# Virality Prediction and Community Structure in Social Networks

arXiv: https://arxiv.org/abs/1306.0158 · HTML: https://mlsanity.com/paper/1306.0158/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2013.

## Built on by (most cited first, 12 of 12)

- [CCGL: Contrastive Cascade Graph Learning](https://mlsanity.com/paper/2107.12576/) (2021)
- [CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks](https://mlsanity.com/paper/2308.04961/) (2023)
- [From Leakage to Fidelity: Reliable Benchmarking for Temporal Cascade Prediction](https://mlsanity.com/paper/2510.25348/) (2025)
- [ViralBERT: A User Focused BERT-Based Approach to Virality Prediction](https://mlsanity.com/paper/2206.10298/) (2022)
- [Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction](https://mlsanity.com/paper/2308.09976/) (2023)
- [AutoCas: Autoregressive Cascade Predictor in Social Networks via Large Language Models](https://mlsanity.com/paper/2502.18040/) (2025)
- [An adaptation of InfoMap to absorbing random walks using absorption-scaled graphs](https://mlsanity.com/paper/2112.10953/) (2021)
- [DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property](https://mlsanity.com/paper/2310.01696/) (2023)
- [On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction](https://mlsanity.com/paper/2409.16623/) (2024)
- [Predicting Social Media Engagement from Emotional and Temporal Features](https://mlsanity.com/paper/2508.21650/) (2025)
- [Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit: A Time-Window Analysis](https://mlsanity.com/paper/2510.05761/) (2025)
- [Network World Models as Environments for Algorithm Design on Complex Systems](https://mlsanity.com/paper/2610.01048/) (2026)

## Abstract (arXiv)

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed behave like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection may lead to significant advances in computational social science, social media analytics, and marketing applications.