Flow Subgraphs and Flow Network Design under End-to-End Power Dissipation Constraints
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908861246472192 |
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| author | Qiu, Zhihao Liu, Xinhan Noldus, Rogier Van Mieghem, Piet |
| author_facet | Qiu, Zhihao Liu, Xinhan Noldus, Rogier Van Mieghem, Piet |
| contents | We investigate how the underlying graph of a network supports a flow between a source node and a destination node and propose to compute the expected number of nodes and links that contribute to transferring items in random graphs. Since the transportation is associated with a \quotes{cost} or \quotes{power dissipation}, we further address how to construct a graph given predetermined end-to-end power dissipation, which can be reduced to the \quotes{inverse effective resistance problem} that asks for a weighted graph in which the effective resistance matrix equals a predetermined demand matrix. We propose a heuristic algorithm, \quotes{Resistor Gap Pruning} (RGP), which provides sparse graphs closely approximating the demand effective resistance and which shows stable performance across different demand scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02336 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Flow Subgraphs and Flow Network Design under End-to-End Power Dissipation Constraints Qiu, Zhihao Liu, Xinhan Noldus, Rogier Van Mieghem, Piet Mathematical Physics We investigate how the underlying graph of a network supports a flow between a source node and a destination node and propose to compute the expected number of nodes and links that contribute to transferring items in random graphs. Since the transportation is associated with a \quotes{cost} or \quotes{power dissipation}, we further address how to construct a graph given predetermined end-to-end power dissipation, which can be reduced to the \quotes{inverse effective resistance problem} that asks for a weighted graph in which the effective resistance matrix equals a predetermined demand matrix. We propose a heuristic algorithm, \quotes{Resistor Gap Pruning} (RGP), which provides sparse graphs closely approximating the demand effective resistance and which shows stable performance across different demand scenarios. |
| title | Flow Subgraphs and Flow Network Design under End-to-End Power Dissipation Constraints |
| topic | Mathematical Physics |
| url | https://arxiv.org/abs/2603.02336 |