Papers › Relation order histograms as a network embedding tool

Relation order histograms as a network embedding tool

9 Jun 2021International Conference on Computational Science 2021 6archive 2025-07-28

Radosław Łazaz, Michał Idzik

In this work, we introduce a novel graph embedding technique called NERO (Network Embedding based on Relation Order histograms). Its performance is assessed using a number of well-known classification problems and a newly introduced benchmark dealing with detailed laminae venation networks. The proposed algorithm achieves results surpassing those attained by other kernel-type methods and comparable with many state-of-the-art GNNs while requiring no GPU support and being able to handle relatively large input data. It is also demonstrated that the produced representation can be easily paired with existing model interpretation techniques to provide an overview of the individual edge and vertex influence on the investigated process.

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Graph ClassificationGraph EmbeddingNetwork Embedding

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Graph Classification D&D NERO Accuracy 80.45% #13 of 53 Archive leaderboard report
Graph Classification MUTAG NERO Accuracy 88.68% #35 of 74 Archive leaderboard report
Graph Classification NCI1 NERO Accuracy 81.63% #34 of 69 Archive leaderboard report
Graph Classification PROTEINS NERO Accuracy 77.89% #24 of 103 Archive leaderboard report

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