Existence identifications of unobserved paths in graph-based social networks
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Existence identifications of unobserved paths in graph-based social networks Huan Wang 1,2 & Qiufen Ni 3 & Jiali Wang 4 & Hao Li 1 & Fuchuan Ni 1 & Hao Wang 5 Liping Yan 2
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Received: 3 September 2019 / Revised: 3 May 2020 / Accepted: 24 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In recent years, social networks have surged in popularity as one of the main applications of the Internet. One key aspect of social network research is exploring important unobserved network information which is not explicitly represented. This study first introduces a new path identification problem to identify the existences of unobserved paths between nodes. Given a partial social network structure where the indications of observed nodes about unobserved paths are assumed to exist, we propose a multiple-level classification based path identification method (MCPIM) for graph-based social networks. MCPIM presents the new multiple-level similarity to efficiently represent the structural positions of subgraph placeholders. Subsequently, a quantum mechanism based genetic classification algorithm (QGCA) is constructed to efficiently divide subgraph placeholders into different clusters. The nodes whose subgraph placeholders are in the same cluster owning large structural similarities are inferred to have unobserved paths.
* Hao Wang [email protected] Huan Wang [email protected] Qiufen Ni [email protected] Jiali Wang [email protected] Hao Li [email protected] Fuchuan Ni [email protected] Liping Yan [email protected] Extended author information available on the last page of the article
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Results obtained by comparing with state-of-the-art methods via extensive experiments using disparate real-world social networks show that MCPIM can well identify the existences of unobserved paths between nodes in graph-based social networks. Keywords Social network . Subgraph placeholder . Position representation . Unobserved path
1 Introduction Services based on social networks, such as WhatsApp, WeChat, Twitter, and Facebook, enable people to share information with each other. These generalized networks are usually denoted by graphs, where the nodes represent individuals (people, organizations, or other social entities) and the edges represent social relations or interactions (friendship, co-working, or information exchange) [1]. Recent years have witnessed the great success of social networks in various fields, from user identity linkage [29] to evolutionary behavior discovery [31] and from mining opinion leaders [32] to fake news detection [30]. One challenging work is exploring important unobserved information which is not explicitly represented in the graph-based social network. To date, this line of research typically focused on “Link Prediction Problem” which predicts the unobserved edges between nodes in the network [26]. In this problem setting of “Link Prediction Problem”, the nodes of the entire network are known, and unobserved edges are derived fro
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