# Predicting Information Pathways Across Online Communities

arXiv: https://arxiv.org/abs/2306.02259 · HTML: https://mlsanity.com/paper/2306.02259/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2023.

## Built on by (most cited first, 15 of 15)

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- [arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering](https://mlsanity.com/paper/2306.04962/) (2023)
- [Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories](https://mlsanity.com/paper/2406.17963/) (2024)
- [MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways](https://mlsanity.com/paper/2310.02529/) (2023)
- [SciEvo: A 2 Million, 30-Year Cross-disciplinary Dataset for Temporal Scientometric Analysis](https://mlsanity.com/paper/2410.09510/) (2024)
- [ScreenLLM: Stateful Screen Schema for Efficient Action Understanding and Prediction](https://mlsanity.com/paper/2503.20978/) (2025)
- [CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs](https://mlsanity.com/paper/2504.14905/) (2025)
- [Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)](https://mlsanity.com/paper/2409.16590/) (2024)
- [Large language models for spreading dynamics in complex systems](https://mlsanity.com/paper/2602.08085/) (2026)

## Abstract (arXiv)

The problem of community-level information pathway prediction (CLIPP) aims at predicting the transmission trajectory of content across online communities. A successful solution to CLIPP holds significance as it facilitates the distribution of valuable information to a larger audience and prevents the proliferation of misinformation. Notably, solving CLIPP is non-trivial as inter-community relationships and influence are unknown, information spread is multi-modal, and new content and new communities appear over time. In this work, we address CLIPP by collecting large-scale, multi-modal datasets to examine the diffusion of online YouTube videos on Reddit. We analyze these datasets to construct community influence graphs (CIGs) and develop a novel dynamic graph framework, INPAC (Information Pathway Across Online Communities), which incorporates CIGs to capture the temporal variability and multi-modal nature of video propagation across communities. Experimental results in both warm-start and cold-start scenarios show that INPAC outperforms seven baselines in CLIPP.