Metric Distribution to Vector: Constructing Data Representation via Broad-Scale Discrepancies

• URL: http://arxiv.org/abs/2210.00415v1
• Date: Sun, 2 Oct 2022 03:18:30 GMT
• Title: Metric Distribution to Vector: Constructing Data Representation via Broad-Scale Discrepancies
• Authors: Xue Liu, Dan Sun, Xiaobo Cao, Hao Ye, Wei Wei
• Abstract summary: We present a novel embedding strategy named $mathbfMetricDistribution2vec$ to extract distribution characteristics into the vectorial representation for each data. We demonstrate the application and effectiveness of our representation method in the supervised prediction tasks on extensive real-world structural graph datasets.
• Score: 15.40538348604094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph embedding provides a feasible methodology to conduct pattern classification for graph-structured data by mapping each data into the vectorial space. Various pioneering works are essentially coding method that concentrates on a vectorial representation about the inner properties of a graph in terms of the topological constitution, node attributions, link relations, etc. However, the classification for each targeted data is a qualitative issue based on understanding the overall discrepancies within the dataset scale. From the statistical point of view, these discrepancies manifest a metric distribution over the dataset scale if the distance metric is adopted to measure the pairwise similarity or dissimilarity. Therefore, we present a novel embedding strategy named $\mathbf{MetricDistribution2vec}$ to extract such distribution characteristics into the vectorial representation for each data. We demonstrate the application and effectiveness of our representation method in the supervised prediction tasks on extensive real-world structural graph datasets. The results have gained some unexpected increases compared with a surge of baselines on all the datasets, even if we take the lightweight models as classifiers. Moreover, the proposed methods also conducted experiments in Few-Shot classification scenarios, and the results still show attractive discrimination in rare training samples based inference.

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