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.
Metadata
Statistics
| 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
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| Maximum outdegree | d+max = | 490
|
| Maximum indegree | d−max = | 398
|
| Average degree | d = | 12.786 7
|
| Fill | p = | 0.001 690 46
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| 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
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| Balanced inequality ratio | P = | 0.221 988
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| Outdegree balanced inequality ratio | P+ = | 0.225 544
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| Indegree balanced inequality ratio | P− = | 0.228 686
|
| Relative edge distribution entropy | Her = | 0.861 907
|
| Power law exponent | γ = | 1.924 11
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| Tail power law exponent | γt = | 2.091 00
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| Tail power law exponent with p | γ3 = | 2.091 00
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| p-value | p = | 0.000 00
|
| Outdegree tail power law exponent with p | γ3,o = | 2.121 00
|
| Outdegree p-value | po = | 0.006 000 00
|
| Indegree tail power law exponent with p | γ3,i = | 2.231 00
|
| Indegree p-value | pi = | 0.000 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
|
| Spectral separation | |λ1[A] / λ2[A]| = | 1.373 80
|
| Reciprocity | y = | 0.832 052
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| Non-bipartivity | bA = | 0.516 941
|
| Normalized non-bipartivity | bN = | 0.039 852 3
|
| Algebraic non-bipartivity | χ = | 0.072 870 2
|
| Spectral bipartite frustration | bK = | 0.002 435 24
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| Negativity | ζ = | 0.063 507 8
|
| Algebraic conflict | ξ = | 0.140 284
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| Triadic conflict | τ = | 0.143 562
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| Spectral signed frustration | φ = | 0.004 771 95
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| Controllability | C = | 1,817
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| Relative controllability | Cr = | 0.480 307
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Plots
Matrix decompositions plots
Downloads
References
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[1]
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Jérôme Kunegis.
KONECT – The Koblenz Network Collection.
In Proc. Int. Conf. on World Wide Web Companion, pages
1343–1350, 2013.
[ http ]
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[2]
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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.
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