Update NASA Knowledge Graph Dataset to version v1.1.0

README.md

@@ -12,27 +12,35 @@ tags:
 
 # NASA Knowledge Graph Dataset
 
+## 🚀 **Update Notice**
+**Version v1.1.0 - Updated on: 2025-02-19**
+
+This is the updated version of the NASA Knowledge Graph Dataset, reflecting new datasets, publications, and relationships as of February 2025. This version includes improved node connectivity, updated embeddings, and refined dataset linking.
+
+---
+
 ## Dataset Summary
 
-The **NASA Knowledge Graph Dataset** is an expansive graph-based dataset designed to integrate and interconnect information about satellite datasets, scientific publications, instruments, platforms, projects, data centers, and science keywords. This knowledge graph is particularly focused on datasets managed by NASA's Distributed Active Archive Centers (DAACs), which are NASA's data repositories responsible for archiving and distributing scientific data. In addition to NASA DAACs, the graph includes datasets from 184 data providers worldwide, including various government agencies and academic institutions.
+The **NASA Knowledge Graph Dataset** is an expansive graph-based dataset designed to integrate and interconnect information about satellite datasets, scientific publications, instruments, platforms, projects, data centers, and science keywords. This knowledge graph focuses on datasets managed by NASA's Distributed Active Archive Centers (DAACs), along with datasets from 184+ data providers worldwide, including government agencies and academic institutions.
 
-The primary goal of the NASA Knowledge Graph is to bridge scientific publications with the datasets they reference, facilitating deeper insights and research opportunities within NASA's scientific and data ecosystem. By organizing these interconnections within a graph structure, this dataset enables advanced analyses, such as discovering influential datasets, understanding research trends, and exploring scientific collaborations.
+The graph bridges scientific publications with the datasets they reference, enabling advanced analyses such as discovering influential datasets, understanding research trends, and exploring scientific collaborations.
+
+---
 
 ## Data Integrity
 
 Each file in the dataset has a SHA-256 checksum to verify its integrity:
 
-| File Name                  | SHA-256 Checksum                                                          |
-|----------------------------|---------------------------------------------------------------------------|
-| `graph.cypher`             | `d78f7b166a86be3ceec16db75a1575dac95249af188b1d2e2adab7388a8e654a`        |
-| `graph.graphml`            | `a781e358b338a181db5081a50127325febab67dcb283cb4124c877bf06de439e`        |
-| `graph.json`               | `4794fd070953b3544ba3c841f13155f1cf8a1ca9abd1b240bbfaf482ce2cde32`        |
+| File Name      | SHA-256 Checksum                                                          |
+|----------------|---------------------------------------------------------------------------|
+| `graph.cypher` | `6aef9d433ea1179f0b006e9c402ca48d487e78cb193d4535e920d61c078cbae3`        |
+| `graph.graphml`| `3b653606e7ed6ee5e4367d2b4e8c4800240e1c9dc9520eec5803a2f3204ebe5a`        |
+| `graph.json`   | `8d49fc801f87cb43dfdadb3dc77a6e1b2b1e7e8044b46883f398b69a71dd3ac4`        |
 
 ### Verification
 
-To verify the integrity of each file, calculate its SHA-256 checksum and compare it with the hashes provided above.
+To verify file integrity, calculate the SHA-256 checksum and compare it with the provided hashes:
 
-You can use the following Python code to calculate the SHA-256 checksum:
 ```python
 import hashlib
 
@@ -42,483 +50,119 @@ def calculate_sha256(filepath):
         for byte_block in iter(lambda: f.read(4096), b""):
             sha256_hash.update(byte_block)
     return sha256_hash.hexdigest()
-```
-
-
-## Dataset Structure
-
-### Nodes and Properties
-
-The knowledge graph consists of seven main node types, each representing a different entity within NASA's ecosystem:
-
-#### 1. Dataset
-
-- **Description**: Represents satellite datasets, particularly those managed by NASA DAACs, along with datasets from other governmental and academic data providers. These nodes contain metadata and attributes specific to each dataset.
-- **Properties**:
-  - `globalId` (String): Unique identifier for the dataset.
-  - `doi` (String): Digital Object Identifier.
-  - `shortName` (String): Abbreviated name of the dataset.
-  - `longName` (String): Full name of the dataset.
-  - `abstract` (String): Brief summary of the dataset.
-  - `cmrId` (String): Common Metadata Repository ID.
-  - `daac` (String): Distributed Active Archive Center associated with the dataset.
-  - `temporalFrequency` (String): Frequency of data collection (e.g., daily, monthly).
-  - `temporalExtentStart` (Date): Start date of the dataset's temporal coverage.
-  - `temporalExtentEnd` (Date): End date of the dataset's temporal coverage.
-  - `nwCorner` (Geo-Coordinate): Northwest corner coordinate of spatial coverage.
-  - `seCorner` (Geo-Coordinate): Southeast corner coordinate of spatial coverage.
-  - `landingPageUrl` (URL): Webpage link to the dataset.
-  - `pagerank_global` (Float): PageRank score calculated using the natural graph direction.
-  - `fastrp_embedding_with_labels` (List<Float>): FastRP embedding vector considering node labels.
-
-#### 2. Publication
-
-- **Description**: This node type captures publications that reference or use datasets, particularly those from NASA DAACs and other included data providers. By linking datasets and publications, this node type enables analysis of scientific impact and research usage of NASA’s datasets.
-- **Properties**:
-  - `globalId` (String): Unique identifier for the publication.
-  - `DOI` (String): Digital Object Identifier.
-  - `title` (String): Title of the publication.
-  - `abstract` (String): Summary of the publication's content.
-  - `authors` (List<String>): List of authors.
-  - `year` (Integer): Year of publication.
-  - `pagerank_global` (Float): PageRank score.
-  - `fastrp_embedding_with_labels` (List<Float>): FastRP embedding vector.
-
-#### 3. ScienceKeyword
-
-- **Description**: Represents scientific keywords used to classify datasets and publications. Keywords provide topical context and aid in identifying research trends and related areas.
-- **Properties**:
-  - `globalId` (String): Unique identifier.
-  - `name` (String): Name of the science keyword.
-  - `pagerank_global` (Float): PageRank score.
-  - `fastrp_embedding_with_labels` (List<Float>): FastRP embedding vector.
-
-#### 4. Instrument
-
-- **Description**: Instruments used in data collection, often mounted on platforms like satellites. Instruments are linked to the datasets they help generate.
-- **Properties**:
-  - `globalId` (String): Unique identifier.
-  - `shortName` (String): Abbreviated name.
-  - `longName` (String): Full name.
-  - `pagerank_global` (Float): PageRank score.
-  - `fastrp_embedding_with_labels` (List<Float>): FastRP embedding vector.
-
-#### 5. Platform
-
-- **Description**: Platforms, such as satellites or airborne vehicles, that host instruments for data collection.
-- **Properties**:
-  - `globalId` (String): Unique identifier.
-  - `shortName` (String): Abbreviated name.
-  - `longName` (String): Full name.
-  - `Type` (String): Type of platform.
-  - `pagerank_global` (Float): PageRank score.
-  - `fastrp_embedding_with_labels` (List<Float>): FastRP embedding vector.
-
-#### 6. Project
-
-- **Description**: NASA projects associated with datasets, often indicating a funding source or collaborative initiative for data collection and research.
-- **Properties**:
-  - `globalId` (String): Unique identifier.
-  - `shortName` (String): Abbreviated name.
-  - `longName` (String): Full name.
-  - `pagerank_global` (Float): PageRank score.
-  - `fastrp_embedding_with_labels` (List<Float>): FastRP embedding vector.
-
-#### 7. DataCenter
-
-- **Description**: Represents data centers, primarily NASA DAACs, as well as other data providers in academia and government who manage and distribute datasets.
-- **Properties**:
-  - `globalId` (String): Unique identifier.
-  - `shortName` (String): Abbreviated name.
-  - `longName` (String): Full name.
-  - `url` (URL): Website of the data center.
-  - `pagerank_global` (Float): PageRank score.
-  - `fastrp_embedding_with_labels` (List<Float>): FastRP embedding vector.
-
-### Relationships
-
