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bitcoin_trust — Bitcoin OTC trust network (2017)

Description

A who-trusts-whom network of users of Bitcoin OTC, an online over-the-counter marketplace 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. Nodes are users, and 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.

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 (a floating-point number, with sub-second resolution).
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-bitcoinotc.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_trust 5,881 35,592 6.05 38.30 50.83 25.68 -0.16 0.06 9 1.00 Directed Unipartite name rating time 433 KiB 524 KiB 514 KiB 500 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.