NED: An Inter-Graph Node Metric Based On Edit Distance

TL;DR

This paper introduces NED, a novel polynomial-time metric for measuring inter-graph node similarity based on local neighborhood structures represented as unordered trees, improving efficiency and interpretability in graph analytics.

Contribution

The paper proposes a new edit distance-based metric, TED*, for comparing neighborhood trees, enabling efficient and accurate inter-graph node similarity measurement without relying on labels.

Findings

NED outperforms existing methods in graph de-anonymization tasks.

TED* is a polynomially computable approximation of tree edit distance.

NED demonstrates efficiency and effectiveness on real-world graph data.

Abstract

Node similarity is a fundamental problem in graph analytics. However, node similarity between nodes in different graphs (inter-graph nodes) has not received a lot of attention yet. The inter-graph node similarity is important in learning a new graph based on the knowledge of an existing graph (transfer learning on graphs) and has applications in biological, communication, and social networks. In this paper, we propose a novel distance function for measuring inter-graph node similarity with edit distance, called NED. In NED, two nodes are compared according to their local neighborhood structures which are represented as unordered k-adjacent trees, without relying on labels or other assumptions. Since the computation problem of tree edit distance on unordered trees is NP-Complete, we propose a modified tree edit distance, called TED*, for comparing neighborhood trees. TED* is a metric distance, as the original tree edit distance, but more importantly, TED* is polynomially computable. As a metric distance, NED admits efficient indexing, provides interpretable results, and shows to perform better than existing approaches on a number of data analysis tasks, including graph de-anonymization. Finally, the efficiency and effectiveness of NED are empirically demonstrated using real-world graphs.