Leonard - EAKOS 2009

VAST 2009 Challenge
Challenge 2 - Social Network and Geospatial

Authors and Affiliations:

Lorne Leonard, The Pennsylvania State University - Research Computing & Cyberinfrastructure, lorne_leonard@hotmail.com [PRIMARY contact]

Tool(s):

EAKOS is a collection of tools to demonstrate how one can interface with web based visualization and GIS services. The toolset is an early prototype developed by Lorne Leonard during his spare time at the 2008 Christmas break and weekends leading up to the competition deadline. Lorne works with researchers and faculty at The Pennsylvania State University and he uses the toolset to demonstrate potential visualization and analytical solutions to enhance their research goals.

Video:

Leonard_Vast2009_Challenge2.mov

ANSWERS:

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MC2.1: Which of the two social structures, A or B, most closely match the scenario you have identified in the data?

A

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MC2.2:  Provide the social network structure you have identified as a tab delimitated file. It should contain the employee, one or more handler, any middle folks, and the localized leader with their international contacts. What are the Flitter names of the persons involved? Please identify only key connections (not all single links for example) as well as any other nodes related to the scenario (if any) you may have discovered that were not described in the two scenarios A and B above.

Flitter.txt

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MC2.3:  Characterize the difference between your social network and the closest social structure you selected (A or B). If you include extra nodes please explain how they fit in to your scenario or analysis.

To examine the possible social structures, I first approached this challenge by identifying each entities' calling aggregation per city and country. Figure 1 shows the matrix for the 6000 unique IDs. Blue rectangles represent total calls per city, red is calls per same country and orange is calls outside the country.

Figure 1: Call links per unique ID and region.

From the 6000 IDs, only 21 IDs meet the criteria of having over 100 links for Fearless leader (Figure 2). These are 1,2,3,4,5,6,7,8,10,11,12,15,17,18,20,23,25,28,35,36 and 46.

This tool took approximately 2 days to code and less than 5 minutes to identify which IDs have over 100 links.

Figure 2: Filtering ID's who have more than 100 links.

My next step was to develop a query tool to help match the criteria identified with the two forms of social structures (Figure 3). The control marked at Figure 3A is where the user can identify the range of contacts for the employee role. The user controls the range of contacts for possible handlers in Figure 3B. The dropdown list in Figure 3C contains the possible handlers that meet these criteria. In Figure 3C, cells marked in light red indicate the possible handler and yellow cells indicate a partial match, but are not necessarily common with all the handlers. Orange cells indicate which IDs from all the handlers share common middle persons in the organization. Figure 4D shows whom the possible middlemen are contacting. Cells marked in light red are the middleman handlers while orange cells are possible leaders or other related contacts. Figure 4E is where the user can specify the minimum number of contacts for possible leaders (and show the count). Figure 4F shows whom the possible leader is contacting. It took approximately two days to code this query tool and less than 10 minutes to explore the possible solutions found.

Range of contacts for Employee Role	Range of contacts for each of the Possible Handlers	Possible Handlers
Figure 3: Query tool Part 1.

Possible MiddleMan Contacts	Possible Leaders	Leader International Contacts
Figure 4: Query tool Part 2.

Using this technique, I have identified the closest social structure as form A. Two networks closely match this structure with Employee ID 100 (Table 1) being the best match and Employee ID 142 a close match (Table 2). ID 100 is the best answer as the middleman "Boris" (ID 4994) as he has five contacts in total (Figure 4D). Three of the five contacts are the handlers (193,261,563), two others are the leader (ID 4 who has 256 contacts) and the remaining is ID 1612 (Related Other). The international contacts for ID 4 are listed in Table 3.

Employee ID 142 is not an identical match to social structure A due to the number of contacts for the Middleman ID 4980 (Figure 5). There are six contacts in total, three being the handlers (38,101,318) and three others (11, 2909, 5977). ID 11is the leader as the only ID with more than 100 links with a total of 168 links. The international contacts for ID 11 are listed in Table 4. IDs 2909 and 5977 mean that "Boris" is communicating not with one other person but with two more contacts.

ID	Role	Filter Name
100	Employee	@schaffter
194	Handler	@reitenspies
261	Handler	@kushnir
563	Handler	@pettersson
4994	Middleman	@good
1612	Related Other	@ moilanen
4	Fearless Leader	@szemeredi
Table 1: Social Network with Employee ID 100

ID	Role	Filter Name
142	Employee	@lafouge
38	Handler	@krintz
101	Handler	@lonning
318	Handler	@formenti
4980	Middleman	@rosch
2909	Related Other	@berglund
5977	Related Other	@koshkin
11	Fearless Leader	@cornell
Table 2: Social Network with Employee ID 142

Figure 5: Employee ID 142 and Middleman 4980

ID	Filter Name	City	Country
5078	avouris	Otello	Posana
1450	barvinok	Otello	Posana
551	chandru	Otello	Posana
282	decker	Otello	Posana
1630	heyderhoff	Otello	Posana
629	nakhaeizadeh	Otello	Posana
2077	streng	Otello	Posana
4776	bolotov	Transpasko	Transak
3946	hogstedt	Transpasko	Transak
2103	wotawa	Transpasko	Transak
589	kodama	Tulamuk	Trium
3235	reed	Tulamuk	Trium
92	tolbert	Tulamuk	Trium
5561	wenocur	Tulamuk	Trium
Table 3: International Calls from ID 4

