Bitcoin Alpha

This is a user–user trust/distrust network, from the Bitcoin Alpha platform, on which Bitcoins are traded. Each directed edge represents trust or distrust on a scale from −10 to +10. Positive edge weights denote trust, and negative edge weights denote distrust. An edge weight of zero does not appear.


Internal namesoc-sign-bitcoinalpha
NameBitcoin Alpha
Data source,
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Online social network
Dataset timestamp 2010 ⋯ 2016
Node meaningMember
Edge meaningTrust/distrust
Network formatUnipartite, directed
Edge typeSigned, possibly weighted, no multiple edges
Temporal data Edges are annotated with timestamps
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsDoes not contain loops


Size n =3,783
Volume m =24,186
Loop count l =0
Wedge count s =851,958
Claw count z =282,238,264
Cross count x =39,076,718,662
Triangle count t =22,153
Square count q =615,962
4-Tour count T4 =8,363,776
Maximum degree dmax =888
Maximum outdegree d+max =490
Maximum indegree dmax =398
Average degree d =12.786 7
Fill p =0.001 690 46
Size of LCC N =3,775
Size of LSCC Ns =3,235
Relative size of LSCC Nrs =0.855 141
Diameter δ =10
50-Percentile effective diameter δ0.5 =3.182 39
90-Percentile effective diameter δ0.9 =4.424 37
Median distance δM =4
Mean distance δm =3.688 88
Gini coefficient G =0.703 513
Relative edge distribution entropy Her =0.861 907
Power law exponent γ =1.924 11
Tail power law exponent γt =2.091 00
Degree assortativity ρ =−0.168 516
Degree assortativity p-value pρ =5.632 93 × 10−179
In/outdegree correlation ρ± =+0.917 288
Clustering coefficient c =0.078 007 4
Directed clustering coefficient c± =0.071 794 6
Spectral norm α =215.586
Operator 2-norm ν =126.098
Cyclic eigenvalue π =93.416 9
Algebraic connectivity a =0.253 239
Reciprocity y =0.832 052
Non-bipartivity bA =0.516 941
Negativity ζ =0.063 507 8
Algebraic conflict ξ =0.140 284
Triadic conflict τ =0.143 562
Spectral signed frustration φ =0.004 771 95


Fruchterman–Reingold graph drawing

Degree distribution

Cumulative degree distribution

Lorenz curve

Spectral distribution of the adjacency matrix

Spectral distribution of the normalized adjacency matrix

Spectral distribution of the Laplacian

Spectral graph drawing based on the adjacency matrix

Spectral graph drawing based on the Laplacian

Spectral graph drawing based on the normalized adjacency matrix

Degree assortativity

Zipf plot

Hop distribution

Double Laplacian graph drawing

Delaunay graph drawing

In/outdegree scatter plot

Item rating evolution

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution

Temporal distribution

Temporal hop distribution

Signed temporal distribution

Rating class evolution


Matrix decompositions plots



[1] Jérôme Kunegis. KONECT – The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion, pages 1343–1350, 2013. [ http ]
[2] Srijan Kumar, Francesca Spezzano, V. S. Subrahmanian, and Christos Faloutsos. Edge weight prediction in weighted signed networks. In Proc. Int. Conf. Data Min., pages 221–230, 2016.