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Table 2 List, with sources, of the networks we used in our benchmark

From: Graph neural network inspired algorithm for unsupervised network community detection

No

Name

Nodes

Edges

Weighted

Description

1

karate

34

78

NO

Zachary’s Karate network Zachary (1977)

2

chesapeake

39

170

NO

Chesapeake Bay Mesohaline Network Baird and Ulanowicz (1989)

3

dolphins

62

159

NO

Dolphins’ Social Network Lusseau et al. (2003)

4

lesmis

77

254

YES

Co-appeareance of characters in Les Miserables Knuth (1993)

5

polbooks

105

441

NO

Amazon.com Co-purchases of political booksa

6

adjnoun

112

425

NO

Common adjective and noun adjacencies in David Copperfield Newman (2006)

7

football

115

613

NO

American College Football games in year 2000 Girvan and Newman (2002)

8

jazz

198

2742

NO

Network of Jazz Musicians Gleiser and Danon (2003)

9

C.Elegans N.

297

2148

YES

Neural network of C. Elegans White et al. (1986)

10

US Airports1997

332

2126

YES

US Aiports network from 1997b

11

C.Elegans M.

453

2025

NO

Metabolic Network of C. ElegansDuch and Arenas (2005)

12

email

1133

5451

NO

Email Networks University of Tarragona Guimerà et al. (2003)

13

polblogs

1490

16715

NO

Connections among political blogs Adamic and Glance (2005)

14

US Airports2010

1858

17215

YES

Complete network of US airports in 2010c

15

power

4941

6594

NO

A network of the Western States Power Grid of the US Watts and Strogatz (1998)

16

PGPgiantcompo

10680

24316

NO

Giant component of the network of users of the PGP algorithm Boguñá et al. (2004)

17

Krong500slogn16

65536

2456071

NO

A synthetic graphs created with the Kronecker generatord

  1. aValdis Krebs, data available online at http://www.orgnet.com
  2. bVladimir Batagelj and Andrej Mrvar (2006): Pajek datasets. Airports. http://vlado.fmf.uni-lj.si/pub/networks/data/mix/USAir97.net
  3. cData from the Bureau of Transportation Statistics - details at http://toreopsahl.com/datasets/#usairports
  4. dhttps://graph500.org