Research map / Probabilistic and scientific modeling / Molecules and proteins
Heterogeneous Diffusion Structure Inference for Network Cascade
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes a method for inferring heterogeneous diffusion structure from network cascade observations; only the title and a truncated abstract were available.
Problem
Inferring the propagation structure of network cascades, where diffusion behavior varies across heterogeneous parts of the network. Abstract is truncated, so details are limited.
Approach
Not stated in the available abstract text.
Closest papers by meaning
- Enhancing Multi-Scale Diffusion Prediction via Sequential Hypergraphs and Adversarial Learning (AAAI 2024 · similarity 0.87)
- A Generalized Neural Diffusion Framework on Graphs (AAAI 2024 · similarity 0.86)
- Learning Diffusions under Uncertainty (AAAI 2024 · similarity 0.86)
- Probing the Latent Hierarchical Structure of Data via Diffusion Models (ICLR 2025 · similarity 0.86)
- MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction (AAAI 2025 · similarity 0.85)
- A Thorough Comparison Between Independent Cascade and Susceptible-Infected-Recovered Models (AAAI 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
