Khan, Nasr Ullah;
Dickens, Luke;
(2023)
Dynamic Hyper-graph Regularized Non-negative Matrix Factorization.
In:
Proceedings of Learning on Graphs 2023 (LoG 2023).
Proceedings of Machine Learning Research (PMLR)
Preview |
Text
khan-2023-dynamic.pdf - Other Download (363kB) | Preview |
Abstract
Recent advances in dynamic uni-graph methods make predictions about links between nodes at the current time, based on previous observations. The most powerful of these approaches are based on regularizing over previous graph laplacians with a greater emphasis placed on more recent observations as opposed to older observations. Concurrently, researchers have identified domains in which hyper-graph formulations of data provide more detailed information about relationships between entities when those relationships can be multi-factored. This work presents a natural synthesis of these two strands of work, extending regularization based on dynamic observations to hypergraphs. We present a modelling framework for dynamic hypergraphs, an algorithm for 1-step ahead prediction of the dynamic adjacency matrix, and experiments demonstrating the improved accuracy of this algorithm compared to dynamic uni-graph approaches.
| Type: | Proceedings paper |
|---|---|
| Title: | Dynamic Hyper-graph Regularized Non-negative Matrix Factorization |
| Event: | LoG 2023: Learning on Graphs Conference 2023 |
| Dates: | 27 Nov 2023 - 30 Nov 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://openreview.net/forum?id=SFFs9AtGSi |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Dynamic link prediction, dynamic graphs, hyper-graphs, graph regularization, non-negative matrix factorization, graph machine learning, time series analysis |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10185930 |
Archive Staff Only
 |
View Item |
