The Impact of Ego-Neighbor Separation and Graph Topology on Graph Neural Network Memorization

Recent studies on memorization in Graph Neural Networks (GNNs) indicate that nodes with high label inconsistency within their neighborhoods are more susceptible to being memorized. However, this perspective overlooks the interplay between diverse topological factors and the structural inductive biases that dictate how ego-node and neighborhood representations are integrated during the aggregation phase. In this work, we systematically dissect GNN memorization by categorizing GNNs into two paradigms: Ego-Neighbor Integration GNNs, which mix ego-node and neighborhood representations during aggregation (e.g., GCN), and Ego-Neighbor Separation GNNs, which utilize independent ego-weight layers to process ego-node representations without mixing them during aggregation (e.g., GraphSAGE). Our findings reveal that topological factors like node degree can suppress memorization in both paradigms; however, Ego-Neighbor Separation GNNs still retain their memorization ability more effectively than Integration GNNs. Finally, bridging these architectural insights with privacy concerns, we show that GNNs equipped with ego-weight layers exhibit substantially increased susceptibility to Membership Inference Attacks. Ultimately, our findings demonstrate that GNN memorization is not merely a topology-dependent phenomenon, but rather the result of a complex interplay between the topological environment and how ego-node and neighborhood representations are integrated.