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Sequence classification has a wide range of real-world applications indifferent domains, such as genome classification in health and anomalydetection in business. However, the lack of explicit features in sequence datamakes it difficult for machine learning models. While Neural Network (NN)models address this with learning features automatically, they are limited tocapturing adjacent structural connections and ignore global, higher-orderinformation between the sequences. To address these challenges in the sequenceclassification problems, we propose a novel Hypergraph Attention Network model,namely Seq-HyGAN. To capture the complex structural similarity between sequencedata, we first create a hypergraph where the sequences are depicted ashyperedges and subsequences extracted from sequences are depicted as nodes.Additionally, we introduce an attention-based Hypergraph Neural Network modelthat utilizes a two-level attention mechanism. This model generates a sequencerepresentation as a hyperedge while simultaneously learning the crucialsubsequences for each sequence. We conduct extensive experiments on four datasets to assess and compare our model with several state-of-the-art methods.Experimental results demonstrate that our proposed Seq-HyGAN model caneffectively classify sequence data and significantly outperform the baselines.We also conduct case studies to investigate the contribution of each module inSeq-HyGAN.

Seq-HyGAN: Sequence Classification via Hypergraph Attention Network