Jose Quesada Director, Data Science Retreat jose@datascienceretreat.com

@quesada

• Mentors are world-class. CTOs, library authors, inventors, founders of fast-growing companies, etc

• DSR accepts fewer than 5% of the applications

• Strong focus on commercial awareness

• 5 years of working experience on average

• 30+ partner companies in Europe

DSR participants do a portfolio project

Why is DSR talking about Scala/Spark?

They are behind Scala

IBM is behind this They hired us to make training materials

What is a good question?

What is a good question?• Business case • Data available • Technology to answer the question is available • We know when the solution worked

Does he look like a bitch?

What is a good question?• Business case • Data available • Technology to answer the question is available • We know when the solution worked

The question: When should I tweet to influence the right account?

Or ‘beat Buffer at their own game’

What is a good question?• Business case

DJ J & MAX RECORDS

DJ J & MAX RECORDS

DJ J & MAX RECORDS

DJ J & MAX RECORDS

DJ J & MAX RECORDS

DJ J & MAX RECORDS

Overlap Tweet hours

Tweet frequency per UTC hour

What is a good question?• Business case • Data available

24GB

What is a good question?• Business case • Data available • Technology to answer the question is available

What is a good question?• Business case • Data available • Technology to answer the question is available • We know when the solution worked

Graph theory parts we can use to solve this problem

Graph theory primer• Random walk • Shortest path • Sampling

Sampling in networks

Sampling in NetworksNote that sampling in Networks is fraught with difficulties. One cannot simply sample the edges and nodes and expect that the sample be representative of the original network. In the graph below, a sample that missed node 1 or 2 would disconnect the two clusters, and would not have the same properties as the original

Node 11Node 2

Random surfer

Random surfer

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Random surfer

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Random surfer

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Visited more often: • Nodes with many links • Coming from frequently visited nodes

Computing Pagerank 

 

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Computing Pagerank 

 

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Computing Pagerank 

 

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Computing Pagerank 

 

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Computing Pagerank 

 

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Computing Pagerank 

 

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Teleport

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Teleport

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Teleport

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Teleport

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Teleport

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At regular node: invoke teleport operation with probability α and standard random walk with probability (1-α)

  

 

  

 

(1-α)

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Personalized pagerank

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At regular node: invoke teleport operation with probability α and standard random walk with probability (1-α). When teleporting, go to target node

  

 

  

(1-α)

Personalized pagerank

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At regular node: invoke teleport operation with probability α and standard random walk with probability (1-α). When teleporting, go to target node

(1-α)α

Personalized pagerank

• Special case of Pagerank with priors (distribution of weights over the nodes)

From: http://

Implementation

A partitioned, distributed graph processing engine is significantly more complex and difficult to build

GraphX and graphframes (new in spark 2.0)• GraphX is to RDD as graphframe is to dataframe • GraphX is lower level, and the API is scala-only. Graphframe is

very new:

• It’s not designed to be a graph database, as neo4J. Nodes and edges can contain metadata, but the query engine is not as complete as cypher

Advantages of graphframes• Graphframes have a python API

• Graphframes give you simple querying for free.  GraphFrame vertices and edges are stored as DataFrames, many queries are just DataFrame (or SQL) queries

• They contain most of the algorithms in graphX, but the API is less well-tested

• Pyspark shell instead of spark-shell

Distributed PageRank• Problem: Computing PageRank on graph too large for one

machine • Algorithm: – Shard edges randomly, – compute on each machine – average results • Basic idea: Duplicate edges from low-degree nodes. Gives an

unbiased estimator

•Nodes: 41.652.230• Edges:

1.468.365.182

Summary of implementation, benefits• Graph theory is a really flexible way to represent a problem

• Data structures to represent graphs are mature

• You can do now out-of-core, distributed graph analysis for cheap

• Implementations are there for even state-of-the-art methods

Summary, finding a problem• We live in an age of abundance (methods, data, hardware, ideas)

• Finding the question is more than half of the battle

• I had about a week to prepare this talk, but I managed to put together something that showcases what you can do with large graphs today, and it could be effective as a startup idea

• My question is not great because you cannot demonstrate that it works till you use it (common problem for unsupervised methods)

The question: When should I tweet to influence the right account?

Or ‘beat Buffer at their own game’

References: Drawing graphs• Graphs in this slide set have been drawn with Gephi • If you use Zeppelin notebook, you can draw graphs with:

drawGraph(org.apache.spark.graphx.util. GraphGenerators.rmatGraph(sc,32,60))

25 videos explaining ML on spark, 50 more to come. A bunch on graphX• For people who already know ML

• http://datascienceretreat.com/videos/data-science-with-scala-and-spark

About learning new tech over seven weekends…

About learning new tech over seven weekends• You have time and enjoy using it to learn alone: learn it ‘the

hard way’

• You are extremely motivated and talented, have money: Apply for DSR

• You want your weekends for yourself. You are already very good but want to switch jobs. Apply for codekitt

Thanks!Jose Quesada Director, Data Science Retreat jose@datascienceretreat.com @quesada

http://datascienceretreat.com/

codekitt.com