seminar: algorithms for large social networks in theory...

Darren Strash:Seminar: Algorithms for Large Social Networks in Theory and Practice

Institute of Theoretical InformaticsAlgorithmics

Institute of Theoretical Informatics – Algorithmics

www.kit.eduKIT – University of the State of Baden-Wuerttemberg andNational Laboratory of the Helmholtz Association

Seminar: Algorithms for Large SocialNetworks in Theory and Practice

Yaroslav Akhremtsev · Peter Sanders · Christian SchulzDarren Strash



Introductions

Please introduce yourself, by stating:

Your nameWhat year you are in (+ Bachelor’s or Master’s)Who you work with, and what you work on (if applicable)If you want: Why social networks?



Introductions



Yaroslav Akhremtsev

PhD researcher with Peter SandersSearch data structures, some external memory algorithms



Introductions



Christian Schulz

2nd year postdoc with Peter SandersExpert in engineering efficient graph partitioning algorithms

Yaroslav Akhremtsev

PhD researcher with Peter SandersSearch data structures, some external memory algorithms



Motivation

Make money

AdvertisingProduct recommendation



Motivation

Make money


Understand our behavior

Sociologists–How do communities form? How do we identifycommunities? Who is “important”?Military–Who is a terrorist? Where will an attack happen next?Emergency Responders–How do we communicate, and howcan we collaborate better?



Motivation

Make money


Understand our behavior

Sociologists–How do communities form? How do we identifycommunities? Who is “important”?Military–Who is a terrorist? Where will an attack happen next?Emergency Responders–How do we communicate, and howcan we collaborate better?

Algorithmics

Tons of publicly available dataInteresting open problemsExploiting the structure of real-world data



Social Networks

Facebook, Google+, TwitterCommunication (e-mail, commenting on blog)Contact (meetings, sex, war)CitationsItems purchased together (+ people who made similarpurchases)Working together (co-authors, actors)Criminal/Terrorist networksPolitical corruption



What do we want to compute?

Graph Features

Subgraphs: triangles, cliques, cycles, pathsCommunity overlapClusters / partitions




Graph Features


Graph Measures

Correlation coefficientCentralityDiameterLargest clique / independent set / vertex cover




Graph Features


Graph Measures

Correlation coefficientCentralityDiameterLargest clique / independent set / vertex cover

Models

Detect and predict patterns (brokerage)



Theory and Practice

Types of articles in this area...



Theory and Practice


Theory

Problem

Application

Algorithm



Theory and Practice


Theory

Problem

Application

Algorithm

Experiments

ProblemApplication

Algorithm

Experiments



Theory and Practice


Theory

Problem

Application

Algorithm

Experiments

ProblemApplication

Algorithm

Experiments

Applications

Problem

Application

Algorithm

Understanding



Theory and Practice

This Seminar

Understanding

Experiments

Algorithm

Application

Problem

End-to-end (synthesis)

Assigned a topic problem, with aninteresting “seed” paper.Theory paper? Further discussapplications, experiments, and what welearn from applying the algorithm.Experimental paper? Further discusstheory, applications, understanding.Applications? Further discuss theory,experiments, and what we learn fromapplying the algorithm.



Course Outline

Presentations

To be made with Ipe [http://ipe7.sourceforge.net/]5-minute teaser presentation45-minute full presentation



Course Outline

Presentations


Comprehensive write-up

To be written with LATEX template12–15 pages



Course Outline

Presentations


Comprehensive write-up

To be written with LATEX template12–15 pages

Supervisor

Work with a supervisor, who will help guide you withpresentations and write-up.



Grade Breakdown: Presentations

5-minute teaser presentation - 10%

Motivation & persuasivenessClarity of presentationUse of illustrative images



Grade Breakdown: Presentations

5-minute teaser presentation - 10%

Motivation & persuasivenessClarity of presentationUse of illustrative images

45-minute full presentation (40min + 5min for questions) - 50%Same as teaser presentation, plus:

Incorporating feedback from teaser presentationCohesivenessShowing clear understanding of methodsDiscussion of theory and applicationsAbility to answer questions



Write-up - 40%

GrammarClear structureAnalysis of competitors and applicationsShow clear understanding of methodCritical analysis of techniques

Grade Breakdown: Write-up



Guidelines: Presentations

Plan to talk for about 2 minutes per slideAvoid discussing basics, use majority of time to discussunique properties of your subjectUse bullet points, not paragraphsAvoid long theorems–keep it simpleUse helpful images!Proofread your slidesUnsure about something? Talk to your supervisorIt’s ok to be nervous. Keep calm and carry on



Guidelines: Write-up

Structure:Use the templateProvide succinct, but complete abstractWhy is this topic so important, and what makes thepaper great/unique?Complete bibliography with BibTeX

Use search tools such as Google Scholar to find relatedworkThoroughly discuss related workDiscuss the end-to-end impact of your topic



Today Choose articles, set up first meeting withsupervisor24.04 Template & Ipe introduction + assign additionalarticles as needed15.05 5-minute teaser presentations12.06 2 presentations (TBD)19.06 2 presentations (TBD)26.06 2 presentations (TBD)10.07 Write-ups due

Class Schedule



Dynamic subgraph counting

A Dynamic Data Structure for Counting Subgraphs in SparseGraphsZdenek Dvorak and Vojtech Tuma

Considers graphs with bounded expansionMaintaining counts during graph updates takes polylogarithmictime

Theory






Theory

· · ·5

4

6

0

3

6

1






Theory

· · ·5

3

7

0

2

5

3



Triangle listing

Triangle Listing Algorithms: Back from the Diversionby Mark Ortmann and Ulrik Brandes

Fits existing algorithms into a common methodologyAlmost all theoretical algorithms match the best algorithmExperimental analysis on large sparse graphs

Theory Exp



Clique finding

Finding Maximal Cliques in Massive Networks by H*-graphJ. Cheng, Y. Ke, A. Fu, J. Xu Yu, and L. Zhu

External memory algorithmUsed to list all cliques in graphs with up to 10 million nodes

Experiments Th



Diameter

An Exact Algorithm for Diameters of Large Real DirectedGraphsTakuya Akiba, Yoichi Iwata, and Yuki Kawata

First algorithm to compute diameter exactly for large sparsegraphs.

Experiments



Shortest path queries

Dynamic and Historical Shortest-Path Distance Queries onLarge Evolving Networks by Pruned Landmark LabelingTakuya Akiba, Yoichi Iwata, and Yuichi Yoshida

A new algorithm for reporting the shortest distance betweenpoints in dynamic graphsSignificantly faster than previous methods

Experiments






Experiments

1






Experiments

4



Personalized PageRank

Computing Personalized PageRank Quickly by ExploitingGraph StructuresTakanori Maehara, Takuya Akiba, Yoichi Iwata, and Ken-ichiKawarabayashi

Apply a graph decomposition to acheive faster algorithm forlarge sparse graphs

Experiments

[0.98, 1.12, ..., 9.8] + = Score/Ranking



Partner prediction

Romantic Partnerships and the Dispersion of Social Ties: ANetwork Analysis of Relationship Status on FacebookLars Backstrom and Jon Kleinberg

New graph measure dispersionUse measure to determine which Facebook friend is aromantic partner

Application Understanding



1. Dynamic subgraph counting2. Triangle listing3. Clique finding4. Diameter5. Shortest path queries6. Personalized PageRank7. Partner prediction

Articles

Theory

Theory

Experiments

Experiments

Experiments

Experiments

Th

Application Understanding

Exp



1. Dynamic subgraph counting (Darren)2. Triangle listing (Yaroslav)3. Clique finding (Darren)4. Diameter (Darren)5. Shortest path queries (Yaroslav)6. Personalized PageRank (Darren)7. Partner prediction (Christian)

Articles

Contact Info:

Darren: [email protected]: [email protected]: [email protected]

seminar: algorithms for large social networks in theory...

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