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Undirected Multicast Network Coding Gaps via Locally Decodable Codes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The network coding problem asks whether data throughput in a network can be increased using coding (compared to treating bits as commodities in a flow). While it is well-known that a network coding advantage exists in directed graphs, the situation in undirected graphs is much less understood - in particular, despite significant effort, it is not even known whether network coding is helpful at all for unicast sessions.In this paper we study the multi-source multicast network coding problem in undirected graphs. There are k sources broadcasting each to a subset of nodes in a graph of size n. The corresponding combinatorial problem is a version of the Steiner tree packing problem, and the network coding question asks whether the multicast coding rate exceeds the tree-packing rate.We give the first super-constant bound to this problem, demonstrating an example with a coding advantage of Ω(log k). In terms of graph size, we obtain a lower bound of 2 Ω(sqrt log log n). We also obtain an upper bound of O(log n) on the gap.Our main technical contribution is a new reduction that converts locally-decodable codes in the low-error regime into multicast coding instances. This gives rise to a new family of explicitly constructed graphs, which may have other applications.

Original languageEnglish (US)
Title of host publicationProceedings - 2025 IEEE 66th Annual Symposium on Foundations of Computer Science, FOCS 2025
PublisherIEEE Computer Society
Pages2796-2812
Number of pages17
ISBN (Electronic)9798331571320
DOIs
StatePublished - 2025
Event66th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2025 - Sydney, Australia
Duration: Dec 14 2025Dec 17 2025

Publication series

NameProceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS
ISSN (Print)0272-5428

Conference

Conference66th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2025
Country/TerritoryAustralia
CitySydney
Period12/14/2512/17/25

All Science Journal Classification (ASJC) codes

  • General Computer Science

Keywords

  • graph algorithms
  • locally decodable codes
  • multicast
  • network coding

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