k-hop Graph Neural Networks
This work tackles the problem of limited expressiveness in GNNs for graph learning, which is incremental as it builds on known bottlenecks in the field.
The paper addresses the limitation of standard Graph Neural Networks (GNNs) in identifying fundamental graph properties like connectivity and triangle freeness by proposing k-hop GNNs, which aggregate information from k-hop neighborhoods, and shows that this architecture achieves performance better or comparable to standard GNNs and state-of-the-art algorithms on node and graph classification tasks.
Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisfeiler-Leman test of graph isomorphism in terms of distinguishing non-isomorphic graphs. However, it was recently shown that this test cannot identify fundamental graph properties such as connectivity and triangle freeness. We show that GNNs also suffer from the same limitation. To address this limitation, we propose a more expressive architecture, k-hop GNNs, which updates a node's representation by aggregating information not only from its direct neighbors, but from its k-hop neighborhood. We show that the proposed architecture can identify fundamental graph properties. We evaluate the proposed architecture on standard node classification and graph classification datasets. Our experimental evaluation confirms our theoretical findings since the proposed model achieves performance better or comparable to standard GNNs and to state-of-the-art algorithms.