Social Network Analysis: An Introduction
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Social network analysis takes as its starting point the premise that social life is created primarily and most importantly by relations and the patterns formed by these relations.Social networks are formally defined as a set of nodes (or network members) that are tied by one or more types of relations (Wasserman and Faust, 1994).Because network analysts take these networks as the primary building blocks of the social world, they not only collect unique types of data, they begin their analyses from a fundamentally different perspective than that adopted by individualist or attribute-based social science.For example, a conventional approach to understanding high-innovation regions such as Silicon Valley would focus on the high levels of education and expertise common in the local labour market.Education and expertise are characteristics of the relevant actors.By contrast, a network analytic approach to understanding the same phenomenon would draw attention to the ways in which mobility between educational institutions and multiple employers has created connections between organizations (Fleming et al., forthcoming).Thus, people moving from one organization to another bring their ideas, expertise, and tacit knowledge with them.They also bring with them the connections they have made to coworkers, some of whom have moved on to new organizations themselves.This pattern of connections between organizations, in which each organization is tied through its employees to multiple other organizations, allows each to draw on diverse sources of knowledge.Since combining previously disconnected ideas is the heart of innovation and a useful problem-solving strategy (Hargadon and Sutton, 1997), this pattern of connections -not just the human capital of individual actors -leads to accelerating rates of innovation in the sectors and regions where it occurs (Fleming et al., forthcoming).In this chapter, we begin by discussing issues involved in defining social networks, and then go on to describe three principles implicit in the social network perspective.We explain how these principles set network analysis apart from attribute-or group-based perspectives.In Section II we summarize the theoretical roots of network analysis and the current state of the field, while in Section III we discuss theoretical approaches to asking and answering questions using a network analytic approach.In Section IV we turn our attention to social network methods -which we see as a set of tools for applying network theory rather than as the defining feature of network analysis.In our concluding section we argue that social network analysis is best understood as a perspective within the social sciences and not as a method or narrowly-defined theory.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle