Low-Rank Representations Towards Classification Problem of Complex Networks

• URL: http://arxiv.org/abs/2210.11561v1
• Date: Thu, 20 Oct 2022 19:56:18 GMT
• Title: Low-Rank Representations Towards Classification Problem of Complex Networks
• Authors: Murat \c{C}elik, Ali Baran Ta\c{s}demir, Lale \"Ozkahya
• Abstract summary: Complex networks representing social interactions, brain activities, molecular structures have been studied widely to be able to understand and predict their characteristics as graphs. Models and algorithms for these networks are used in real-life applications, such as search engines, and recommender systems. We study the performance of such low-rank representations of real-life networks on a network classification problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Complex networks representing social interactions, brain activities, molecular structures have been studied widely to be able to understand and predict their characteristics as graphs. Models and algorithms for these networks are used in real-life applications, such as search engines, and recommender systems. In general, such networks are modelled by constructing a low-dimensional Euclidean embedding of the vertices of the network, where proximity of the vertices in the Euclidean space hints the likelihood of an edge (link). In this work, we study the performance of such low-rank representations of real-life networks on a network classification problem.

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