-The knowledge graph includes several relationship types that define how nodes are interconnected.
-
-#### 1. HAS_DATASET
-
-- **Description**: Connects a `DataCenter` to its `Dataset(s)`.
-- **Properties**: None.
-
-#### 2. OF_PROJECT
-
-- **Description**: Links a `Dataset` to a `Project`.
-- **Properties**: None.
-
-#### 3. HAS_PLATFORM
-
-- **Description**: Associates a `Dataset` with a `Platform`.
-- **Properties**: None.
-
-#### 4. HAS_INSTRUMENT
-
-- **Description**: Connects a `Platform` to an `Instrument`.
-- **Properties**: None.
-
-#### 5. HAS_SCIENCEKEYWORD
 
-- **Description**: Links a `Dataset` to a `ScienceKeyword`.
-- **Properties**: None.
-
-#### 6. HAS_SUBCATEGORY
-
-- **Description**: Defines hierarchical relationships between `ScienceKeyword` nodes.
-- **Properties**: None.
-
-#### 7. CITES
-
-- **Description**: Represents citation relationships between `Publication` nodes, indicating how research builds on previous work.
-- **Properties**: None.
+print(calculate_sha256("graph.json"))
+```
 
-#### 8. HAS_APPLIED_RESEARCH_AREA
+---
 
-- **Description**: Associates a `Publication` with a `ScienceKeyword`.
-- **Properties**: None.
+## Dataset Statistics
 
-## Statistics
+### Total Counts
 
-# Data Statistics
+| Type                 | Count     |
+|----------------------|-----------|
+| **Total Nodes**      | 145,047   |
+| **Total Relationships** | 406,444 |
 
-## Total Counts
-| Type                 | Count   |
-|----------------------|---------|
-| **Total Nodes**      | 135,764 |
-| **Total Relationships** | 365,857 |
+### Node Label Counts
 
-## Node Label Counts
 | Node Label        | Count    |
 |-------------------|----------|
-| Dataset           | 6,390    |
-| DataCenter        | 184      |
-| Project           | 333      |
-| Platform          | 442      |
-| Instrument        | 867      |
+| Publication       | 134,724  |
+| Dataset           | 6,817    |
 | ScienceKeyword    | 1,609    |
-| Publication       | 125,939  |
+| Instrument        | 897      |
+| Platform          | 451      |
+| Project           | 352      |
+| DataCenter        | 197      |
+
+### Relationship Label Counts
 
-## Relationship Label Counts
 | Relationship Label        | Count     |
 |---------------------------|-----------|
-| HAS_DATASET              | 9,017     |
-| OF_PROJECT               | 6,049     |
-| HAS_PLATFORM             | 9,884     |
-| HAS_INSTRUMENT           | 2,469     |
+| CITES                    | 208,429   |
+| HAS_APPLIEDRESEARCHAREA  | 119,695   |
+| USES_DATASET             | 25,814    |
+| HAS_SCIENCEKEYWORD       | 21,559    |
+| HAS_PLATFORM             | 10,393    |
+| HAS_DATASET              | 9,830     |
+| OF_PROJECT               | 6,375     |
+| HAS_INSTRUMENT           | 2,526     |
 | HAS_SUBCATEGORY          | 1,823     |
-| HAS_SCIENCEKEYWORD       | 20,436    |
-| CITES                    | 208,670   |
-| HAS_APPLIED_RESEARCH_AREA| 89,039    |
-| USES_DATASET             | 18,470    |
 
+---
 
-## Data Formats
-
-The Knowledge Graph Dataset is available in three formats:
-
-### 1. JSON
-
-- **File**: `graph.json`
-- **Description**: A hierarchical data format representing nodes and relationships. Each node includes its properties, such as `globalId`, `doi`, and `pagerank_global`.
-- **Usage**: Suitable for web applications and APIs, and for use cases where hierarchical data structures are preferred.
-
-#### Loading the JSON Format
+## Dataset Structure
 
-To load the JSON file into a graph database using Python and multiprocessing you can using the following script:
+The knowledge graph consists of seven main node types representing different entities within NASA's ecosystem:
 
-```python
-import json
-from tqdm import tqdm
-from collections import defaultdict
-from multiprocessing import Pool, cpu_count
-from neo4j import GraphDatabase
+1. **Dataset:** Satellite datasets managed by NASA DAACs and other providers.
+2. **Publication:** Research publications referencing datasets.
+3. **ScienceKeyword:** Keywords classifying datasets and publications.
+4. **Instrument:** Instruments used for data collection.
+5. **Platform:** Satellites and airborne platforms hosting instruments.
+6. **Project:** NASA projects associated with datasets.
+7. **DataCenter:** DAACs and other data providers.
 
-# Batch size for processing
-BATCH_SIZE = 100
-
-# Neo4j credentials (replace with environment variables or placeholders)
-NEO4J_URI = "bolt://<your-neo4j-host>:<port>"  # e.g., "bolt://localhost:7687"
-NEO4J_USER = "<your-username>"
-NEO4J_PASSWORD = "<your-password>"
-
-
-def ingest_data(file_path):
-    # Initialize counters and label trackers
-    node_label_counts = defaultdict(int)
-    relationship_label_counts = defaultdict(int)
-    node_count = 0
-    relationship_count = 0
-
-    with open(file_path, "r") as f:
-        nodes = []
-        relationships = []
-
-        # Read and categorize nodes and relationships, and count labels
-        for line in tqdm(f, desc="Reading JSON Lines"):
