CAT-Walk：通过集合游走的归纳式超图学习
CAT-Walk: Inductive Hypergraph Learning via Set Walks
AI总结：
针对现有超图学习方法多适配静态场景或特定任务的问题，提出归纳式时序超图学习方法CAT-Walk，结合SetWalk高阶时序游走、SetMixer池化等技术，在10个基准数据集的超边预测等任务上表现优异。
摘要：
时序超图为复杂系统中依赖时间的高阶交互建模提供了强大范式。超图表示学习对于提取高阶交互模式至关重要，这类模式在社交网络分析、神经科学、金融等领域的现实问题中具有关键意义。然而现有方法通常仅针对特定任务或静态超图设计。我们提出CAT-Walk，这是一种归纳式方法，可学习支配时序超图背后时序与结构过程的潜在动态规律。CAT-Walk引入了一种在超图上的时序高阶游走方法SetWalk，用于提取高阶因果模式。CAT-Walk采用一种新颖的自适应且置换不变的池化策略SetMixer，同时搭配基于集合的匿名化过程以隐藏超边的身份信息。最后，我们提出了一个简单却有效的神经网络模型来编码超边。我们在10个超图基准数据集上的评估表明，CAT-Walk在归纳式和直推式设置下的时序超边预测基准任务中均取得了优异性能，其在节点分类任务上的表现也与当前最优方法具有竞争力。（代码仓库：https://github.com/ubc-systopia/CATWalk）
英文摘要：
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. (https://github.com/ubc-systopia/CATWalk)
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
