A pioneering work by Maiya and Berger-Wolf [22] suggested the benefit of the bias of crawl- based sampling strategies in identifying high-degree nodes. Their finding suggest the hypothesis that the bias in crawl- based sampling is beneficial also for identifying the set of key nodes.
May 5, 2020
Apr 20, 2020 · Abstract: We study the problem of identifying a set of key nodes from a network when limited knowledge about its structure is available.
Specifically, we investigate the effect of conventional network sampling strategies on the solutions found for two types of key node set identification problems ...
Article "Benefits of Bias in Crawl-Based Network Sampling for Identifying Key Node Set" Detailed information of the J-GLOBAL is an information service ...
CiNii Labs, CiNii's experimental service public site, has been released. Benefits of Bias in Crawl-Based Network Sampling for Identifying Key Node Set. DOI ...
It is shown that certain biases are beneficial for many applications, as they "push the sampling process towards inclusion of desired properties, ...
ABSTRACT. From social networks to P2P systems, network sampling arises in many settings. We present a detailed study on the nature of biases in network ...
We show that certain biases are, in fact, beneficial for many applications, as they "push" the sampling process towards inclusion of desired properties. Finally ...
Benefits of Bias in Crawl-Based Network Sampling for Identifying Key Node Set. 2020, IEEE Access. Electrical Network Operational Vulnerability Evaluation ...
Benefits of Bias in Crawl-Based Network Sampling for Identifying Key Node Set · Sho TsugawaH. Ohsaki. Computer Science. IEEE Access. 2020. TLDR. The results ...