Does GraphMETRO's mixture-of-experts design improve cross-domain robustness on node classification tasks under

Abstract

Abstract: Multi-view graph refining-based clustering (MGRC) methods aim to facilitate the clustering of data via Graph Neural Networks (GNNs) by learning optimal graphs that reflect the underlying topology of the data. However, current MGRC approaches are limited by their disjoint two-stage process, where the graph structure is learned in the first stage before the GNN messages are propagated in the subsequent stage. Additionally, current approaches neglect the importance of cross-view structural consistency and semantic-level information and only consider intra-view embeddings. To address these issues,

Research Question

Does GraphMETRO's mixture-of-experts design improve cross-domain robustness on node classification tasks under distribution shifts compared to standard GNN architectures with identical FLOPs?

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