# AI Can Enhance Creativity in Social Networks

arXiv: https://arxiv.org/abs/2410.15264 · HTML: https://mlsanity.com/paper/2410.15264/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 87 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Attribute-conditioned Layout GAN for Automatic Graphic Design](https://mlsanity.com/paper/2009.05284/) (2020)
- [Empirical evidence of Large Language Model's influence on human spoken communication](https://mlsanity.com/paper/2409.01754/) (2024)
- [The Diversity-Innovation Paradox in Science](https://mlsanity.com/paper/1909.02063/) (2019)
- [Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation](https://mlsanity.com/paper/1607.04373/) (2016)
- [Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat](https://mlsanity.com/paper/1906.00355/) (2019)
- [SimulBench: Evaluating Language Models with Creative Simulation Tasks](https://mlsanity.com/paper/2409.07641/) (2024)
- [Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest](https://mlsanity.com/paper/1511.04003/) (2015)
- [Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems](https://mlsanity.com/paper/2409.14682/) (2024)
- [Don't follow the leader: Independent thinkers create scientific innovation](https://mlsanity.com/paper/2301.02396/) (2023)
- [Exploring the Role of Randomization on Belief Rigidity in Online Social Networks](https://mlsanity.com/paper/2407.01820/) (2024)
- [Buildup of Speaking Skills in an Online Learning Community: A Network-Analytic Exploration](https://mlsanity.com/paper/1707.01886/) (2017)
- [Deep Intention-Aware Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2211.08650/) (2022)

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

- [MuseScorer: Idea Originality Scoring At Scale](https://mlsanity.com/paper/2505.16232/) (2025)
- [Ex Ante Evaluation of AI-Induced Idea Diversity Collapse](https://mlsanity.com/paper/2605.06540/) (2026)

## Abstract (arXiv)

Can peer recommendation engines elevate people's creative performances in self-organizing social networks? Answering this question requires resolving challenges in data collection (e.g., tracing inspiration links and psycho-social attributes of nodes) and intervention design (e.g., balancing idea stimulation and redundancy in evolving information environments). We trained a model that predicts people's ideation performances using semantic and network-structural features in an online platform. Using this model, we built SocialMuse, which maximizes people's predicted performances to generate peer recommendations for them. We found treatment networks leveraging SocialMuse outperforming AI-agnostic control networks in several creativity measures. The treatment networks were more decentralized than the control, as SocialMuse increasingly emphasized network-structural features at large network sizes. This decentralization spreads people's inspiration sources, helping inspired ideas stand out better. Our study provides actionable insights into building intelligent systems for elevating creativity.