An Invertible Graph Diffusion Neural Network for Source Localization

Author:

Wang Junxiang1,Jiang Junji2,Zhao Liang1

Affiliation:

1. Computer Science, Emory University, USA

2. Tianjin University, China

Funder

NVIDIA GPU Grant

Design Knowledge Company

National Science Foundation (NSF) Grant

Jeffress Memorial Trust Award

Amazon Research Award

Publisher

ACM

Reference66 articles.

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3. Modeling and Predicting Popularity Dynamics of Microblogs using Self-Excited Hawkes Processes

4. Jens Behrmann , Will Grathwohl , Ricky  TQ Chen , David Duvenaud , and Jörn-Henrik Jacobsen . 2019 . Invertible residual networks . In International Conference on Machine Learning. PMLR, 573–582 . Jens Behrmann, Will Grathwohl, Ricky TQ Chen, David Duvenaud, and Jörn-Henrik Jacobsen. 2019. Invertible residual networks. In International Conference on Machine Learning. PMLR, 573–582.

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1. PGSL: A probabilistic graph diffusion model for source localization;Expert Systems with Applications;2024-03

2. Reconstructing Graph Diffusion History from a Single Snapshot;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04

3. Identifying Multiple Propagation Sources With Motif-Based Graph Convolutional Networks for Social Networks;IEEE Access;2023

4. Two-Stage Denoising Diffusion Model for Source Localization in Graph Inverse Problems;Machine Learning and Knowledge Discovery in Databases: Research Track;2023

5. Improving Source Localization by Perturbing Graph Diffusion;2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA);2022-10-13

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