1. The document describes using Pajek software to analyze affiliation networks by exploring the relationships between actors (advertising creatives) and events (award-winning advertising project teams) over time. 2. It shows how to partition the network by attributes like the advertising agency involved, and use this to identify creatives who worked with multiple agencies. Degree distributions reveal most creatives worked on ads for a single agency. 3. Techniques like k-neighbor analysis and extended partitions are used to track the careers of top creatives by comparing their co-workers and ads across media like TV, magazines and newspapers over the years. This shows TV teams tended to be much larger on average.

1. Crude, Simple,
Effective
Using Pajek to Analyze 2-Mode Networks
John McCreery
The Word Works, Ltd
INSNA 2013, Xi’an, China

2. Assumptions
•2-mode affiliation networks: actors + events
•Events have attributes
•You are interested in how those attributes shape
relations between actors

3. Example
•Actors are advertising creatives
•Events are project teams that produce award-
winning ads
•The logic is generalizable to all 2-mode networks
where nodes in one mode can be subdivided by
attributes

4. Background

5. The Research Plan
DATA: Credits from winning ads in the Tokyo
Copywriters Club Annual
SNA: Explore networks linking members of
winning teams
DESK RESEARCH: Books and articles written
by or about central figures in the networks
INTERVIEWS: Conversations with central
figures using output from SNA and desk
research as stimulus material

6. The Data
3634
22907
7018
Note1: Ad production requires multiple roles
Note 2: Creators may play more than one role
Note 3: Multiple creators may play the same role

7. Why SNA?
•Explore network structures to see how they
changed over time
•Identify industry stars, and
•Track their careers

8. Why Pajek?
•Freeware
•Exploratory Social Network Analysis with Pajek
•Looked right for what I was trying to do

9. First Encounter

10. Stumbling Blocks
• “Techniques for analyzing one-mode networks cannot
always be applied to two-mode networks without
modification or change of meaning. Special techniques for
two-mode networks are very complicated....
• “The solution commonly used...is to change the two-mode
network into a one-mode network, which can be analyzed
with standard techniques.”
• Inevitably, however, this approach destroys useful
information.

11. For Example
• We begin with the combined network that contains data for all
six networks (1981-2006)
• After simplifying the network to remove multiple lines, we
click on the info icon (Rows=Creators, Columns=Ads)
==============================================================================
1. Z:DocumentsMagic BriefcaseWinner's CirclesNetworksRevised January 2012CAR81-
06Creator Ads Roles 81-06[Single Line].net [2-Mode] (10652)
==============================================================================
Number of vertices (n): 10652
----------------------------------------------------------
Arcs Edges
----------------------------------------------------------
Total number of lines 0 22907
----------------------------------------------------------
Number of loops 0 0
Number of multiple lines 0 0
----------------------------------------------------------
2-Mode Network: Rows=7018, Cols=3634
Density [2-Mode] = 0.00089819
Average Degree = 4.30097634

12. A Giant Component
• Using Network>Create Partition>Components>Weak, we
determine that the network contains 94 components, including
one giant component that accounts for 95.9% of all nodes.
• This network seems to be highly connected. But the single
giant component conceals underlying structures.
• We need to look more deeply.

13. Crude, Simple, Effective
Solutions
•Extended partitions
•Shrinking networks and examining degree
distributions
•Using k-neighbor and extended partitions to track
and compare careers

14. Extended
Partitions

15. Question
•We know that the Japanese advertising industry is
an oligopoly dominated by two giant agencies,
Dentsu and Hakuhodo, with ADK No. 3
•How many creators work on projects for more
than one agency?

16. The Agency Partition
• My Filemaker Pro database makes it simple to partition the Ads using the attribute Agency
• When I import that partition and check info, I see that I have a partition with four clusters
(1=Dentsu, 2=Hakuhodo, 3=ADK, 4= Other) that covers a total of 3634 nodes.
• When I try to use Operations>Network+Partition>Extract Subnetwork, Pajek generates an
error message “Network and Partition of equal size needed.”
==============================================================================
1. Z:DocumentsMagic BriefcaseWinner's CirclesNetworksRevised January 2012CAR81-06Agencies 81-06.clu (3634)
==============================================================================
Dimension: 3634
The lowest value: 1
The highest value: 4
Frequency distribution of cluster values:
Cluster Freq Freq% CumFreq CumFreq% Representative
----------------------------------------------------------------
1 1187 32.6637 1187 32.6637 1
2 628 17.2812 1815 49.9450 40
3 47 1.2933 1862 51.2383 295
4 1772 48.7617 3634 100.0000 3
----------------------------------------------------------------
Sum 3634 100.0000

17. Extending the Partition
• To create a partition of equal size, I begin with Partition>Create Constant Partition, setting
the dimension to 7018 (the number of creators) and the constant to 0.
• Then with the constant partition in the first partition field and the agency partition in the
second partition field, I use Partitions>Fuse Partition
• I save the extended partition for later use.
==============================================================================
3. Fusion of C2 and C1 (10652)
==============================================================================
Dimension: 10652
The lowest value: 0
The highest value: 4
Frequency distribution of cluster values:
Cluster Freq Freq% CumFreq CumFreq% Representative
----------------------------------------------------------------
0 7018 65.8843 7018 65.8843 Nak1
1 1187 11.1434 8205 77.0278 AD1_01
2 628 5.8956 8833 82.9234 AD7_86
3 47 0.4412 8880 83.3646 AD50_01
4 1772 16.6354 10652 100.0000 AD1_81
----------------------------------------------------------------
Sum 10652 100.0000

