Sinan Aral
Biographic Data
| ID | 252216 |
|---|---|
| NAME | Sinan Aral |
| GIVEN NAMES | Sinan |
| FAMILY NAME | Aral |
| SIGNATURE | ARAL S |
| AFFILIATIONS | Massachusetts Institute of Technology |
| ORCID | 0000-0002-2762-058X |
| VERIFIED | Yes |
| TOTAL WORKS | 14 |
| TOTAL CITATIONS | 119 |
| WORKS AS AUTHOR | 14 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2007 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 5 |
A causal test of the strength of weak ties
The authors analyzed data from multiple large-scale randomized experiments on LinkedIn’s People You May Know algorithm, which recommends new connections to LinkedIn members, to test the extent to which weak ties increased job mobility in the world’s largest professional social network. The experiments randomly varied the prevalence of weak ties in the networks of over 20 million people over a 5-year period, during which 2 billion new ties and 600…
Global survey on Covid-19 beliefs, behaviours and norms
Identity effects in social media
Social influence maximization under empirical influence models
The Future of Weak Ties
Tie Strength, Embeddedness, and Social Influence
We leverage the newly emerging business analytical capability to rapidly deploy and iterate large-scale, microlevel, in vivo randomized experiments to understand how social influence in networks impacts consumer demand. Understanding peer influence is critical to estimating product demand and diffusion, creating effective viral marketing, and designing “network interventions” to promote positive social change. But several statistical challenges m…
Social Influence Bias
Follow the Leader? The Internet has increased the likelihood that our decisions will be influenced by those being made around us. On the one hand, group decision-making can lead to better decisions, but it can also lead to “herding effects” that have resulted in financial disasters. Muchnik et al. (p. 647 ) examined the effect of collective information via a randomized experiment, which involved collaboration with a social news aggregation Web si…
Engineering social contagions
We use data on a real, large-scale social network of 27 million individuals interacting daily, together with the day-by-day adoption of a new mobile service product, to inform, build, and analyze data-driven simulations of the effectiveness of seeding (network targeting) strategies under different social conditions. Three main results emerge from our simulations. First, failure to consider homophily creates significant overestimation of the effec…
Identifying Influential and Susceptible Members of Social Networks
Who Influences Who? A goal in social science is how to assess people's influence over one. Aral and Walker (p. 337 , published online 21 June) describe a generalized method for identifying influential and susceptible members of social networks based on large-scale in vivo randomized experimentation. The method was used to estimate peer effects in consumer demand for a commercial Facebook application in a representative sample of 12 million Facebo…
Engineering Social Contagions
The Diversity-Bandwidth Trade-off
The authors propose that a trade-off between network diversity and communications bandwidth regulates access to novel information because a more diverse network structure increases novelty at a cost of reducing information flow. Received novelty then depends on whether (a) the information overlap is small enough, (b) alters' topical knowledge is shallow enough, and (c) alters' knowledge stocks refresh slowly enough to justify bridging structural …
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic…
Computational Social Science
A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.
Productivity Effects of Information Diffusion in Networks
The Diversity-Bandwidth Trade-off
The authors propose that a trade-off between network diversity and communications bandwidth regulates access to novel information because a more diverse network structure increases novelty at a cost of reducing information flow. Received novelty then depends on whether (a) the information overlap is small enough, (b) alters' topical knowledge is shallow enough, and (c) alters' knowledge stocks refresh slowly enough to justify bridging structural …
Engineering social contagions
We use data on a real, large-scale social network of 27 million individuals interacting daily, together with the day-by-day adoption of a new mobile service product, to inform, build, and analyze data-driven simulations of the effectiveness of seeding (network targeting) strategies under different social conditions. Three main results emerge from our simulations. First, failure to consider homophily creates significant overestimation of the effec…
The Future of Weak Ties
Social influence maximization under empirical influence models
Identity effects in social media
Global survey on Covid-19 beliefs, behaviours and norms
Productivity Effects of Information Diffusion in Networks
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic…
Computational Social Science
A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.
Engineering Social Contagions
The Diversity-Bandwidth Trade-off
The authors propose that a trade-off between network diversity and communications bandwidth regulates access to novel information because a more diverse network structure increases novelty at a cost of reducing information flow. Received novelty then depends on whether (a) the information overlap is small enough, (b) alters' topical knowledge is shallow enough, and (c) alters' knowledge stocks refresh slowly enough to justify bridging structural …
Identifying Influential and Susceptible Members of Social Networks
Who Influences Who? A goal in social science is how to assess people's influence over one. Aral and Walker (p. 337 , published online 21 June) describe a generalized method for identifying influential and susceptible members of social networks based on large-scale in vivo randomized experimentation. The method was used to estimate peer effects in consumer demand for a commercial Facebook application in a representative sample of 12 million Facebo…
Social Influence Bias
Follow the Leader? The Internet has increased the likelihood that our decisions will be influenced by those being made around us. On the one hand, group decision-making can lead to better decisions, but it can also lead to “herding effects” that have resulted in financial disasters. Muchnik et al. (p. 647 ) examined the effect of collective information via a randomized experiment, which involved collaboration with a social news aggregation Web si…
Engineering social contagions
We use data on a real, large-scale social network of 27 million individuals interacting daily, together with the day-by-day adoption of a new mobile service product, to inform, build, and analyze data-driven simulations of the effectiveness of seeding (network targeting) strategies under different social conditions. Three main results emerge from our simulations. First, failure to consider homophily creates significant overestimation of the effec…
Tie Strength, Embeddedness, and Social Influence
We leverage the newly emerging business analytical capability to rapidly deploy and iterate large-scale, microlevel, in vivo randomized experiments to understand how social influence in networks impacts consumer demand. Understanding peer influence is critical to estimating product demand and diffusion, creating effective viral marketing, and designing “network interventions” to promote positive social change. But several statistical challenges m…
The Future of Weak Ties
Social influence maximization under empirical influence models
A causal test of the strength of weak ties
The authors analyzed data from multiple large-scale randomized experiments on LinkedIn’s People You May Know algorithm, which recommends new connections to LinkedIn members, to test the extent to which weak ties increased job mobility in the world’s largest professional social network. The experiments randomly varied the prevalence of weak ties in the networks of over 20 million people over a 5-year period, during which 2 billion new ties and 600…
Global survey on Covid-19 beliefs, behaviours and norms
Identity effects in social media
Opinion Dynamics and Social Influence (11 works) · Complex Network Analysis Techniques (9 works) · Computer Science (9 works) · Psychology (9 works) · Business (8 works) · Social Psychology (8 works) · Social media (6 works) · Economics (4 works) · Social influence (4 works) · Social Media and Politics (4 works)