SNAC: An Unbiased Metric Evaluating Topology Recognize Ability of Network Alignment

TL;DR

This paper introduces SNAC, an unbiased metric for network alignment that accounts for indistinguishable nodes, providing a more accurate evaluation of node mapping performance in non-isomorphic graphs.

Contribution

The paper proposes a novel metric, SNAC, which addresses the bias in existing accuracy measures by considering indistinguishable nodes in network alignment evaluation.

Findings

SNAC accurately reflects deviation from benchmark mappings.

SNAC remains stable with increasing indistinguishable nodes.

SNAC outperforms traditional accuracy metrics in various datasets.

Abstract

Network alignment is a problem of finding the node mapping between similar networks. It links the data from separate sources and is widely studied in bioinformation and social network fields. The critical difference between network alignment and exact graph matching is that the network alignment considers node mapping in non-isomorphic graphs with error tolerance. Researchers usually utilize AC (accuracy) to measure the performance of network alignments which comparing each output element with the benchmark directly. However, this metric neglects that some nodes are naturally indistinguishable even in single graphs (e.g., nodes have the same neighbors) and no need to distinguish across graphs. Such neglect leads to the underestimation of models. We propose an unbiased metric for network alignment that takes indistinguishable nodes into consideration to address this problem. Our detailed experiments with different scales on both synthetic and real-world datasets demonstrate that the proposed metric correctly reflects the deviation of result mapping from benchmark mapping as standard metric AC does. Comparing with the AC, the proposed metric effectively blocks the effect of indistinguishable nodes and retains stability under increasing indistinguishable nodes.