Uncovering Patterns of Participant-Invariant Influence in Networks

Fuente: arXiv
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Auteur principal: Shaojie, Min
Format: Preprint
Publié: 2023
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author Shaojie, Min
author_facet Shaojie, Min
contents In this paper, we explore the nature of influence in a network. The concept of participant-invariant influence is derived from an influence matrix M specifically designed to explore this phenomenon. Through nonnegative matrix factorization approximation, we managed to extract a participant-invariant matrix H representing a shared pattern that all participants must obey. The acquired H is highly field-related and can be further utilized to cluster factual networks. Our discovery of the unveiled participant-independent influence within network dynamics opens up new avenues for further research on network behavior and its implications.
format Preprint
id arxiv_https___arxiv_org_abs_2312_02906
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Uncovering Patterns of Participant-Invariant Influence in Networks
Shaojie, Min
Social and Information Networks
In this paper, we explore the nature of influence in a network. The concept of participant-invariant influence is derived from an influence matrix M specifically designed to explore this phenomenon. Through nonnegative matrix factorization approximation, we managed to extract a participant-invariant matrix H representing a shared pattern that all participants must obey. The acquired H is highly field-related and can be further utilized to cluster factual networks. Our discovery of the unveiled participant-independent influence within network dynamics opens up new avenues for further research on network behavior and its implications.
title Uncovering Patterns of Participant-Invariant Influence in Networks
topic Social and Information Networks
url https://arxiv.org/abs/2312.02906