File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Structural Characteristics in Historical Networks Reveal Changes in Political Culture: An Example From Northern Song China (960–1127 C.E.)

TitleStructural Characteristics in Historical Networks Reveal Changes in Political Culture: An Example From Northern Song China (960–1127 C.E.)
Authors
KeywordsChinese history
cultural evolution
social network analysis
structural balance
valence prediction
Issue Date2023
Citation
Ceur Workshop Proceedings, 2023, v. 3558, p. 263-273 How to Cite?
AbstractThe mass digitization and datafication of historical records brings about new possibilities to study or re-assess a broad range of individual events. By evaluating microlevel events in a social context simultaneously, insights into the macrolevel dynamics of society can be gained. This paper presents an innovative framework for historical network research that allows the comparison of structural characteristics in networks across different time periods, and illustrates it with an example of the political networks of Northern Song China. By using machine learning models for valence prediction and tracking the changes of structural characteristics related to structural balance, clustering, and connectivity in temporal networks, we reveal that the mid-to-late 11th century, during which political reforms took place, was characterized by political pluralism and even political tolerance, compared to earlier or later periods. The replicable framework proposed in this paper is capable of revealing significant historical changes that would otherwise be obscured, shedding light on the underlying historical dynamics of such changes.
Persistent Identifierhttp://hdl.handle.net/10722/365317
ISSN
2023 SCImago Journal Rankings: 0.191

 

DC FieldValueLanguage
dc.contributor.authorShang, Wenyi-
dc.contributor.authorChen, Song-
dc.contributor.authorChen, Yuqi-
dc.contributor.authorDiesner, Jana-
dc.date.accessioned2025-11-04T09:40:13Z-
dc.date.available2025-11-04T09:40:13Z-
dc.date.issued2023-
dc.identifier.citationCeur Workshop Proceedings, 2023, v. 3558, p. 263-273-
dc.identifier.issn1613-0073-
dc.identifier.urihttp://hdl.handle.net/10722/365317-
dc.description.abstractThe mass digitization and datafication of historical records brings about new possibilities to study or re-assess a broad range of individual events. By evaluating microlevel events in a social context simultaneously, insights into the macrolevel dynamics of society can be gained. This paper presents an innovative framework for historical network research that allows the comparison of structural characteristics in networks across different time periods, and illustrates it with an example of the political networks of Northern Song China. By using machine learning models for valence prediction and tracking the changes of structural characteristics related to structural balance, clustering, and connectivity in temporal networks, we reveal that the mid-to-late 11th century, during which political reforms took place, was characterized by political pluralism and even political tolerance, compared to earlier or later periods. The replicable framework proposed in this paper is capable of revealing significant historical changes that would otherwise be obscured, shedding light on the underlying historical dynamics of such changes.-
dc.languageeng-
dc.relation.ispartofCeur Workshop Proceedings-
dc.subjectChinese history-
dc.subjectcultural evolution-
dc.subjectsocial network analysis-
dc.subjectstructural balance-
dc.subjectvalence prediction-
dc.titleStructural Characteristics in Historical Networks Reveal Changes in Political Culture: An Example From Northern Song China (960–1127 C.E.)-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-85178653673-
dc.identifier.volume3558-
dc.identifier.spage263-
dc.identifier.epage273-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats