ia-reality-call (Dynamic Networks)
Download network data
This network is in the collection of Dynamic Networks
Visualize ia-reality-call's link structure and discover valuable insights using our interactive graph visualization platform. Compare with hundreds of other networks across many different collections and types.
Metadata
| Category | Sparse networks, temporal networks |
| Collection | Interaction networks |
| Tags | |
| Source | http://realitycommons.media.mit.edu/realitymining.html |
| Short | user-calls-user |
| Vertex type | Person |
| Edge type | Call |
| Format | Undirected |
| Edge weights | Multiple unweighted edges |
| Metadata | Time (edges have timestamps) |
| Description | Reality mining network data consists of human mobile phone call events between a small set of core users at the Massachusetts Institute of Technology (MIT) whom actually were assigned mobile phones for which all calls were collected. The data also contains calls from users outside this small set of users to other phones of individuals that were not actively monitored and thus these nodes generally have fewer edges than nodes within the small set of users at MIT that participated in the experiment and were assigned phones. The data was collected collected by the Reality Mining experiment performed in 2004 as part of the Reality Commons project. The data was collected over 9 months using 100 mobile phones. A node represents a person; an edge indicates a phone call or voicemail between two users. See http://realitycommons.media.mit.edu/realitymining.html for more details. |
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={Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence},
url={http://networkrepository.com},
year={2015}
}
@article{eagle2006reality,
title={Reality mining: sensing complex social systems},
author={Eagle, N. and Pentland, A.},
journal={Personal and Ubiquitous Computing},
volume={10},
number={4},
pages={255--268},
year={2006},
}
Network Statistics
| Nodes | 6.8K |
| Edges | 51.2K |
| Density | 0.00221103 |
| Maximum degree | 3K |
| Minimum degree | 1 |
| Average degree | 15 |
| Assortativity | -0.315687 |
| Number of triangles | 923.7K |
| Average number of triangles | 135 |
| Maximum number of triangles | 97.3K |
| Average clustering coefficient | 0.362203 |
| Fraction of closed triangles | 0.0299949 |
| Maximum k-core | 967 |
| Lower bound of Maximum Clique | 116 |
Data Preview
Interactive visualization of ia-reality-call'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
Interactive Visualization of Node-level Properties and Statistics
Tools for Interactive Exploration of Node-level Statistics
Visualize and interactively explore ia-reality-call 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.
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.