This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL). We focus on the following question: How to construct contrastive views for hypergraphs via augmentations? We provide the solutions in two folds. First, guided by domain knowledge, we fabricate two schemes to augment hyperedges with higher-order relations encoded, and adopt three vertex augmentation strategies from graph-structured data. Second, in search of more effective views in a data-driven manner, we for the first time propose a hypergraph generative model to generate augmented views, and then an end-to-end differentiable pipeline to jointly learn hypergraph augmentations and model parameters. Our technical innovations are reflected in designing both fabricated and generative augmentations of hypergraphs. The experimental findings include: (i) Among fabricated augmentations in HyperGCL, augmenting hyperedges provides the most numerical gains, implying that higher-order information in structures is usually more downstream-relevant; (ii) Generative augmentations do better in preserving higher-order information to further benefit generalizability; (iii) HyperGCL also boosts robustness and fairness in hypergraph representation learning.

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Licence for the codes We use the code for MS-TCN [13], ASRF [24], LAS [9], all of which are under MITLicense according to https://opensource.org/licenses/MIT. For the Jigsaws [18] dataset, we follow the data use agreeement according to https://cs.jhu. Action classification: Action classification is the task of identifying a single action, as opposed to a sequence of actions. Several methods use 2DCNNs to extract frame-wise features from an input video, which are then combined to predict a coarse action taking place in the video [56, 39, 59]. There also exist several works that perform action classification from kinematic data [2, 12]. Action segmentation: Action segmentation is the problem of segmenting an input stream of data, labeling each frame according to the action that is being carried out. Earlier methods for action segmentation employed hidden Markov models [33, 22]. More recently, convolutional neural networks [58, 26] and recurrent neural networks [50] have been applied to this problem Inspired by the success of temporal convolutional networks (TCNs) in speech synthesis, [37] adapted these models to action segmentation. MS-TCN [13], which uses a multi-stage TCN architecture, has become one of the most widely used architecture for action segmentation. Although these methods achieve high frame-wise accuracy, they still produce a significant number of over-segmentation errors. In order to address this, several boundary-aware methods have been developed which perform temporal smoothing of the frame-wise predictions [57, 24]. These methods use ground-truth boundary information to train a binary classification network to perform boundary detection. The boundary estimates are then used to aggregate the frame-wise predictions either in a soft manner (boundary-aware pooling) or by setting a hard threshold. However, for elemental actions with a short duration, such as the functional primitives in the StrokeRehab dataset, the duration of each action is very short. As a result, the boundaries between actions can be hard to detect or even hard to define (see Figure 4). Sequence-to-sequence models: Our proposed method is based on sequence-to-sequence (seq2seq) models. These models allow us to learn a mapping of a variable-length input sequence to a variablelength output sequence [53].

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