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bitcoin_alpha — Bitcoin Alpha trust network (2017)

Description

A who-trusts-whom network of users of Bitcoin Alpha, an online platform for trading with Bitcoin. Since Bitcoin users are anonymous, the platform let members rate each other to build a record of reputation that helps prevent transactions with fraudulent or risky users. A directed edge $i \to j$ means that user $i$ rated user $j$, on a scale from -10 (total distrust) to +10 (total trust) in steps of 1 (a rating of 0 is not possible). The data, covering ratings made from November 2010 to January 2016, were collected and released by S. Kumar and collaborators as the first explicit weighted signed directed network available for research.

The network has the following properties:

  • Nodes: name, the anonymized user identifier.
  • Edges: rating, the rating (an integer from -10 to +10, excluding 0); time, the time of the rating, in seconds since the Unix epoch (with a resolution of one day).
Tags
Social Online Weighted Signed Timestamps
Citation
  • S. Kumar, F. Spezzano, V.S. Subrahmanian, and C. Faloutsos, "Edge weight prediction in weighted signed networks." IEEE 16th International Conference on Data Mining (ICDM), 221-230 (2016) 10.1109/icdm.2016.0033 Sci-Hub
Upstream URL OK
http://snap.stanford.edu/data/soc-sign-bitcoinalpha.html
Networks
Tip: hover over a column header for a description.
Column legend
$\left<k\right>$
Mean degree
$\sigma_k$
Standard deviation of the degrees
$\lambda_h$
Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$
Random walk mixing time
$r$
Degree assortativity
$c$
Global clustering coefficient
$\oslash$
(Pseudo-) diameter
$S$
Relative size of the largest component
NPs
Node properties
EPs
Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
bitcoin_alpha 3,783 24,186 6.39 34.44 45.31 25.67 -0.16 0.08 10 1.00 Directed Unipartite name rating time 147 KiB 246 KiB 238 KiB 206 KiB
Ridiculograms*
* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.