Explaining Missing Data in Graphs: A Constraint-based Approach

Conference · Sun Jun 06 04:00:00 EDT 2021

OSTI ID:1821698

Song, Qi ^[1]; Lin, Peng ^[2]; Ma, Hanchao ^[3]; Wu, Yinghui ^[3]

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1. Amazon.com
2. Washington State University
3. Case Western Reserve University

Abstract: This paper introduces a constraint-based approach to clarify missing values in graphs. Our method capitalizes on a set S of graph data constraints. An explanation is a sequence of operational enforcement of S towards the recovery of interested yet missing data (e.g., attribute values, edges). We show that constraint-based approach helps us to understand not only why a value is missing, but also how to recover the missing value. We study S-explanation problem, which is to compute the optimal explanations with guarantees on the informativeness and conciseness. We show the problem is in ?P^2 for established graph data constraints such as graph keys and graph association rules. We develop an efficient bidirectional algorithm to compute optimal explanations, without enforcing S on the entire graph. We also show our algorithm can be easily extended to support graph refinement within limited time, and to explain missing answers. Using real-world graphs, we experimentally verify the effectiveness and efficiency of our algorithms.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1821698
• Report Number(s): PNNL-SA-159919
• Country of Publication: United States
• Language: English

Related Subjects

Data Analytics