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Dynamics of Peer-to-Peer Networks or Who is Going to be The Next Pop Star?. Yuval Shavitt School of Electrical Engineering [email protected] http://www.eng.tau.ac.il/~shavitt. Credits. Talk is based on the papers:

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Dynamics of peer to peer networks or who is going to be the next pop star l.jpg

Dynamics of Peer-to-Peer Networks or Who is Going to be The Next Pop Star?

Yuval Shavitt

School of Electrical Engineering

[email protected]

http://www.eng.tau.ac.il/~shavitt


Credits l.jpg
Credits

Talk is based on the papers:

  • Static and dynamic characterization of the Gnutella network [Shaked-Gish, S, Tankel, IPTPS 2007]

  • How to predict the next pop star? [Koenigstein, S, Tankel, KDD 2008]


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What are Peer-to-Peer Networks?

client

client

  • The common computing paradigm is client-server

    • Server waits for requests (on a known port)

    • Client sends a request

    • Server serves the client

    • Examples: WWW, FTP, SMTP (e-mail), …..

  • Peer-to-peer networks:

    • Each end-point is both client and server

server

client

client

client

client

client

client


The gnutella network l.jpg
The Gnutella Network

  • Gnutella: The most popular sharing network on the Internet

  • According to the Digital Music News Research Group40% market share in Q4 2007

  • Limewire: The most popular file sharing client in the world. Dominates the Gnutella network.


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The Gnutella Protocol

  • Originally: a flat peer-to-peer distributed protocol.

    • Churn caused instability

  • Today: a 2-level tiered system

    • Stable nodes are promoted to become ultrapeers

    • Queries carry OOB address: The originator’s address or in most cases when the client is firewalled, this is the ultrapeer’s address


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Locating the Origin IP address

UP

UP

listener

UP

IP resolution Process:

  • Detect the U.P. IP

  • Discard queries with more than 2 hops

  • Discard queries with 2 hops and same IP

  • Intercept queries with 2 hops and different IPs

peer

peer

peer

Cancels the bias for rare queries

Introduces bias against firewalled clients


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Data Sets

  • First study:

    • Jul 2006 - Nov 2006

    • 665,000,000 world-wide geo-identified queries

  • Second study

    • Oct 2006 – Jul 2007, Sundays only

    • 310,000,000 USA geo-identified queries

  • A network crawl of 24 hours

    • 1.2M users

    • 533,000 different songs

      Largest studies ever performed

      in length and depth









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How to Predict Artist’s Success?

Noam Koenigstein, Y. Shavitt, and Tomer Tankel. Spotting Out Emerging Artists Using Geo-Aware Analysis of P2P Query Strings. The 2008 ACM SIGKDD Conference, August 2008, Las Vegas, NV, USA.


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The Word of Mouth Effect

The Divergence can be used to predict a new product success probability [Garber et al., Marketing Science 2004]


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The divergence

  • When measured against the uniform distribution, maximum is achieved when P is a  function.

    • True for both Kullback-Leiblar and Jensen-Shannon

    • This is the case when emerging artists are considered

  • Non uniform distribution of potential adopters:


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Party Like a Rockstar in 2007

Week 6: The string “party like a rockstar” is detected by the algorithm

Week 8: Atlanta’s popularity chart in (Feb 18th)

Week 15: Atlanta based Shop Boyz sign contract with Universal Recordings

Week 18: The song first enters the Billboard Hot 100 on (80th position)

Week 23: Reached 2nd position on Billboard Hot 100

Ranked only

10,156

on the global chart


Party like a rockstar l.jpg
Party Like a Rockstar

Shop Boyz related queries in February 2007

Shop Boyz Popularity and Divergence in 2007


Soulja boy l.jpg
Soulja Boy

  • Detected by our alg: already in 2006.

  • The string “soulja boy” entered the “Atlanta queries top 100” already in October 2006

  • Entered the Bubbling Under R&B/Hip-Hop Singles in the 23rd of June 2007

  • Later ranked first in the following Billboard charts:Hot 100, Hot Rap Tracks, Hot Videoclip, Hot RingMasters and Hot Ringtones


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Yung Berg

  • Active in LA

  • Week 2: Entered LA top 100

  • Week 15: First appeared on the Billboard charts

  • Week 32: Reached 18 on the Billboard Top 100



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The Detection Algorithm

  • Input: A list of Geo-identified P2P Query stringsOutput: A list of locally popular query string with high probability to become globally popular

  • Build local and global popularity charts

  • local popularity is detected using local and global popularity thresholds

  • Looking for local popularity growth trends from week to week

  • Filtering:Non-music related content, and already familiar artists are characterized by uniform distribution


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Local Popularity

  • Not all queries are “products”, thus divergence is not effective (e.g., rare typos)

  • Detection is based on local popularity:


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ATPL - All Times Popular List

  • Initialization: All the strings that reached global popularity in 2006

  • Weekly aggregation

  • Filters non-volatile string:

    • adult related, e.g., “porn”

    • well established artists, e.g., “madonna”, “avril lavigne”

    • Movies, software, etc.








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Correlation Measurements

  • Modified time series correlation

  • P2P correlation with the Billboard:



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Prediction Results

  • Example:When a song enters the Billboard will it reach “top 20”?

  • Precision: 89%, Recall: 80%On average songs pass the threshold 2.83 weeks before reaching top Billboard rank

  • More details:Koenigstein, Shavitt, and Zilberman, AdMIRe2009


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Summary

  • Following activity in the Internet can help up detect trends before they are visible

    • P2P networks

    • Social networks

    • Blogs

    • Talk-backs

    • Searches

  • More at http://www.eng.tau.ac.il/~shavitt


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