Amati
Biographic Data
| ID | 36878 |
|---|---|
| NAME | Amati |
| FAMILY NAME | Amati |
| SIGNATURE | AMATI |
| AFFILIATIONS | ETH Zurich |
| ORCID | 0000-0003-1190-1237 |
| VERIFIED | Yes |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 34 |
| WORKS AS AUTHOR | 11 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 3 |
Modeling the duality of content niches and user interactions on online social media platforms
Individuals use online social media platforms for social interaction and to create, consume and share creative content. Within large platforms, individuals’ interaction with content and the congregation of individuals sharing similar interests might have a dual relationship, leading to the emergence of focused content niches of specific individuals and types of content. These niches are smaller-scale social settings that may facilitate and struct…
Random Graph Models
Random graph models are probability distributions that model the structure of a network. These models are commonly applied to test hypotheses concerning the characteristics that might have led to an observed network, and generate networks according to different processes of tie formation. In this chapter, I discuss the application of random graph models to the study of past networks. After a brief introduction to random graph models, I describe h…
Random effects in dynamic network actor models
Dynamic Network Actor Models (DyNAMs) assume that an observed sequence of relational events is the outcome of an actor-oriented decision process consisting of two decision levels. The first level represents the time until an actor initiates the next relational event, modeled by an exponential distribution with an actor-specific activity rate. The second level describes the choice of the receiver of the event, modeled by a conditional multinomial …
Network mechanisms and network models
This chapter introduces the concept of network mechanisms and compares it to the notion of social mechanisms in Analytical Sociology. It discusses how statistical network models and empirically calibrated simulation models are applied to investigate network mechanisms. Different statistical network models are reviewed and compared, in particular conditional uniform graph (CUG) tests, quadratic assignment procedure (QAP) regressions, exponential r…
A Framework for Reconstructing Archaeological Networks Using Exponential Random Graph Models
Reconstructing ties between archaeological contexts may contribute to explain and describe a variety of past social phenomena. Several models have been formulated to infer the structure of such archaeological networks. The applicability of these models in diverse archaeological contexts is limited by the restricted set of assumptions that fully determine the mathematical formulation of the models and are often articulated on a dyadic basis. Here,…
Applying event history analysis to explain the diffusion of innovations in archaeological networks
The simple dyadic structure of a network is the basis for studying a wide variety of entities and their relationships, as well as the outcomes of processes such as the diffusion of innovations. Here, we apply models from event history analysis and cultural evolutionary theory to investigate whether and by what means network ties facilitated the transmission of certain cultural traits in past complex societies. To illustrate the application of the…
Social relations and life satisfaction
Social capital is defined as the individual’s pool of social resources found in his/her personal network. A recent study on Italians living as couples has shown that friendship relationships, beyond those within an individual’s family, are an important source of support. Here, we used data from Aspects of Daily Life, the Italian National Statistical Institute’s 2012 multipurpose survey, to analyze the relation between friendship ties and life sat…
Some days are better than others
Reconstructing Archaeological Networks with Structural Holes
Model-based reconstruction is an approach to infer network structures where they cannot be observed. For archaeological networks, several models based on assumptions concerning distance among sites, site size, or costs and benefits have been proposed to infer missing ties. Since these assumptions are formulated at a dyadic level, they do not provide means to express dependencies among ties and therefore include less plausible network scenarios. I…
Relational Resources of Individuals Living in Couple
Potential and Effective Support Networks of Young Italian Adults
Some days are better than others
Potential and Effective Support Networks of Young Italian Adults
Applying event history analysis to explain the diffusion of innovations in archaeological networks
The simple dyadic structure of a network is the basis for studying a wide variety of entities and their relationships, as well as the outcomes of processes such as the diffusion of innovations. Here, we apply models from event history analysis and cultural evolutionary theory to investigate whether and by what means network ties facilitated the transmission of certain cultural traits in past complex societies. To illustrate the application of the…
Random effects in dynamic network actor models
Dynamic Network Actor Models (DyNAMs) assume that an observed sequence of relational events is the outcome of an actor-oriented decision process consisting of two decision levels. The first level represents the time until an actor initiates the next relational event, modeled by an exponential distribution with an actor-specific activity rate. The second level describes the choice of the receiver of the event, modeled by a conditional multinomial …
