2 research outputs found
Generalized Equivariance and Preferential Labeling for GNN Node Classification
Existing graph neural networks (GNNs) largely rely on node embeddings, which
represent a node as a vector by its identity, type, or content. However, graphs
with unattributed nodes widely exist in real-world applications (e.g.,
anonymized social networks). Previous GNNs either assign random labels to nodes
(which introduces artefacts to the GNN) or assign one embedding to all nodes
(which fails to explicitly distinguish one node from another). Further, when
these GNNs are applied to unattributed node classification problems, they have
an undesired equivariance property, which are fundamentally unable to address
the data with multiple possible outputs. In this paper, we analyze the
limitation of existing approaches to node classification problems. Inspired by
our analysis, we propose a generalized equivariance property and a Preferential
Labeling technique that satisfies the desired property asymptotically.
Experimental results show that we achieve high performance in several
unattributed node classification tasks