---
license: cc-by-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- chemistry
- medical
- finance
size_categories:
- 10B<n<100B
---

# WikiDBGraph Dataset

This document provides an overview of the datasets associated with the research paper on WikiDBGraph, a large-scale graph of interconnected relational databases.

**Description:**
WikiDBGraph is a novel, large-scale graph where each node represents a relational database, and edges signify identified correlations or similarities between these databases. It is constructed from 100,000 real-world-like databases derived from Wikidata. The graph is enriched with a comprehensive set of node (database) and edge (inter-database relationship) properties, categorized into structural, semantic, and statistical features.

**Source:**
WikiDBGraph is derived from the WikiDBs corpus (see below). The inter-database relationships (edges) are primarily identified using a machine learning model trained to predict database similarity based on their schema embeddings, significantly expanding upon the explicitly known links in the source data.

**Key Characteristics:**
* **Nodes:** 100,000 relational databases.
* **Edges:** Millions of identified inter-database relationships (the exact number depends on the similarity threshold $\tau$ used for graph construction).
* **Node Properties:** Include database-level structural details (e.g., number of tables, columns, foreign keys), semantic information (e.g., pre-computed embeddings, topic categories from Wikidata, community IDs), and statistical measures (e.g., database size, total rows, column cardinalities, column entropies).
* **Edge Properties:** Include structural similarity (e.g., Jaccard index on table/column names, Graph Edit Distance based metrics), semantic relatedness (e.g., cosine similarity of embeddings, prediction confidence), and statistical relationships (e.g., KL divergence of shared column distributions).

**Usage in this Research:**
WikiDBGraph is the primary contribution of this paper. It is used to:
* Demonstrate a methodology for identifying and representing inter-database relationships at scale.
* Provide a rich resource for studying the landscape of interconnected databases.
* Serve as a foundation for experiments in collaborative learning, showcasing its utility in feature-overlap and instance-overlap scenarios.

**Availability:**
* **Dataset (WikiDBGraph):** The WikiDBGraph dataset, including the graph structure (edge lists for various thresholds $\tau$) and node/edge property files, will be made publicly available. (Please specify the URL here, e.g., Zenodo, GitHub, or your project website).
* **Code:** The code used for constructing WikiDBGraph from WikiDBs, generating embeddings, and running the experiments will be made publicly available. (Please specify the URL here, e.g., GitHub repository).

**License:**
* **WikiDBGraph Dataset:** Creative Commons Attribution 4.0 International (CC BY 4.0).
* **Associated Code:** Apache License 2.0.

**How to Use WikiDBGraph:**
The dataset is provided as a collection of files, categorized as follows:

**A. Graph Structure Files:**
These files define the connections (edges) between databases (nodes) for different similarity thresholds ($\tau$).
* **`filtered_edges_threshold_0.93.csv`**, **`filtered_edges_threshold_0.94.csv`**, **`filtered_edges_threshold_0.96.csv`**:
    * **Meaning:** CSV files representing edge lists. Each row typically contains a pair of database IDs that are connected at the specified similarity threshold.
    * **How to Load:** Use standard CSV parsing libraries (e.g., `pandas.read_csv()` in Python). These edge lists can then be used to construct graph objects in libraries like NetworkX (`nx.from_pandas_edgelist()`) or igraph.
* **`filtered_edges_0.94_with_confidence.csv`**:
    * **Meaning:** An edge list in CSV format for $\tau=0.94$, which also includes the confidence score (similarity score) for each edge.
    * **How to Load:** Similar to other CSV edge lists, using `pandas`. The confidence score can be used as an edge weight.
* **`graph_raw_0.93.dgl`**, **`graph_raw_0.94.dgl`**, **`graph_raw_0.96.dgl`**:
    * **Meaning:** These are graph objects serialized in the Deep Graph Library (DGL) format for different thresholds. They likely contain the basic graph structure (nodes and edges).
    * **How to Load `.dgl` files:** DGL provides functions to save and load graph objects. You would typically use:
        ```python
        import dgl
        # Example for loading a graph
        graphs, _ = dgl.load_graphs('graph_raw_0.94.dgl')
        g = graphs[0] # Typically, load_graphs returns a list of graphs
        ```
* **`graph_with_properties_0.94.dgl`**:
    * **Meaning:** A DGL graph object for $\tau=0.94$ that includes node and/or edge properties directly embedded within the graph structure. This is convenient for direct use in DGL-based graph neural network models.
    * **How to Load:** Same as other `.dgl` files using `dgl.load_graphs()`. Node features can be accessed via `g.ndata` and edge features via `g.edata`.

**B. Node Property Files:**
These CSV files provide various features and metadata for each database node.
* **`database_embeddings.pt`**:
    * **Meaning:** A PyTorch (`.pt`) file containing the pre-computed embedding vectors for each database. These are crucial semantic features.
    * **How to Load:** Use `torch.load('database_embeddings.pt')` in Python with PyTorch installed.
* **`node_structural_properties.csv`**:
    * **Meaning:** Contains structural characteristics of each database (e.g., number of tables, columns, foreign key counts).
* **`column_cardinality.csv`**:
    * **Meaning:** Statistics on the number of unique values (cardinality) for columns within each database.
* **`column_entropy.csv`**:
    * **Meaning:** Entropy values calculated for columns within each database, indicating data diversity.
* **`data_volume.csv`**:
    * **Meaning:** Information regarding the size or volume of data in each database (e.g., total rows, file size).
* **`cluster_assignments_dim2_sz100_msNone.csv`**:
    * **Meaning:** Cluster labels assigned to each database after dimensionality reduction (e.g., t-SNE) and clustering (e.g., HDBSCAN).
* **`community_assignment.csv`**:
    * **Meaning:** Community labels assigned to each database based on graph community detection algorithms (e.g., Louvain).
* **`tsne_embeddings_dim2.csv`**:
    * **Meaning:** 2-dimensional projection of database embeddings using t-SNE, typically used for visualization.
* **How to Load CSV Node Properties:** Use `pandas.read_csv()` in Python. The resulting DataFrames can be merged or used to assign attributes to nodes in a graph object.

**C. Edge Property Files:**
These CSV files provide features for the relationships (edges) between databases.
* **`edge_embed_sim.csv`**:
    * **Meaning:** Contains the embedding-based similarity scores (EmbedSim) for connected database pairs. This might be redundant if confidence is in `filtered_edges_..._with_confidence.csv` but could be a global list of all pairwise similarities above a certain initial cutoff.
* **`edge_structural_properties_GED_0.94.csv`**:
    * **Meaning:** Contains structural similarity metrics (potentially Graph Edit Distance or related measures) for edges in the graph constructed with $\tau=0.94$.
* **How to Load CSV Edge Properties:** Use `pandas.read_csv()`. These can be used to assign attributes to edges in a graph object.

**D. Other Analysis Files:**
* **`distdiv_results.csv`**:
    * **Meaning:** Likely contains results from a specific distance or diversity analysis performed on the databases or their embeddings. The exact nature would be detailed in the paper or accompanying documentation.
    * **How to Load:** As a CSV file using `pandas`.

Detailed instructions on the specific schemas of these CSV files, precise content of `.pt` and `.dgl` files, and example usage scripts will be provided in the code repository and full dataset documentation upon release.