-            obj = json.loads(line.strip())
-            if obj["type"] == "node":
-                nodes.append(obj)
-                node_count += 1
-                for label in obj["labels"]:
-                    node_label_counts[label] += 1
-            elif obj["type"] == "relationship":
-                relationships.append(obj)
-                relationship_count += 1
-                relationship_label_counts[obj["label"]] += 1
-
-    # Print statistics
-    print("\n=== Data Statistics ===")
-    print(f"Total Nodes: {node_count}")
-    print(f"Total Relationships: {relationship_count}")
-    print("\nNode Label Counts:")
-    for label, count in node_label_counts.items():
-        print(f"  {label}: {count}")
-    print("\nRelationship Label Counts:")
-    for label, count in relationship_label_counts.items():
-        print(f"  {label}: {count}")
-    print("=======================")
-
-    # Multiprocess node ingestion
-    print("Starting Node Ingestion...")
-    node_batches = [nodes[i : i + BATCH_SIZE] for i in range(0, len(nodes), BATCH_SIZE)]
-    with Pool(processes=cpu_count()) as pool:
-        list(
-            tqdm(
-                pool.imap(ingest_nodes_batch, node_batches),
-                total=len(node_batches),
-                desc="Ingesting Nodes",
-            )
-        )
-
-    # Multiprocess relationship ingestion
-    print("Starting Relationship Ingestion...")
-    relationship_batches = [
-        relationships[i : i + BATCH_SIZE]
-        for i in range(0, len(relationships), BATCH_SIZE)
-    ]
-    with Pool(processes=cpu_count()) as pool:
-        list(
-            tqdm(
-                pool.imap(ingest_relationships_batch, relationship_batches),
-                total=len(relationship_batches),
-                desc="Ingesting Relationships",
-            )
-        )
-
-
-def ingest_nodes_batch(batch):
-    with GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD)) as driver:
-        with driver.session() as session:
-            for node in batch:
-                try:
-                    label = node["labels"][0]  # Assumes a single label per node
-                    query = f"""
-                    MERGE (n:{label} {{globalId: $globalId}})
-                    SET n += $properties
-                    """
-                    session.run(
-                        query,
-                        globalId=node["properties"]["globalId"],
-                        properties=node["properties"],
-                    )
-                except Exception as e:
-                    print(
-                        f"Error ingesting node with globalId {node['properties']['globalId']}: {e}"
-                    )
-
-
-def ingest_relationships_batch(batch):
-    with GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD)) as driver:
-        with driver.session() as session:
-            for relationship in batch:
-                try:
-                    rel_type = relationship[
-                        "label"
-                    ]  # Use the label for the relationship
-                    query = f"""
-                    MATCH (start {{globalId: $start_globalId}})
-                    MATCH (end {{globalId: $end_globalId}})
-                    MERGE (start)-[r:{rel_type}]->(end)
-                    """
-                    session.run(
-                        query,
-                        start_globalId=relationship["start"]["properties"]["globalId"],
-                        end_globalId=relationship["end"]["properties"]["globalId"],
-                    )
-                except Exception as e:
-                    print(
-                        f"Error ingesting relationship with label {relationship['label']}: {e}"
-                    )
-
-
-if __name__ == "__main__":
-    # Path to the JSON file
-    JSON_FILE_PATH = "<path-to-your-graph.json>"
-
-    # Run the ingestion process
-    ingest_data(JSON_FILE_PATH)
+**Relationships:** Nodes are connected by relationships such as `CITES`, `USES_DATASET`, `HAS_PLATFORM`, and `HAS_SCIENCEKEYWORD`.
 
-```
+---
 
-### 2. GraphML
+## Data Formats
 
-- **File**: `graph.graphml`
-- **Description**: An XML-based format well-suited for complex graph structures and metadata-rich representations.
-- **Usage**: Compatible with graph visualization and analysis tools, including Gephi, Cytoscape, and databases that support GraphML import.
+The dataset is available in three formats:
 
-#### Loading the GraphML Format
+1. **JSON:** Hierarchical format suitable for APIs and web applications.
+2. **GraphML:** XML-based format compatible with graph analysis tools.
+3. **Cypher:** Cypher commands to recreate the graph structure.
 
-To import the GraphML file into a graph database with APOC support, use the following command:
+### Loading the Dataset
 
+**Using Cypher:**
 ```cypher
-CALL apoc.import.graphml("path/to/graph.graphml", {readLabels: true})
+// Import GraphML
