters.universe_parameters.K}
+  task: classification
+  loss_type: cross_entropy
+  monitor_metric: accuracy
+  task_level: node
+
+#splits
+split_params:
+  learning_setting: inductive
+  data_split_dir: ${dataset.loader.parameters.data_dir}/data_splits
+  data_seed: 0
+  split_type: random #'k-fold' # either "k-fold" or "random" strategies
+  k: 10 # for "k-fold" Cross-Validation
+  train_prop: 0.7 # for "random" strategy splitting
+
+# Dataloader parameters
+dataloader_params:
+  batch_size: 16 
+  num_workers: 0
+  pin_memory: False

configs/dataset/graph/graphuniverse_transductive.yaml

@@ -0,0 +1,51 @@
+
+loader:
+  _target_: topobench.data.loaders.GraphUniverseDatasetLoader
+  parameters: 
+    data_domain: graph
+    data_type: GraphUniverse
+    data_name: GraphUniverse
+    data_dir: ${paths.data_dir}/${dataset.loader.parameters.data_domain}/${dataset.loader.parameters.data_type}
+    generation_parameters:
+      task: community_detection
+      universe_parameters:
+        K: 10
+        feature_dim: 15
+        center_variance: 0.2
+        cluster_variance: 0.5
+        edge_propensity_variance: 0.5
+        seed: 42
+      family_parameters:
+        n_graphs: 1
+        n_nodes_range: [5000, 5000]
+        n_communities_range: [10, 10]
+        homophily_range: [0.5, 0.5]
+        avg_degree_range: [2.5, 2.5]
+        degree_separation_range: [0.5, 0.5]
+        power_law_exponent_range: [2.5, 2.5]
+        seed: ${dataset.loader.parameters.generation_parameters.universe_parameters.seed}
+
+
+# Dataset parameters
+parameters:
+  num_features: ${dataset.loader.parameters.generation_parameters.universe_parameters.feature_dim}
+  num_classes: ${dataset.loader.parameters.generation_parameters.universe_parameters.K}
+  task: classification
+  loss_type: cross_entropy
+  monitor_metric: accuracy
+  task_level: node
+
+#splits
+split_params:
+  learning_setting: transductive
+  data_split_dir: ${dataset.loader.parameters.data_dir}/data_splits
+  data_seed: 0
+  split_type: random #'k-fold' # either "k-fold" or "random" strategies
+  k: 10 # for "k-fold" Cross-Validation
+  train_prop: 0.7 # for "random" strategy splitting
+
+# Dataloader parameters
+dataloader_params:
+  batch_size: 1
+  num_workers: 0
+  pin_memory: False

pyproject.toml

@@ -55,6 +55,7 @@ dependencies=[
     "rootutils",
     "topomodelx @ git+https://github.com/pyt-team/TopoModelX.git",
     "toponetx @ git+https://github.com/pyt-team/TopoNetX.git@c378925",
+    "graph-universe==0.1.2",
     "lightning==2.4.0",
     "torch-scatter",
     "torch-sparse",

topobench/data/loaders/graph/graph_universe_loader.py

@@ -0,0 +1,67 @@
+"""Loaders for GraphUniverse [1] datasets.
+
+[1] "GraphUniverse: Enabling Systematic Evaluation of Inductive Generalization" by Louis Van Langendonck and Guillermo Bernardez and Nina Miolane and Pere Barlet-Ros
+Accepted at The Fourteenth International Conference on Learning Representations, 2026},
+https://openreview.net/forum?id=jRWxvQnqUt
+"""
+
+from graph_universe import GraphUniverseDataset
+from omegaconf import DictConfig
+from torch_geometric.data import Data, Dataset
+
+from topobench.data.loaders.base import AbstractLoader
+
+
+class GraphUniverseDatasetLoader(AbstractLoader):
+    """Load Graph Universe datasets.
+
+    Parameters
+    ----------
+    parameters : DictConfig
+        Configuration parameters containing:
+            - data_dir: Root directory for data
+            - data_name: Name of the dataset
+            - data_type: Type of the dataset (e.g., "graph_classification")
+    """
+
+    def __init__(self, parameters: DictConfig) -> None:
+        super().__init__(parameters)
+
+    def load_dataset(self) -> Dataset:
+        """Load Graph Universe dataset.
+
+        Returns
+        -------
+        Dataset
+            The loaded Graph Universe dataset.
+
+        Raises
+        ------
+        RuntimeError
+            If dataset loading fails.
+        """
+
+        dataset = GraphUniverseDataset(
+            root=str(self.root_data_dir),
+            parameters=self.parameters["generation_parameters"]
+        )
+
+        return dataset
+
+    def load(self, **kwargs) -> tuple[Data, str]:
+        """Load data.
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Additional keyword arguments.
+
+        Returns
+        -------
+        tuple[torch_geometric.data.Data, str]
+            Tuple containing the loaded data and the data directory.
+        """
+        dataset = self.load_dataset(**kwargs)
+        data_dir = dataset.raw_dir
+
+        return dataset, data_dir