Long ties: Formation, social contagion, and economic outcomes

Network structure can affect when, where, and how widely new ideas, products, and behaviors are adopted. Classic work in the social sciences has emphasized that "long ties" provide access to novel and advantageous information. In our empirical work, we show how particular life events (migration, education) are associated with forming long ties and how having long ties is associated with beneficial economic outcomes. Counties in the United States with more long ties (and more strong long ties) have higher incomes, lower unemployment, and more economic mobility, even after adjusting for other measures of social connections.

These stylized facts are consistent with some models of contagion. In widely-used models of biological contagion, interventions that randomly rewire edges (generally making them "longer") accelerate spread. However, there are other models relevant to social contagion, such as those motivated by myopic best-response in games with strategic complements, in which individuals adopt if and only if the number of adopting neighbors exceeds a threshold. Recent work has argued that highly clustered, rather than random, networks facilitate spread of these "complex contagions". Here we show that minor modifications to this model, which make it more realistic, reverse this result: we allow very rare below-threshold adoption, i.e., rarely adoption occurs when there is only one adopting neighbor. In a version of "small world" networks, allowing adoptions below threshold to occur with order 1/√n probability — even only along some "short" cycle edges — is enough to ensure that random rewiring accelerates spread. Hypothetical interventions that randomly rewire existing edges or add random edges (versus adding "short", triad-closing edges) in hundreds of empirical social networks reduce time to spread.

In summary, we provide an empirical and theoretical view of the outsized role of long ties in the spread of valuable information and behaviors, even when those behaviors spread via threshold-based contagions.

This is joint work based on two papers: one on threshold-based contagions with Elchanan Mossel, M. Amin Rahimian, Subhabrata Sen, and one on formation of long ties and economic outcomes with Eaman Jahani, Samuel Fraiberger, and Michael Bailey.

Dean Eckles is an Associate Professor of Marketing at MIT Sloan. His substantive research examines people’s interactions with and through communication technologies, especially how these technologies mediate, amplify, and direct social influence. This work sometimes requires or benefits from new analytical methods, so Eckles also works on applied statistics, design of field experiments, and causal inference. Prior to joining MIT, he was a scientist at Facebook, where he worked on many product areas and analytical methods, including News Feed, messaging, advertising, tools for randomized experiments, and survey methods. Eckles previously worked in research at Nokia and Yahoo. Eckles received his BA in philosophy, a BS and MS in cognitive science,  an MS in statistics, and a PhD in communication, all from Stanford University.

Opinion Formation in Social Networks: Models and Computational Problems


This tutorial covers the main latest advances in recommendation systems, namely the recent focus in offline-online metrics alignment for Recommendation and the introduction of deep leaning techniques for user and item representation coupled with maximum inner-product search (MIPS) for fast retrieval. The tutorial assumes a good understanding of linear algebra and classical Machine Learning techniques and aims to bring insights to the audience on the real-world problems faced by recommendation systems practitioners. The tutorial will be divided into two parts, each containing a theoretical section and a practical session that illustrates the concepts with simple coding examples.

Flavian Vasile is part of the Criteo AI Lab where he works as Principal Scientist. His main focus being on the development of Deep Learning-based Recommendation Systems and on introducing aspects of Causal Inference to Recommendation. His current research interests include Deep Sequential Models for Recommendation and understanding Recommendation as a decision-making system with reward uncertainty. Among his recent research publications, the work on Causal Embeddings for Recommendation received the best paper award at RecSys ‘18 and he co-organized the ‘18–‘20 REVEAL Workshop series in conjunction with ACM RecSys.