# Investigating and Modeling the Dynamics of Long Ties

arXiv: https://arxiv.org/abs/2109.10523 · HTML: https://mlsanity.com/paper/2109.10523/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 61 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Simplicial Closure and higher-order link prediction](https://mlsanity.com/paper/1802.06916/) (2018)
- [Scale-free networks are rare](https://mlsanity.com/paper/1801.03400/) (2018)
- [Modern temporal network theory: A colloquium](https://mlsanity.com/paper/1508.01303/) (2015)
- [Cumulative effects of triadic closure and homophily in social networks](https://mlsanity.com/paper/1809.06057/) (2018)
- [Choosing to Grow a Graph: Modeling Network Formation as Discrete Choice](https://mlsanity.com/paper/1811.05008/) (2018)
- [Fundamental structures of dynamic social networks](https://mlsanity.com/paper/1506.04704/) (2015)
- [Attention on Weak Ties in Social and Communication Networks](https://mlsanity.com/paper/1505.02399/) (2015)
- [Long ties accelerate noisy threshold-based contagions](https://mlsanity.com/paper/1810.03579/) (2018)

## Built on by (most cited first, 2 of 2)

- [Network Formation and Dynamics Among Multi-LLMs](https://mlsanity.com/paper/2402.10659/) (2024)
- [A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing](https://mlsanity.com/paper/2308.09790/) (2023)

## Abstract (arXiv)

Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks. However, some existing network theories and prediction models indicate that long ties might dissolve quickly or eventually become redundant, thus putting into question the long-term value of long ties. Our empirical analysis of real-world dynamic networks shows that contrary to such reasoning, long ties are more likely to persist than other social ties, and that many of them constantly function as social bridges without being embedded in local networks. Using a novel cost-benefit analysis model combined with machine learning, we show that long ties are highly beneficial, which instinctively motivates people to expend extra effort to maintain them. This partly explains why long ties are more persistent than what has been suggested by many existing theories and models. Overall, our study suggests the need for social interventions that can promote the formation of long ties, such as mixing people with diverse backgrounds.