arXiv hep-th

This is the co-citation graph of papers of scientific papers from the arXiv's High Energy Physics – Theory (hep-th) section. An edge between two papers represents a paper that cites both papers. Timestamps denote the date of a publication.


Internal nameca-cit-HepTh
NamearXiv hep-th
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Co-citation network
Node meaningAuthor
Edge meaningCo-citation
Network formatUnipartite, undirected
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps
LoopsDoes not contain loops
Join Is the join of an underlying network


Size n =22,908
Volume m =2,673,133
Unique edge count m̿ =2,444,798
Wedge count s =2,134,203,872
Claw count z =1,506,168,874,130
Cross count x =1,478,988,337,901,334
Triangle count t =191,358,360
Square count q =92,607,592,200
4-Tour count T4 =749,402,442,684
Maximum degree dmax =11,967
Average degree d =233.380
Fill p =0.009 317 89
Average edge multiplicity m̃ =1.093 40
Size of LCC N =22,721
Diameter δ =9
50-Percentile effective diameter δ0.5 =2.212 36
90-Percentile effective diameter δ0.9 =3.131 39
Median distance δM =3
Mean distance δm =2.717 19
Gini coefficient G =0.684 442
Balanced inequality ratio P =0.235 728
Power law exponent γ =1.233 90
Tail power law exponent γt =1.721 00
Degree assortativity ρ =−0.033 934 3
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.268 988
Spectral norm α =1,225.32
Algebraic connectivity a =0.175 383
Spectral separation 1[A] / λ2[A]| =2.093 52
Non-bipartivity bA =0.896 403
Normalized non-bipartivity bN =0.152 926
Algebraic non-bipartivity χ =0.235 724
Spectral bipartite frustration bK =0.000 273 859
Controllability C =22
Relative controllability Cr =0.000 960 363


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

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution

Temporal distribution

Temporal hop distribution

Diameter/density evolution


Inter-event distribution

Node-level inter-event distribution

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] Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. Graph evolution: Densification and shrinking diameters. ACM Trans. Knowl. Discov. from Data, 1(1):1–40, 2007.