# Microscopic Evolution of Social Networks by Triad Position Profile

arXiv: https://arxiv.org/abs/1310.1525 · HTML: https://mlsanity.com/paper/1310.1525/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2013.

## Built on by (most cited first, 1 of 1)

- [What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs](https://mlsanity.com/paper/2410.12126/) (2024)

## Abstract (arXiv)

Disentangling the mechanisms underlying the social network evolution is one of social science's unsolved puzzles. Preferential attachment is a powerful mechanism explaining social network dynamics, yet not able to explain all scaling-laws in social networks. Recent advances in understanding social network dynamics demonstrate that several scaling-laws in social networks follow as natural consequences of triadic closure. Macroscopic comparisons between them are discussed empirically in many works. However the network evolution drives not only the emergence of macroscopic scaling but also the microscopic behaviors. Here we exploit two fundamental aspects of the network microscopic evolution: the individual influence evolution and the process of link formation. First we develop a novel framework for the microscopic evolution, where the mechanisms of preferential attachment and triadic closure are well balanced. Then on four real-world datasets we apply our approach for two microscopic problems: node's prominence prediction and link prediction, where our method yields significant predictive improvement over baseline solutions. Finally to be rigorous and comprehensive, we further observe that our framework has a stronger generalization capacity across different kinds of social networks for two microscopic prediction problems. We unveil the significant factors with a greater degree of precision than has heretofore been possible, and shed new light on networks evolution.