Synthie (Labeled Networks)
Download network data
This network dataset is in the category of Labeled Networks
Visualize Synthie's link structure and discover valuable insights using the interactive network data visualization and analytics platform. Compare with hundreds of other network data sets across many different categories and domains.
Metadata
| Category | Labeled Networks |
| Collection | Labeled Networks |
| Tags | |
| Short | Labeled Networks |
| Description | This network contains the following comma separated text files: |
| Synthie is a synthetic data sets consisting of 400 graphs. The data set is subdivided into four classes. Each node has a real-valued attribute vector of dimension 15 and no labels. We used the following procedure to generate the data set: First, we generated two Erdös-Rényi graphs using the G(n,p) model with p | 0.2 and n: 10. For each graph we generated a seed set S_i for i in {1,2 of 200 graphs by randomly adding or deleting 25% of the edges. From these seed sets we generated two classes C_1 and C_2 of 200 graphs each by randomly sampling 10 graphs from S_1 cup S_2 and randomly connecting these graphs. For C_1 we choose a seed graph with probability 0.8 from S_1 and with probability 0.2 from S_2. The class C_2 was generated the same way but with interchanged probabilities. Finally, we generated a set of real-valued vectors of dimension 15 subdivided into two classes A and B using the make_classification method Scikit-learn. We then subdivided C_i into two classes C^A_i and C^B_i by drawing a random attribute from A or B for each node. For class C^A_i, we drew an attribute from A if the node belonged to a seed graph of seed set S_1, and from B otherwise. Class C^B_i was created the same way but with interchanged seed sets. |
Please cite the following if you use the data:
Note that if you transform/preprocess the data, please consider sharing the data by uploading it along with the details on the transformation and reference to any published materials using it.
@inproceedings{nr,
title={The Network Data Repository with Interactive Graph Analytics and Visualization},
author={Ryan A. Rossi and Nesreen K. Ahmed},
booktitle={AAAI},
url={http://networkrepository.com},
year={2015}
}
Network Data Statistics
| Nodes | 36.6K |
| Edges | 161.7K |
| Density | 0.000240981 |
| Maximum degree | 48 |
| Minimum degree | 2 |
| Average degree | 8 |
| Assortativity | -0.171457 |
| Number of triangles | 125.8K |
| Average number of triangles | 3 |
| Maximum number of triangles | 68 |
| Average clustering coefficient | 0.0801794 |
| Fraction of closed triangles | 0.0615987 |
| Maximum k-core | 11 |
| Lower bound of Maximum Clique | 4 |
Network Data Preview
Interactive visualization of Synthie's graph structure
Interactively explore the networks graph structure!
- Use mouse wheel to zoom in/out
- Mouseover nodes to see their degree
- Drag network to see more details
Loading...
Interactive Visualization of Node-level Properties and Statistics
Tools for Interactive Exploration of Node-level Statistics
Visualize and interactively explore Synthie and its important node-level statistics!
- Each point represents a node (vertex) in the graph.
- A subset of interesting nodes may be selected and their properties may be visualized across all node-level statistics. To select a subset of nodes, hold down the left mouse button while dragging the mouse in any direction until the nodes of interest are highlighted.This feature allows users to explore and analyze various subsets of nodes and their important interesting statistics and properties to gain insights into the graph data
- Zoom in/out on the visualization you created at any point by using the buttons below on the left.
- Once a subset of interesting nodes are selected, the user may further analyze by selecting and drilling down on any of the interesting properties using the left menu below.
- We also have tools for interactively visualizing, comparing, and exploring the graph-level properties and statistics.
Note: You are not logged in!
Please login or join the community to leverage the many other tools and features available in our interactive graph analytics platform.
Please login or join the community to leverage the many other tools and features available in our interactive graph analytics platform.
Interactive Visualization of Node-level Feature Distributions
Node-level Feature Distributions
Discuss and Share
Collaborate and contribute to the first interactive and community-oriented data repository!
Share key insights, awesome visualizations, or simply discuss advantages of data, any observed or known properties, challenges, problems, corrections, and any other helpful comments! Post and discuss recent published works that utilize this dataset (including your own). Any and all feedback is welcome and encouraged.