# Efficient Cavity Searching for Gene Network of Influenza A Virus

arXiv: https://arxiv.org/abs/2211.02935 · HTML: https://mlsanity.com/paper/2211.02935/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [Be More with Less: Hypergraph Attention Networks for Inductive Text Classification](https://mlsanity.com/paper/2011.00387/) (2020)
- [Hyper-SAGNN: a self-attention based graph neural network for hypergraphs](https://mlsanity.com/paper/1911.02613/) (2019)
- [What are higher-order networks?](https://mlsanity.com/paper/2104.11329/) (2021)
- [DIANet: Dense-and-Implicit Attention Network](https://mlsanity.com/paper/1905.10671/) (2019)
- [The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Network](https://mlsanity.com/paper/2207.07858/) (2022)
- [Totally Homogeneous Networks](https://mlsanity.com/paper/1903.11289/) (2019)
- [Computing Cliques and Cavities in Networks](https://mlsanity.com/paper/2101.00536/) (2021)

## Abstract (arXiv)

High order structures (cavities and cliques) of the gene network of influenza A virus reveal tight associations among viruses during evolution and are key signals that indicate viral cross-species infection and cause pandemics. As indicators for sensing the dynamic changes of viral genes, these higher order structures have been the focus of attention in the field of virology. However, the size of the viral gene network is usually huge, and searching these structures in the networks introduces unacceptable delay. To mitigate this issue, in this paper, we propose a simple-yet-effective model named HyperSearch based on deep learning to search cavities in a computable complex network for influenza virus genetics. Extensive experiments conducted on a public influenza virus dataset demonstrate the effectiveness of HyperSearch over other advanced deep-learning methods without any elaborated model crafting. Moreover, HyperSearch can finish the search works in minutes while 0-1 programming takes days. Since the proposed method is simple and easy to be transferred to other complex networks, HyperSearch has the potential to facilitate the monitoring of dynamic changes in viral genes and help humans keep up with the pace of virus mutations.