ID	Filter Name	City	Country
2091	agnew	Otello	Posana
2839	grabe	Otello	Posana
4244	haase	Otello	Posana
5221	anily	Otello	Posana
5547	gates	Otello	Posana
1336	geibel	Transpasko	Transak
1525	baik	Transpasko	Transak
3578	singh	Transpasko	Transak
4358	crockett	Transpasko	Transak
5052	tusera	Transpasko	Transak
66	lyonns	Tulamuk	Trium
925	kechadi	Tulamuk	Trium
3859	konakovsky	Tulamuk	Trium
4184	anderssen	Tulamuk	Trium
Table 4: International Calls from ID 11

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MC2.4:  How is your hypothesis about the social structure in Part 1 supported by the city locations of Flovania? What part(s), if any, did the role of geographical information play in the social network of part one?

Based on the Employee ID 100 social structure, the employee and the three handlers (ID 194,261,563) are located in Prounov (Large City). The middleman (ID 4994) is situated in Kannvic (Mid-Sized City) and the fearless leader (ID 4) is located at Kouvnic (Mid-Sized City).

Based on the Employee ID 142 social structure, the employee is based in Prounov (Large City). The three handlers (ID 38,101,318) and the middleman (ID 4980) are all based in Koul (Large City), and the fearless leader (ID 11) is located at Ryzkland (Small City).

In both social structures, the fearless leader is close to Flovania's border, perhaps for an easier escape route and physical exchange of information/money/drugs. In both cases, the leader has connections in all cities, with the most connections in the larger cities.

Boris the middleman 4994 (based on Employee ID 100) has only two geographic links between the leaders home town (Kouvnic) and the handlers location in Prounov. However, this may cause a problem for Boris, as it could be more difficult to keep an eye on his handlers based in another city. Boris with ID 4980 (based on Employee ID 142) shares the same city as his handlers.

However, in the social structure based on ID 100, the handlers are in the same city as the employee (Figure 1), but the social structure based on ID 142 is not (Figure 2). It is more probable that the handlers need to be in the same city as the employee to pick up intelligence in a short amount of time and to keep an eye on the employee. Thus, the best social structure is based on ID 100 due to this geographic connection.

Figure 1: Social Structure based on ID 100 and the

three handlers (ID 194,261,563) are located in Prounov

Figure 2: Social Structure based on ID 142 who is based in Prounov,

the three handlers (ID 38,101,318)

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MC2.5:  In general, how are the Flitter users dispersed throughout the cities of this challenge? Which of the surrounding countries may have ties to this criminal operation? Why might some be of more significant concern than others?

The largest numbers of Flitter users are located in the largest cities and Table 1 summarizes the number of flitter users per city and the number of users per country as a percentage. This table is automatically produced when examining Flitter users' city linkages.

City	City Count	Country	Country Count	Country Percent
Koul	21027	Flovania	56274	37.3653907666062
Prounov	16243	Flovania	56274	28.8641290827025
Kouvnic	7911	Flovania	56274	14.0580019191811
Kannvic	3380	Flovania	56274	6.00632618971461
Solvenz	1869	Flovania	56274	3.32124960017059
Ryzkland	1656	Flovania	56274	2.94274442904361
Sresk	1522	Flovania	56274	2.70462380495433
Pasko	1362	Flovania	56274	2.4203006717134
Solank	1304	Flovania	56274	2.31723353591357
Otello	1227	Posana	1227	100
Transpasko	1188	Transak	1188	100
Tulamuk	1063	Trium	1063	100
Table 1: Summary of the number of Flitter users throughout the cities

Following the Employee ID 100 social structure, Table 2 summarizes the total outside country calls per ID. Table 3 summarizes the breakdown of outside country links for each ID. Finally, Table 4 summarizes the total number of calls per country for the entire social structure. The results from Table 4 indicate that all the surrounding countries (Posana, Transak and Trium) are possibly tied to the criminal operation. Posana has a slightly larger number of links compared to the other two countries, but the most significant concern is the number of links made by the fearless leader. Fifty percent of the fearless links (7 of 14) are with Posana, and the fearless leader has the most outside country links compared to the remaining social structure (14 of 24 outside country links). From this social structure, Posana is of more significant concern than the other surrounding countries.

ID	Name	City	Country	TotalOutsideCountry
100	schaffter	Prounov	Flovania	2
194	reitenspies	Prounov	Flovania	3
261	kushnir	Prounov	Flovania	2
563	pettersson	Prounov	Flovania	3
4994	good	Kannvic	Flovania	0
4	szemeredi	Kouvnic	Flovania	14
Total Links				24
Table 2: Number of links outside the country based on social structure from ID 100

ID	Name	Outside Country	Count
100	schaffter	Transak	1
100	schaffter	Trium	1
194	reitenspies	Posana	1
194	reitenspies	Transak	1
194	reitenspies	Trium	1
261	kushnir	Trium	2
563	pettersson	Posana	1
563	pettersson	Transak	2
4994	good	N/A	N/A
4	szemeredi	Posana	7
4	szemeredi	Transak	3
4	szemeredi	Trium	4
Total Links			24
Table 3: Outside country calls per ID.

Country	Count
Transak	7
Trium	8
Posana	9
Total Links	24
Table 4: Total Number of calls per country by social structure ID 100