Role Similarity Metric Based on Spanning Rooted Forest
Qi Bao, Zhongzhi Zhang, Haibin Kan

TL;DR
This paper introduces ForestSim, a novel role similarity metric for network analysis that enables efficient top-k queries on large networks, with proven theoretical properties and scalable algorithms.
Contribution
The paper proposes ForestSim, a new role similarity measure with an efficient top-k search algorithm and an approximate precomputation method, improving scalability and performance.
Findings
ForestSim is admissible and satisfies axiomatic properties.
The top-k search runs in O(k) time after precomputation.
Experimental results show ForestSim's efficiency on million-scale networks.
Abstract
As a fundamental issue in network analysis, structural node similarity has received much attention in academia and is adopted in a wide range of applications. Among these proposed structural node similarity measures, role similarity stands out because of satisfying several axiomatic properties including automorphism conformation. Existing role similarity metrics cannot handle top-k queries on large real-world networks due to the high time and space cost. In this paper, we propose a new role similarity metric, namely \textsf{ForestSim}. We prove that \textsf{ForestSim} is an admissible role similarity metric and devise the corresponding top-k similarity search algorithm, namely \textsf{ForestSimSearch}, which is able to process a top-k query in time once the precomputation is finished. Moreover, we speed up the precomputation by using a fast approximate algorithm to compute the…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Bioinformatics and Genomic Networks · Complex Network Analysis Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
