Temporal hypergraphs provide a powerful paradigm for modeling time-dependent,
higher-order interactions in complex systems. Representation learning for
hypergraphs is essential for extracting patterns of the higher-order
interactions that are critically important in real-world problems in social
network analysis, neuroscience, finance, etc. However, existing methods are
typically designed only for specific tasks or static hypergraphs. We present
CAT-Walk, an inductive method that learns the underlying dynamic laws that
govern the temporal and structural processes underlying a temporal hypergraph.
CAT-Walk introduces a temporal, higher-order walk on hypergraphs, SetWalk, that
extracts higher-order causal patterns. CAT-Walk uses a novel adaptive and
permutation invariant pooling strategy, SetMixer, along with a set-based
anonymization process that hides the identity of hyperedges. Finally, we
present a simple yet effective neural network model to encode hyperedges. Our
evaluation on 10 hypergraph benchmark datasets shows that CAT-Walk attains
outstanding performance on temporal hyperedge prediction benchmarks in both
inductive and transductive settings. It also shows competitive performance with
state-of-the-art methods for node classification.

CAT-Walk 是一种基于时态高阶图的归纳式研究方法，使用 SetWalk 提取高阶因果模式，使用 SetMixer 池化策略和集合匿名过程隐藏超边的身份，并使用神经网络模型对超边进行编码。在 10 个超图基准数据集上的评估表明， CAT-Walk 在归纳式和转导式设置下的时间超边预测基准方面表现出卓越的性能，并显示出与节点分类的现有最佳方法相竞争的性能。