+CALL apoc.import.graphml("graph.graphml", {readLabels: true});
 ```
 
-### 3. Cypher
-
-- **File**: `graph.cypher`
-- **Description**: A series of Cypher commands to recreate the knowledge graph structure.
-- **Usage**: Useful for recreating the graph in any Cypher-compatible graph database.
-
-#### Loading the Cypher Format
-
-To load the Cypher script, execute it directly using a command-line interface for your graph database:
-
-```bash
-neo4j-shell -file path/to/graph.cypher
-```
-
-### 4. Loading the Knowledge Graph into PyTorch Geometric (PyG)
-
-This knowledge graph can be loaded into PyG (PyTorch Geometric) for further processing, analysis, or model training. Below is an example script that shows how to load the JSON data into a PyG-compatible `HeteroData` object.
+**Using Python (Neo4j Driver):**
+```python
+from neo4j import GraphDatabase
 
-The script first reads the JSON data, processes nodes and relationships, and then loads everything into a `HeteroData` object for use with PyG.
+def load_data():
+    uri = "bolt://localhost:7687"
+    auth = ("neo4j", "password")
+    driver = GraphDatabase.driver(uri, auth=auth)
 
-```python
-import json
-import torch
-from torch_geometric.data import HeteroData
-from collections import defaultdict
-
-# Load JSON data from file
-file_path = "path/to/graph.json"  # Replace with your actual file path
-graph_data = []
-with open(file_path, "r") as f:
-    for line in f:
-        try:
-            graph_data.append(json.loads(line))
-        except json.JSONDecodeError as e:
-            print(f"Error decoding JSON line: {e}")
-            continue
-
-# Initialize HeteroData object
-data = HeteroData()
-
-# Mapping for node indices per node type
-node_mappings = defaultdict(dict)
-
-# Temporary storage for properties to reduce concatenation cost
-node_properties = defaultdict(lambda: defaultdict(list))
-edge_indices = defaultdict(lambda: defaultdict(list))
-
-# Process each item in the loaded JSON data
-for item in graph_data:
-    if item['type'] == 'node':
-        node_type = item['labels'][0]  # Assuming first label is the node type
-        node_id = item['id']
-        properties = item['properties']
-
-        # Store the node index mapping
-        node_index = len(node_mappings[node_type])
-        node_mappings[node_type][node_id] = node_index
-
-        # Store properties temporarily by type
-        for key, value in properties.items():
-            if isinstance(value, list) and all(isinstance(v, (int, float)) for v in value):
-                node_properties[node_type][key].append(torch.tensor(value, dtype=torch.float))
-            elif isinstance(value, (int, float)):
-                node_properties[node_type][key].append(torch.tensor([value], dtype=torch.float))
-            else:
-                node_properties[node_type][key].append(value)  # non-numeric properties as lists
-
-    elif item['type'] == 'relationship':
-        start_type = item['start']['labels'][0]
-        end_type = item['end']['labels'][0]
-        start_id = item['start']['id']
-        end_id = item['end']['id']
-        edge_type = item['label']
-
-        # Map start and end node indices
-        start_idx = node_mappings[start_type][start_id]
-        end_idx = node_mappings[end_type][end_id]
-
-        # Append to edge list
-        edge_indices[(start_type, edge_type, end_type)]['start'].append(start_idx)
-        edge_indices[(start_type, edge_type, end_type)]['end'].append(end_idx)
-
-# Finalize node properties by batch processing
-for node_type, properties in node_properties.items():
-    data[node_type].num_nodes = len(node_mappings[node_type])
-    for key, values in properties.items():
-        if isinstance(values[0], torch.Tensor):
-            data[node_type][key] = torch.stack(values)
-        else:
-            data[node_type][key] = values  # Keep non-tensor properties as lists
-
-# Finalize edge indices in bulk
-for (start_type, edge_type, end_type), indices in edge_indices.items():
-    edge_index = torch.tensor([indices['start'], indices['end']], dtype=torch.long)
-    data[start_type, edge_type, end_type].edge_index = edge_index
-
-# Display statistics for verification
-print("Nodes and Properties:")
-for node_type in data.node_types:
-    print(f"\nNode Type: {node_type}")
-    print(f"Number of Nodes: {data[node_type].num_nodes}")
-    for key, value in data[node_type].items():
-        if key != 'num_nodes':
-            if isinstance(value, torch.Tensor):
-                print(f"  - {key}: {value.shape}")
-            else:
-                print(f"  - {key}: {len(value)} items (non-numeric)")
-
-print("\nEdges and Types:")
-for edge_type in data.edge_types:
-    edge_index = data[edge_type].edge_index
-    print(f"Edge Type: {edge_type} - Number of Edges: {edge_index.size(1)} - Shape: {edge_index.shape}")
+    with driver.session() as session:
+        session.run("CALL apoc.import.json('graph.json')")
+    driver.close()
 
+load_data()
 ```
 
