{"product_id":"stochastic-actor-oriented-models-for-longitudinal-networks-1","title":"Stochastic Actor-Oriented Models for Longitudinal Networks","description":"Interest in social networks – patterns of relations between social actors such as individuals, corporations, and countries – has grown in the last decade, and analysis of longitudinal network data has moved forward strongly. Social networks often change; understanding this process, where changes lead to other changes, requires tools that can uncover the rules driving these changes. In 'Stochastic Actor-Oriented Models for Longitudinal Networks,' Tom A. B. Snijders and Christian Steglich bring together the first comprehensive textbook on the Stochastic Actor-Oriented Model (SAOM), a leading method for analyzing dynamic network data. They present the diverse SAOM variants developed over the past three decades, covering the co-evolution of networks and actor attributes as well as the co-evolution of multiple one-mode and two-mode networks. Providing a foundation for applying the methods as well as advice for problems encountered in practice, this book offers a detailed guide into the best practices of modeling longitudinal network data.","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57502190666101,"sku":"9781009843683","price":49.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9781009843683.jpg?v=1786781010","url":"https:\/\/backstory.london\/products\/stochastic-actor-oriented-models-for-longitudinal-networks-1","provider":"Backstory","version":"1.0","type":"link"}