Obtaining actionable insights from large datasets requires the use methods that must be, at once, fast, scalable, and statistically sound. This is the field of study of algorithmic data science, a discipline at the border of computer science and statistics. In this talk I outline the fundamental questions that motivate research in this area, present a general framework to formulate many problems in this field, introduce the challenges in balancing theoretical and statistical correctness with practical efficiency, and I show how sampling-based algorithms are extremely effective at striking the correct balance in many situations, giving examples from social network analysis and pattern mining. I will conclude with some research directions and areas for future explorations.

1. Algorithmic Data Science
=
Theory + Practice
Matteo Riondato – Labs, Two Sigma Investments
@teorionda – http://matteo.rionda.to
IEEE MIT URTC – November 5, 2016
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2. Matteo Riondato
Ph.D. in CS
Working at
Labs, Two Sigma Investments (Research Scientist);
CS Dept., Brown U. (Visiting Asst. Prof.);
Doing research on algorithmic data science;
Tweeting @teorionda;
Reading matteo@twosigma.com;
“Living” at http://matteo.rionda.to.
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3. Conjecture
Let X be a scientific discipline. Then
21st
-century X = datascience (X) + ε .
Partial evidence: “Computational X” exists for many X.
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4. data science : 21st
century = statistics : 20th
century
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5. data science for 21st
century society



questions
data
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6. data science
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7. data science
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8. data science =



1/4 data representation and management
1/4 mathematical and statistical modeling
1/4 computational thinking and algorithms
1/4 domain expertise
Shake well, and strain into a cocktail glass.
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9. domain expertise modeling
management
algorithms
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10. domain expertise modeling
management
algorithms
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11. domain expertise modeling
management
algorithms
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12. algorithmic data science:
=
algorithms for/with:



approximation guarantees
data streams
Spark/MapReduce
sampling
statistical testing
graph analysis
. . . 9 / 24

13. algorithmic data science
=
theory
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14. algorithmic data science
≈
theory + practice
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15. algorithmic data science
=
(theory × practice)(theory×practice)
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16. Example
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17. Scientific question: Find relevant webpages on the web, influential participants in
a email chain, key proteins in a network, . . .
Data representation: represent the data as a graph G = (V , E).
a
h
b
g f e
c d
Modeling question: What are the important nodes in a graph G = (V , E)?
We need f : V → R+ to express the importance of a node.
The higher is f (x), the more important is x ∈ V .
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18. Domain Knowledge / Modeling: Assume that
1) every node wants to communicate with every node; and
2) communication progresses along Shortest Paths (SPs).
Then, the higher the no. of SPs that a node v belongs to, the more important v is.
Definition
For each node x ∈ V , the betweeness b(x) of x is:
b(x) =
1
n(n − 1) u=x=v∈V
σuv (x)
σuv
∈ [0, 1]
• σuv : number of SPs from u to v, u, v ∈ V ;
• σuv (x): number of SPs from u to v that go through x.
I.e., b(x) is weighted fraction of SPs that go through x, among all SPs in G.
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19. a
h
b
g f e
c d
Node x a b c d e f g h
b(x) 0 0.250 0.125 0.036 0.054 0.080 0.268 0
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20. Algorithmic question: How to compute all b(x)?
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21. Algorithmic question: How to compute all b(x)?
Brandes’ Algorithm
Intuition: For each vertex s ∈ V :
1) Build the SP DAG from s via Dijkstra/BFS;
2) Traverse the SP DAG from the most distant node towards s, in reverse order of
distance. During the walk, appropriately increment b(v) of each non-leaf node v
traversed.
Source s: 1
1
234
567
89
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22. Algorithmic question: How to compute all b(x)?
Brandes’ Algorithm
Intuition: For each vertex s ∈ V :
1) Build the SP DAG from s via Dijkstra/BFS;
2) Traverse the SP DAG from the most distant node towards s, in reverse order of
distance. During the walk, appropriately increment b(v) of each non-leaf node v
traversed.
Source s: 1
1
234
567
89
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23. Algorithmic question: How to compute all b(x)?
Brandes’ Algorithm
Intuition: For each vertex s ∈ V :
1) Build the SP DAG from s via Dijkstra/BFS;
2) Traverse the SP DAG from the most distant node towards s, in reverse order of
distance. During the walk, appropriately increment b(v) of each non-leaf node v
traversed.
Source s: 1
1
234
567
89
(update to b(v) not shown)
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24. Algorithmic question: How to compute all b(x)?
Brandes’ Algorithm
Intuition: For each vertex s ∈ V :
1) Build the SP DAG from s via Dijkstra/BFS;
2) Traverse the SP DAG from the most distant node towards s, in reverse order of
distance. During the walk, appropriately increment b(v) of each non-leaf node v
traversed.
Source s: 1
1
234
567
89
(update to b(v) not shown)
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25. Algorithmic question: How to compute all b(x)?
Brandes’ Algorithm
Intuition: For each vertex s ∈ V :
1) Build the SP DAG from s via Dijkstra/BFS;
2) Traverse the SP DAG from the most distant node towards s, in reverse order of
distance. During the walk, appropriately increment b(v) of each non-leaf node v
traversed.
Source s: 1
1
234
567
89
(update to b(v) not shown)
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26. Algorithmic question: How to compute all b(x)?
Brandes’ Algorithm
Intuition: For each vertex s ∈ V :
1) Build the SP DAG from s via Dijkstra/BFS;
2) Traverse the SP DAG from the most distant node towards s, in reverse order of
distance. During the walk, appropriately increment b(v) of each non-leaf node v
traversed.
Source s: 1
1
234
567
89
(update to b(v) not shown)
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27. Algorithmic question: How to compute all b(x)?
Brandes’ Algorithm
Intuition: For each vertex s ∈ V :
1) Build the SP DAG from s via Dijkstra/BFS;
2) Traverse the SP DAG from the most distant node towards s, in reverse order of
distance. During the walk, appropriately increment b(v) of each non-leaf node v
traversed.
Source s: 1
1
234
567
89
(update to b(v) not shown)
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28. Algorithmic question: How to compute all b(x)?
Brandes’ Algorithm
Intuition: For each vertex s ∈ V :
1) Build the SP DAG from s via Dijkstra/BFS;
2) Traverse the SP DAG from the most distant node towards s, in reverse order of
distance. During the walk, appropriately increment b(v) of each non-leaf node v
traversed.
Source s: 1
1
234
567
89
(update to b(v) not shown)
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29. Algorithmic question: How to compute all b(x)?
Brandes’ Algorithm
Intuition: For each vertex s ∈ V :
1) Build the SP DAG from s via Dijkstra/BFS;
2) Traverse the SP DAG from the most distant node towards s, in reverse order of
distance. During the walk, appropriately increment b(v) of each non-leaf node v
traversed.
Source s: 1
1
234
567
89
(update to b(v) not shown)
Time complexity: O(nm + n2 log n)
n Dijkstra’s, plus n backward walks,
taking at most n each
Too much even with just 104 nodes.
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30. Modeling / Domain knowledge:
High-quality approximations of all BCs are sufficient.
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31. Modeling / Domain knowledge:
High-quality approximations of all BCs are sufficient.
Let ε ∈ (0, 1), and δ ∈ (0, 1) be user-specified parameters;
An (ε, δ)-approximation is a set {b(x), x ∈ V } of n values s.t.
Pr(∃x ∈ V s.t. |b(x) − b(x)| > ε) ≤ δ
i.e., with prob. ≥ 1 − δ, for all x ∈ V , b(x) is within ε of b(x):
a uniform probabilistic guarantee over all the estimations.
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32. Algorithmic question:
How to obtain an (ε, δ)-approximation quickly?
Answer:
Sampling
Instead of computing all the SPs from each node x ∈ V , compute them only from
some randomly chosen nodes (samples).
Theory question:
How many samples do we need to obtain an (ε, δ)-approximation?
The more the better, but really, how many?
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33. How many samples do we need to obtain an (ε, δ)-approximation?
Theory: Hoeffding Bound + Union Bound
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34. How many samples do we need to obtain an (ε, δ)-approximation?
Theory: Hoeffding Bound + Union Bound
Need O
1
ε2
log |V | + log
1
δ
samples
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35. How many samples do we need to obtain an (ε, δ)-approximation?
Theory: Hoeffding Bound + Union Bound
Need O
1
ε2
log |V | + log
1
δ
samples
Comments
Practice:
Fewer samples than the above are sufficient for (ε, δ)-approx.
Theory:
Dependency on |V | and not on edge structure seems wrong.
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36. How many samples do we need to obtain an (ε, δ)-approximation?
Theory: Vapnik-Chervonenkis (VC) Dimension
Developed to evaluate supervised learning classifiers.
We twisted it to work in a non-supervised graph mining problem.
“The most practical theory ever” – Me, right now
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37. How many samples do we need to obtain an (ε, δ)-approximation?
Theory: Vapnik-Chervonenkis (VC) Dimension
Developed to evaluate supervised learning classifiers.
We twisted it to work in a non-supervised graph mining problem.
“The most practical theory ever” – Me, right now
Need O
1
ε2
log diam(G) + log
1
δ
samples
Decreased sample size exponentially on small-world networks.
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38. How many samples do we need to obtain an (ε, δ)-approximation?
Theory: Vapnik-Chervonenkis (VC) Dimension
Developed to evaluate supervised learning classifiers.
We twisted it to work in a non-supervised graph mining problem.
“The most practical theory ever” – Me, right now
Need O
1
ε2
log diam(G) + log
1
δ
samples
Decreased sample size exponentially on small-world networks.
Comments
Practice: Great improvement but still too many samples.
Theory: Graphs with the same diameter are not equally “hard”.
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39. How many samples do we need to obtain an (ε, δ)-approximation?
Theory: Progressive sampling + Rademacher Averages
Let’s start sampling, use the sample to decide when to stop.
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40. How many samples do we need to obtain an (ε, δ)-approximation?
Theory: Progressive sampling + Rademacher Averages
Let’s start sampling, use the sample to decide when to stop.
Stop when ηi ≤ ε, where ηi is. . .
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41. How many samples do we need to obtain an (ε, δ)-approximation?
Theory: Progressive sampling + Rademacher Averages
Let’s start sampling, use the sample to decide when to stop.
Stop when ηi ≤ ε, where ηi is. . .
ηi = 2 min
t∈R+
1
t
ln
(r,C)∈T
et2
r2
/(2S2
i )
+ 3
(i + 1) ln(2/δ)
2Si
Comments
Practice: Getting closer to the empirical bound
Theory: Proving stuff is getting complicated (isn’t that good?)
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42. Theory + Practice:
Get rid of “theoretical elegance” while maintaining correctness.
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43. Theory + Practice:
Get rid of “theoretical elegance” while maintaining correctness.
Let
gS(x, y) = 2 exp −2 x2
(y − 2RF (S))2
+ exp − ((1 − x)y + 2xRF (S))
φ
2RF (S)
(1 − x)y + 2xRF (S)
− 1 .
Then compute
min
x,ξ
ξ
s.t. gS(x, ξ) ≤ η
ξ ∈ (2RF (S), 1]
x ∈ (0, 1)
and check if ξ < ε.
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44. To be a data scientist, you need to get your hands dirty in data.
To be an algorithmic data scientist,
you need to get your hands dirty in



data
theory
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45. Other examples



pattern mining
(Rademacher Averages)
selectivity of database queries
(VC-dimension)
triangle counting from data streams
(non-i.i.d. sampling)
graph summarization
(Szemerédi Regularity)
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46. 1) Embrace data science
2) Combine theory and practice
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47. 1) Embrace data science
2) Combine theory and practice
Thank you!
EML: matteo@twosigma.com TWTR: @teorionda
WWW: http://matteo.rionda.to
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