reptilia-tortoise-network-fi (Dynamic Networks)
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This network dataset is in the category of Dynamic Networks
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Metadata
| Category | Animal Social Networks |
| Collection | Animal Networks |
| About | Real-world animal interaction network data sets. Animal interaction data from published studies of wild, captive, and domesticated animals. |
| Tags | |
| Source | https://bansallab.github.io/asnr/data.html |
| Short | Animal Networks |
| Vertex type | Animal, Reptile, desert, tortoise |
| Edge type | Interaction |
| Format | Undirected |
| Edge weights | Unweighted |
| Species | Gopherus agassizii |
| Taxon. class | Reptilia |
| Population | free-ranging |
| Geo. location | Nevada, USA |
| Data collection | radio tags |
| Interaction type | social projection bipartite |
| Definition of interaction | A bipartite network was first constructed based on burrow use - an edge connecting a tortoise node to a burrow node indicated burrow use by the individual. Social networks of desert tortoises were then constructed by the bipartite network into a single-mode projection of tortoise nodes. |
| Edge weight type | unweighted |
| Data collection duration | 8 months |
| Time span (within a day) | focal follow/ad libitum |
| Description | Networks represent social data collected over different years and inactive (NovemberÐFebruary)/active (MarchÐOctober) season. |
| Citation | Sah, Pratha, et al. "Inferring social structure and its drivers from refuge use in the desert tortoise, a relatively solitary species." Behavioral Ecology and Sociobiology 70.8 (2016): 1277-1289. |
| Edge timestamps | Third column encodes the weights for the edges and the fourth column represents the edge timestamps. If the graph is unweighted (has only 3 columns), then the third column represents the timestamps.For this temporal network, edge timestamps are not recorded at the finest granularity (sec. or ms.) and are instead discrete approximations of the actual temporal network. Unfortunately, the actual edge timestamps, that is, when the interactions were actually observed (e.g., at the level of seconds) has not been provided.Hence, one can create a sequence of static snapshot graphs by aggregating all edges that occur at each unique edge timestamp and repeating this for all edge timestamps. |
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={https://networkrepository.com},
year={2015}
}
Network Data Statistics
| Nodes | 787 |
| Edges | 1.7K |
| Density | 0.00553847 |
| Maximum degree | 40 |
| Minimum degree | 1 |
| Average degree | 4 |
| Assortativity | 0.53928 |
| Number of triangles | 7.6K |
| Average number of triangles | 9 |
| Maximum number of triangles | 266 |
| Average clustering coefficient | 0.353495 |
| Fraction of closed triangles | 0.463881 |
| Maximum k-core | 16 |
| Lower bound of Maximum Clique | 5 |
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