DBLP co-authorship

This is the co-authorship network of the DBLP computer science bibliography. Nodes are authors and an undirected edge between two nodes exists if the corresponding authors have published at least one paper together.


Internal namecom-dblp
NameDBLP co-authorship
Data sourcehttp://snap.stanford.edu/data/com-DBLP.html
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Co-authorship network
Node meaningAuthor
Edge meaningCo-authorship
Network formatUnipartite, undirected
Edge typeUnweighted, no multiple edges
LoopsDoes not contain loops
Join Is the join of an underlying network


Size n =317,080
Volume m =1,049,866
Wedge count s =21,780,889
Triangle count t =2,224,385
Square count q =55,107,655
4-Tour count T4 =530,084,528
Maximum degree dmax =343
Average degree d =6.622 09
Fill p =2.088 47 × 10−5
Size of LCC N =317,080
Diameter δ =23
50-Percentile effective diameter δ0.5 =6.087 33
90-Percentile effective diameter δ0.9 =8.164 68
Median distance δM =7
Mean distance δm =6.752 76
Tail power law exponent γt =3.261 00
Tail power law exponent with p γ3 =3.261 00
p-value p =0.000 00
Degree assortativity ρ =+0.266 521
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.306 377
Algebraic connectivity a =0.012 818 8
Normalized non-bipartivity bN =0.032 705 7
Spectral bipartite frustration bK =0.002 175 53


Degree distribution

Cumulative degree distribution

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

Hop distribution

Clustering coefficient 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] Jaewon Yang and Jure Leskovec. Defining and evaluating network communities based on ground-truth. In Proc. ACM SIGKDD Workshop on Min. Data Semant., page 3, 2012.