18. Shrinking networks and
examining degree
distributions

19. Shrink and Examine Degree
• With the simplified network and extended partition as input, I use Operations>Network+Partition>Shrink
Network, leaving the 0 cluster, the creatives, unshrunk
• We know that we are starting with a 2-mode network, in which creators can only be linked directly to ads. Thus,
in the shrunk network, creators will have at most four immediate neighbors
• Using Network>Create Partition>Degree>All, we find that of 7018 creators, 5976 have worked for only one
agency, 822 for two agencies, 195 for three agencies, and only 25 for four agencies. As an added bonus we can
see the total number of creatives who have worked for each of the agencies (our database makes it simple to
track down the agency that created the ad whose label is used for the agency cluster)
==============================================================================
5. All Degree Partition of N2 (7022)
==============================================================================
Dimension: 7022
The lowest value: 1
The highest value: 3362
Frequency distribution of cluster values:
Cluster Freq Freq% CumFreq CumFreq% Representative
----------------------------------------------------------------
1 5976 85.1040 5976 85.1040 Saw5
2 822 11.7061 6798 96.8100 Nak1
3 195 2.7770 6993 99.5870 Nak190
4 25 0.3560 7018 99.9430 Yag350
230 1 0.0142 7019 99.9573 #AD50_01
1779 1 0.0142 7020 99.9715 #AD7_86
2934 1 0.0142 7021 99.9858 #AD1_01
3362 1 0.0142 7022 100.0000 #AD1_81
----------------------------------------------------------------
Sum 7022 100.0000

20. Using k-neighbor and extended
partitions to track and compare
careers

21. Largest No. of Ads
Table 1. Highest Degree Creators in 2-Mode Network
=Number of Ads in AnnualsRank Vertex Cluster Id
1 17 244 Nak190
2 3 172 Sas3
3 56 139 Soe903
4 54 126 Aki264
5 35 90 Oka258
6 311 89 Iwa101
7 47 84 Mak65
8 45 84 Kas90
9 564 71 Oka1165
10 48 67 Oos113
11 21 67 Ito6226
12 51 63 Jum193
13 770 60 Hos265
14 280 59 Miy952
15 1 58 Nak1
16 100 54 Saw8
17 316 52 Sak118
18 773 51 Yos6391
19 380 50 Nis1415
20 290 49 Fuj112

22. Largest No. of Co-Workers
Table 2. Highest Degree Creators in 1-Mode Network
=Ties to Other CreatorsRank Vertex Cluster Id
1 3 421 Sas3
2 17 345 Nak190
3 35 332 Oka258
4 311 289 Iwa101
5 48 254 Oos113
6 216 210 Tad27
7 202 206 Sig283
8 54 199 Aki264
9 100 198 Saw8
10 56 173 Soe903
11 1706 171 Ter536
12 51 169 Jum193
13 1573 162 Kim488
14 1827 159 Sat597
15 78 157 Ish370
16 305 157 Ato51
17 2446 152 Som1032
18 339 151 Mr.6672
19 290 147 Fuj112
20 475 146 Kam122

23. With Pajek
•Read Network
•Read Extended Media Partition
•Network>Create Partition>k-Neighbors
•#Extended Partition first, k-Neighbors
partition second
•Partitions>Extract SubPartition (Second from
First)

24. Ads by Media
1=TV, 2=Radio,3=Newspaper,4=Magazine,5=Poster, 6=Other
3=Newspaper,4=Magazine,5=Poster, 6=OtherK1, Ads by Media, Nakahata Takashi
=====================================================================
Dimension: 244
Frequency distribution of cluster values:
Cluster Freq Freq% CumFreq CumFreq% Representative
----------------------------------------------------------------
1 40 16.3934 40 16.3934 7
2 2 0.8197 42 17.2131 189
3 92 37.7049 134 54.9180 1
4 36 14.7541 170 69.6721 28
5 68 27.8689 238 97.5410 2
6 6 2.4590 244 100.0000 39
----------------------------------------------------------------
Sum 244 100.0000
K1, Ads by Media, Sasaki Hiroshi
=====================================================================
Dimension: 172
Frequency distribution of cluster values:
Cluster Freq Freq% CumFreq CumFreq% Representative
----------------------------------------------------------------
1 67 38.9535 67 38.9535 2
2 6 3.4884 73 42.4419 131
3 31 18.0233 104 60.4651 1
4 4 2.3256 108 62.7907 36
5 51 29.6512 159 92.4419 5
6 13 7.5581 172 100.0000 82
----------------------------------------------------------------
Sum 172 100.0000

25. TV Teams Twice as Large
Creators Ads Avg Team
TV 12135 1159 10.47
Radio 1185 194 6.11
Newspaper 6152 1226 5.02
Magazine 2284 517 4.42
•Based on the number of individuals who are given credits per ad,
creative teams for TV commercials are, on average, twice as large as
those for newspaper ads and more than twice as large as those for
magazine ads.
•A team of size n contributes n(n-1)/2 links to the network.

26. Conclusions
•Think simple
•Use extended partitions
•Take advantage of 2-mode network characteristics

27. Thank You