Uncovering Patterns of Participant-Invariant Influence in Networks
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866914326447652864 |
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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 |