Topological Properties Affect the Power of Network Coding in Decentralized Broadcast
Bibliographic record
Abstract
There exists a certain level of ambiguity regarding whether network coding can further improve download performance in P2P content distribution systems, as compared to commonly applied heuristics such as rarest first protocols. In this paper, we revisit the problem of broadcasting multiple data blocks from a single source in an overlay network using gossip-like protocols. Our new finding reveals that the marginal benefit of network coding critically depends on the dynamics of network topologies. We show that although network coding is optimal as a block selection mechanism, simple non-coding protocols are close to optimal in complete and random graphs, leading to marginal benefits of network coding. However, network coding demonstrates salient benefits in clustered and time-varying topologies, which are common in real-world systems with ISP-locality mechanisms implemented. Through both theoretical analysis and simulation results, we unveil the underlying reasons behind discrepancies in the power of network coding under different scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".