2016 SIAM International Conference on Data Mining (SDM’16)

May 5-7, 2016      Miami, Florida

 

Tutorial Title:  Problems with Incomplete Networks: Biases, Skewed Results, and Solutions

 

Abstract:  Networked representations of physical and social phenomena are often incomplete because the phenomena are partially observed. Working with incomplete networks can skew analyses. Hoping to acquire the full data is often unrealistic, but one may be able to collect data selectively to enrich the incomplete network. For example, suppose a cyber-network administrator has partially observed a network through trace-routes. Which parts of the partially observed network should be more closely examined to give the best (i.e., most complete) view of the entire network? With a limited query budget, how should this further exploration be done? Alternatively, suppose that one has obtained a sample of a Twitter retweet network from a Web site. The sample was collected for some other purpose (unbeknownst to us), and so may not contain the most useful structural information for one’s purposes. How should one best supplement this sampled data? This tutorial addresses the aforementioned questions.

 

Presenters

_      Tina Eliassi-Rad, Rutgers University & Northeastern University, tina@eliassi.org

_      Sucheta Soundarajan, Syracuse University, susounda@syr.edu

_      Ali Pinar, Sandia National Laboratories, apinar@sandia.gov

_      Brian Gallagher, Lawrence Livermore National Laboratory, bgallagher@llnl.gov

 

Schedule: This two-hour tutorial will cover the following:

_      Session One (1st Hour)

o   Graph crawling [13]

o   Graph sampling [1][2][3][4][6][7][8][21]

o   Estimating network parameters [9][22][23][24]

_      Session Two (2nd Hour)

o   Enriching nodes and edges [5][19][20]

o   Applications [14][15][16][17][18]

 

Slides: http://eliassi.org/sdm16-tutorial-slides.pdf

 

Resources & code: Will be uploaded soon. Stay Tuned…

 

Target Audience and Prerequisites: Our target audience includes researchers and practitioners in data mining and machine learning, with an interest in incomplete (a.k.a. partially observed) networks and graphs. We are targeting people who are concerned about the latent biases in the “real-world” data being used in research and industry. We expect the audience to come away with an overview of the state-of-art in enriching incomplete networks and have a better understanding of the challenges in this area. No assumption is made about familiarity with complex networks, graph mining, graph sampling, and incomplete data. A brief overview of them will be included in the tutorial.

 

References

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