|10. Geom Collaboration network in computational geometry|
|12. The friendships of registered users in NEUSNCP|
|13. E. coli transcription networks. 大肠杆菌转录|
|15. Social networks of positive sentiment|
|17. SUNBELT 2013 DATA|
|24. London Multiplex Transport Network|
|26. mammalia-voles-plj-trapping-60 （田鼠诱捕网络）|
|27. Lesmis.gml 小说《悲惨世界》人物共现网络|
|33. WV (a network of Wikipedia who-votes-on-whom).gml|
|34. Sex(a bipartite network in which nodes are females (sex sellers) and males (sex buyers) ).gml|
|35. PG (a snapshot of the Gnutella peer-to-peer file sharing network).gml|
|37. Email (Rovirai Virgili University).gml|
|38. GrQc (is a collaboration network of eprint articles in arXiv categories General Relativity and Quantum Cosmology.).gml|
|39. A network of coauthorships between 379 scientists|
|40. Weibo user_relationships (预览失败，请直接下载)|
|41. Weibo_relationships (预览失败，请直接下载).gml|
|49. Power (美国西部电力网络)|
|63. Jazz musicians network 爵士乐音乐家合作网络 （1）|
|67. (EEC) trans_email.gml|
|84. The relationships of characters in the novel ``A Song of Ice and Fire''|
|85. Email-Europe-Research-Insisute-core network|
|86. netscience.gml (科学家合作网络，From Newman, 2006 )|
|88. Jazz musicians network 爵士乐音乐家合作网络|
|89. USAir (USA_Air_Lines).gml|
|92. neusncp dataset [txt]|
|93. Tmall online shopping records|
|94. krackhardt_kite （风筝网络）|
|95. Southern Women Activities Networks (南部妇女活动网络)|
|96. Scotland Enterprise Network（苏格兰连锁企业网络）|
|98. Books about US politics（美国政治书籍网络）.gml|
|99. American College football（美国大学足球俱乐部网络）|
|100. Dolphin social network（海豚社交网络）|
|101. Zachary's karate club（空手道俱乐部网络）|
Geom.net is based on the file
geombib.bib that contains Computational Geometry Database, version February 2002.
The authors collaboration network in computational geometry was produced from the BibTeX bibliography [Beebe, 2002] obtained from the Computational Geometry Database
geombib, version February 2002 [Jones, 2002].
Two authors are linked with an edge, iff they wrote a common work (paper, book, ...). The value of an edge is the number of common works. Using a simple program written in programming language Python, the BibTeX data were transformed into the corresponding network, and output to the file in Pajek format.
The obtained network has 9072 vertices (authors) and 22577 edges (common papers or books) / 13567 edges as a simple network - multiple edges between a pair of authors are replaced with a single edge.
The problem with the obtained network is that, because of non standardized writing of the author's name, it contains several vertices corresponding to the same author. For example:
R.S. Drysdale, Robert L. Drysdale, Robert L. Scot Drysdale, R.L. Drysdale, S. Drysdale, R. Drysdale, and R.L.S. Drysdale;or:
Pankaj K. Agarwal, P. Agarwal, Pankaj Agarwal, and P.K. Agarwalthat are easy to guess; but an 'insider' information is needed to know that Otfried Schwarzkopf and Otfried Cheong are the same person. Also, no provision is made in the database to discern two persons with the same name. We manually produced the name equivalence partition and then shrank (in Pajek) the network according to it.
The reduced simple network contains 7343 vertices and 11898 edges. It is a sparse network - its average degree is 2m/n = 3.24.
Geom.bibtransformed in Pajek format and 'cleaned' by V. Batagelj and M. Zaveršnik.
N and E are the number of nodes and links. 〈k〉 and 〈d〉 are the average degree and the average distance, respectively. C and r are the average clustering coefficient and the assortative coefficient. H is the degree heterogeneity. βc is the epidemic threshold of the SIR model.