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• W2529121416 endingPage "39" @default.
• W2529121416 startingPage "1" @default.
• W2529121416 abstract "Networks represent relationships between entities in many complex systems, spanning from online social interactions to biological cell development and brain connectivity. In many cases, relationships between entities are unambiguously known: are two users “friends” in a social network? Do two researchers collaborate on a published article? Do two road segments in a transportation system intersect? These are directly observable in the system in question. In most cases, relationships between nodes are not directly observable and must be inferred: Does one gene regulate the expression of another? Do two animals who physically co-locate have a social bond? Who infected whom in a disease outbreak in a population? Existing approaches for inferring networks from data are found across many application domains and use specialized knowledge to infer and measure the quality of inferred network for a specific task or hypothesis. However, current research lacks a rigorous methodology that employs standard statistical validation on inferred models. In this survey, we examine (1) how network representations are constructed from underlying data, (2) the variety of questions and tasks on these representations over several domains, and (3) validation strategies for measuring the inferred network’s capability of answering questions on the system of interest." @default.
• W2529121416 created "2016-10-14" @default.
• W2529121416 creator A5055448715 @default.
• W2529121416 creator A5060005215 @default.
• W2529121416 creator A5084093422 @default.
• W2529121416 date "2018-04-17" @default.
• W2529121416 modified "2023-09-25" @default.
• W2529121416 title "Network Structure Inference, A Survey" @default.
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