  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)