Reconstructing Archaeological Networks with Structural Holes
Model-based reconstruction is an approach to infer network structures where they cannot be observed. For archaeological networks, several models based on assumptions concerning distance among sites, site size, or costs and benefits have been proposed to infer missing ties. Since these assumptions are formulated at a dyadic level, they do not provide means to express dependencies among ties and therefore include less plausible network scenarios. I…
A Framework for Reconstructing Archaeological Networks Using Exponential Random Graph Models
Reconstructing ties between archaeological contexts may contribute to explain and describe a variety of past social phenomena. Several models have been formulated to infer the structure of such archaeological networks. The applicability of these models in diverse archaeological contexts is limited by the restricted set of assumptions that fully determine the mathematical formulation of the models and are often articulated on a dyadic basis. Here,…
Relational Resources of Individuals Living in Couple
Potential and Effective Support Networks of Young Italian Adults
Reconstructing Archaeological Networks with Structural Holes
Model-based reconstruction is an approach to infer network structures where they cannot be observed. For archaeological networks, several models based on assumptions concerning distance among sites, site size, or costs and benefits have been proposed to infer missing ties. Since these assumptions are formulated at a dyadic level, they do not provide means to express dependencies among ties and therefore include less plausible network scenarios. I…
Relational Resources of Individuals Living in Couple
Social relations and life satisfaction
Social capital is defined as the individual’s pool of social resources found in his/her personal network. A recent study on Italians living as couples has shown that friendship relationships, beyond those within an individual’s family, are an important source of support. Here, we used data from Aspects of Daily Life, the Italian National Statistical Institute’s 2012 multipurpose survey, to analyze the relation between friendship ties and life sat…
Some days are better than others
A Framework for Reconstructing Archaeological Networks Using Exponential Random Graph Models
Reconstructing ties between archaeological contexts may contribute to explain and describe a variety of past social phenomena. Several models have been formulated to infer the structure of such archaeological networks. The applicability of these models in diverse archaeological contexts is limited by the restricted set of assumptions that fully determine the mathematical formulation of the models and are often articulated on a dyadic basis. Here,…
Applying event history analysis to explain the diffusion of innovations in archaeological networks
The simple dyadic structure of a network is the basis for studying a wide variety of entities and their relationships, as well as the outcomes of processes such as the diffusion of innovations. Here, we apply models from event history analysis and cultural evolutionary theory to investigate whether and by what means network ties facilitated the transmission of certain cultural traits in past complex societies. To illustrate the application of the…
Network mechanisms and network models
This chapter introduces the concept of network mechanisms and compares it to the notion of social mechanisms in Analytical Sociology. It discusses how statistical network models and empirically calibrated simulation models are applied to investigate network mechanisms. Different statistical network models are reviewed and compared, in particular conditional uniform graph (CUG) tests, quadratic assignment procedure (QAP) regressions, exponential r…
Random Graph Models
Random graph models are probability distributions that model the structure of a network. These models are commonly applied to test hypotheses concerning the characteristics that might have led to an observed network, and generate networks according to different processes of tie formation. In this chapter, I discuss the application of random graph models to the study of past networks. After a brief introduction to random graph models, I describe h…
Random effects in dynamic network actor models
Dynamic Network Actor Models (DyNAMs) assume that an observed sequence of relational events is the outcome of an actor-oriented decision process consisting of two decision levels. The first level represents the time until an actor initiates the next relational event, modeled by an exponential distribution with an actor-specific activity rate. The second level describes the choice of the receiver of the event, modeled by a conditional multinomial …
Modeling the duality of content niches and user interactions on online social media platforms
Individuals use online social media platforms for social interaction and to create, consume and share creative content. Within large platforms, individuals’ interaction with content and the congregation of individuals sharing similar interests might have a dual relationship, leading to the emergence of focused content niches of specific individuals and types of content. These niches are smaller-scale social settings that may facilitate and struct…
Computer Science (10 works) · Complex Network Analysis Techniques (5 works) · Mathematics (5 works) · Psychology (5 works) · Random graph (5 works) · Artificial Intelligence (4 works) · Exponential random graph models (4 works) · Graph (4 works) · Opinion Dynamics and Social Influence (4 works) · Sociology (4 works)