May 3-5, 2018 San Diego, California
Tutorial Title: Problems with Partially Observed (Incomplete) Networks: Biases, Skewed Results, and Solutions
Abstract: Networked representations of physical and social phenomena are ubiquitous. Examples include social and information networks, technological and communication networks, co-purchasing networks, etc. These networks are often incomplete because the phenomena are partially observed. Working with incomplete networks can skew analyses. Acquiring the full data is often unrealistic (e.g., obtaining the Twitter Firehose is not viable), but one may be able to collect data selectively to enrich the incomplete network. With a limited query budget, which parts of a partially observed network should be examined to give the best (i.e., most complete) view of the entire network? 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 above questions. In particular, it we will focus on multi-armed bandit and reinforcement learning solutions
· Tina Eliassi-Rad, Northeastern University, email@example.com
· Sucheta Soundarajan, Syracuse University, firstname.lastname@example.org
· Sahely Bhadra, Indian Institute of Technology, Palakkad, Kerala, India, email@example.com
Schedule: This two-hour tutorial will cover the following:
· Complex networks and their properties
· Partial observability, biases, and skewed results
· The network completion problem
· Multi-armed bandit solutions
· Limits of learning in incomplete networks
Slides: Available here.
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.
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