[Slides for presentation to STEAM Vent Meetup on Jan 8th, 2015.] Presents a (somewhat fictional) progression of ideas using graphs to analyze data, and how blindly following this progression leads to failed analysis because we did not consider supporting human reasoning as an end goal. Audience is general math and science knowledgeable group.

1. the
BIGger
data story
Ben Keller [@vinegarbin bjkeller.github.io linkedin.com/in/bjkeller ]
STEAM Vent
8 January 2015
ger/
Creative Commons Attribution-ShareAlike 4.0 International License

2. prelude

3. na
i=1
wi⇤
f
!
ma
i=1
vi⇤
la
i=1
xi⇤
g
!
na
i=1
wi⇤
Given
⇤ = K /Iwhere
compute
Im g = ker fsuch that

4. algorithmic problem
na
i=1
wi⇤
f
!
ma
i=1
vi⇤
la
i=1
xi⇤
g
!
na
i=1
wi⇤
Given
⇤ = K /Iwhere
compute
Im g = ker fsuch that
input
output

5. na
i=1
wi⇤
f
!
ma
i=1
vi⇤
la
i=1
xi⇤
g
!
na
i=1
wi⇤
Given
⇤ = K /Iwhere
compute
Im g = ker fsuch that
a graph

6. a (directed) graph
a
bc
d
1 2
3

7. a (directed) graph
vertices
a
bc
d
1 2
3

8. a (directed) graph
an edge (arc)
a
bc
d
1 2
3

9. graphs
a
bc
d
1 2
3
used to represent relationships
in our algebra, represents
which multiplications work
ab, abc, ad, aba, …
ba = bd = cb = … = 0
while others don’t

10. act I

11. recommender systems
Abby
Brian
Charles
David
apples
bananas
cherries
doughnuts
eggs
tell us what we might like
based on similarities and
what others have liked
can represent data as a
graph
how do algorithms work
with respect to graph?

12. recommender graphs
Brian
Charles
cherries
apples
model similarity of items
by shared likes of users

13. recommender graphs
Brian
Charles
cherries
apples
model similarity of items
by shared likes of users
to construct new edges
weighted by number of
shared users
cherries
apples

14. similarity graph
Brian
Charles
cherries
apples
model similarity of items
by shared likes of users
to construct new edges
weighted by number of
shared users
cherries
apples
giving a new graph
representing similarity
between items
apples
bananas
cherries
doughnuts
eggs

15. making recommendations
with similarity graph
apples
bananas
cherries
doughnuts
eggs
apples
bananas
Abby
combining graph of “likes”

16. making recommendations
make list by ranking them by weight
recommend items similar to those a user likes
apples
bananas
cherries
doughnuts
eggs
Abby

17. interlude

18. genetic disease
• causal factors of disease are
inherited
• assumed to manifest
themselves as variations of
the genome
• may combine in complex
ways

19. act II

20. a genetic disease question
chr A
chr B
have paired regions of
genome where variations
cooccur in bipolar
disorder patients
how are these
regions related
by biology?

21. a genetic disease question
chr A
chr B
…
… …
genes in regions
“biology” of genes

22. a genetic disease question
…
… …
…
……
genes “biology”

23. a familiar graph
• model biological factors of
genes by words found in
descriptions of what gene
does
• gives us a graph similar to
starting graph for
recommenders
• form similarity graph only
between genes in regions
…
……

24. sCDKN2A/B
PPARG
HHEX TCF7L2
"mortality"
"g1""repression"
MTHFR
TNF
"ethanol""intake" "consumption"
gene-
environment
interaction
NURR1
FOS
"cocaine"
dopamine
signalling
Look at local
connections
between genes
words can help find explanations

25. interlude

26. user's ways of thinking
how user
thinks
about:
tasks
biology of
disease

27. tool support
how tools
allow
manipulations
represent
information

28. manipulations
representation
tasks
knowledge
user has to manage relationships in her head

29. cognitive engineering
Interdisciplinary approach to supporting
user performance in complex systems

30. act III

31. a model of biology
A
B
C
D

32. A
B
C
D
genetic variations

33. A
B
C
D
genetic variations
affect
how cells/
organs work

34. A
B
C
D
genetic variations
affect
how cells/
organs work
resulting in
symptoms of
disease

35. A
B
C
D
genetic variations
affect
how cells/organs
work
resulting in
symptoms of
disease
groups of
in particular ways
shared
certain
^
^
^
and there are other things we don’t know about

36. A
B
C
D
data is observation of
measures in cells/organs
of individuals

37. with our graph trick we can take data
relating people to observations
and create a graph showing which
observations are shared by groups of people

38. and we can do it for each kind of observation

39. how do
these
relate?
scientist left to decide:
but…

40. scientist wants to know
how groups here
affect values below
and how values here
are affected by groups
of values above

41. scientist wants to know
how groups here
affect values below
and how values here
are affected by groups
of values above
but cannot answer easily with the tools we’ve chosen

42. A
B
C
D
we will ultimately solve this problem by
supporting the scientist in her reasoning
not by choosing our favorite tool

43. finale

44. data science
math/
statistics
computation

45. data science
math/
statistics
computation
human
reasoning

46. This work is licensed under a
Creative Commons Attribution-ShareAlike
4.0 International License.