 ---
 
 ## Citation
 
-Please cite the dataset as follows:
-
-**NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC).** (2024). *Knowledge Graph of NASA Earth Observations Satellite Datasets and Related Research Publications* [Data set]. DOI: [10.57967/hf/3463](https://doi.org/10.57967/hf/3463)
+**NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC).** (2025). *Knowledge Graph of NASA Earth Observations Satellite Datasets and Related Research Publications* [Data set]. DOI: [10.57967/hf/3463](https://doi.org/10.57967/hf/3463)
 
 ### BibTeX
 ```bibtex
-@misc {nasa_goddard_earth_sciences_data_and_information_services_center__(ges-disc)_2024,
-    author       = { {NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC)} },
-    title        = { nasa-eo-knowledge-graph },
-    year         = 2024,
-    url          = { https://huggingface.co/datasets/nasa-gesdisc/nasa-eo-knowledge-graph },
-    doi          = { 10.57967/hf/3463 },
-    publisher    = { Hugging Face }
+@misc{nasa_knowledge_graph_2025,
+    author       = {NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC)},
+    title        = {NASA Knowledge Graph Dataset (Version 2.0)},
+    year         = 2025,
+    url          = {https://huggingface.co/datasets/nasa-gesdisc/nasa-eo-knowledge-graph},
+    doi          = {10.57967/hf/3463},
+    publisher    = {Hugging Face}
 }
 ```
 
-## References
-For details on the process of collecting these publications, please refer to:
-
-Gerasimov, I., Savtchenko, A., Alfred, J., Acker, J., Wei, J., & KC, B. (2024). *Bridging the Gap: Enhancing Prominence and Provenance of NASA Datasets in Research Publications.* Data Science Journal, 23(1). DOI: [10.5334/dsj-2024-001](https://doi.org/10.5334/dsj-2024-001)
-
-
-For any questions or further information, please contact:
+---
 
+## Contact
+For inquiries, please contact:
 - Armin Mehrabian: [[email protected]](mailto:[email protected])
 - Irina Gerasimov: [[email protected]](mailto:[email protected])
 - Kendall Gilbert: [[email protected]](mailto:[email protected])

graph.cypher

@@ -1,3 +1,3 @@
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-oid sha256:d78f7b166a86be3ceec16db75a1575dac95249af188b1d2e2adab7388a8e654a
-size 1117381849
+oid sha256:6aef9d433ea1179f0b006e9c402ca48d487e78cb193d4535e920d61c078cbae3
+size 297709550

graph.graphml

@@ -1,3 +1,3 @@
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-oid sha256:a781e358b338a181db5081a50127325febab67dcb283cb4124c877bf06de439e
-size 1094260229
+oid sha256:3b653606e7ed6ee5e4367d2b4e8c4800240e1c9dc9520eec5803a2f3204ebe5a
+size 320845858

graph.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 6377999766
+oid sha256:8d49fc801f87cb43dfdadb3dc77a6e1b2b1e7e8044b46883f398b69a71dd3ac4
+size 1504091353