US patents

This is the citation network of patents registered with the United States Patent and Trademark Office. Each node is a patent, and a directed edge represents a patent and an edge represents a citation. The network does not contain loops, i.e., self-citations, and is acyclic.


Internal namepatentcite
NameUS patents
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Citation network
Node meaningPatent
Edge meaningCitation
Network formatUnipartite, directed
Edge typeUnweighted, no multiple edges
ReciprocalContains reciprocal edges
Directed cyclesDoes not contain directed cycles
LoopsDoes not contain loops


Size n =3,774,768
Volume m =16,518,947
Loop count l =0
Wedge count s =335,781,273
Claw count z =6,803,403,509
Cross count x =387,506,408,979
Triangle count t =7,515,023
Square count q =341,906,226
4-Tour count T4 =4,111,412,794
Maximum degree dmax =793
Maximum outdegree d+max =770
Maximum indegree dmax =779
Average degree d =8.752 30
Fill p =1.159 32 × 10−6
Size of LCC N =3,764,117
Size of LSCC Ns =1
Relative size of LSCC Nrs =2.649 17 × 10−7
Diameter δ =26
50-Percentile effective diameter δ0.5 =7.708 80
90-Percentile effective diameter δ0.9 =9.482 76
Median distance δM =8
Mean distance δm =8.244 90
Gini coefficient G =0.516 094
Balanced inequality ratio P =0.308 714
Outdegree balanced inequality ratio P+ =0.343 822
Indegree balanced inequality ratio P =0.299 293
Relative edge distribution entropy Her =0.968 856
Power law exponent γ =1.608 72
Tail power law exponent γt =4.001 00
Degree assortativity ρ =+0.167 679
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.067 142 1
Directed clustering coefficient c± =0.091 476 0
Spectral norm α =113.040
Operator 2-norm ν =112.215
Reciprocity y =0.000 00
Non-bipartivity bA =0.013 644 5
Normalized non-bipartivity bN =0.008 306 86


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 normalized adjacency matrix

Hop distribution

In/outdegree scatter plot


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] Bronwyn H. Hall, Adam B. Jaffe, and Manuel Trajtenberg. The NBER patent citations data file: Lessons, insights and methodological tools. In NBER Working Papers 8498, Natl. Bureau of Economic Research, Inc, 2001.