network system designers

Abstract: Generative communication uses transmitted information to control stochastic generation at the receiver. A fundamental question is how much source-specific information must be communicated so that outputs generated at the receiver satisfy prescribed fidelity and reliability requirements while preserving the conditional source distribution. We address this question for sources on compact Riemannian manifolds by developing a generative rate-distortion-perception (GRDP) framework without common randomness. We formulate an asymptotic operational GRDP function under a geodesic distortion radius and a failure-probability constraint, and establish its single-letter characterization through an equivalent informational formulation. Furthermore, for the informational formulation with the high-reliability limit, we derive a geometric lower bound determined by the largest source probability mass of a region with bounded geodesic diameter, together with an upper bound based on probabilistic coverings of the source manifold. Small-radius analysis of the lower bound reveals the roles of intrinsic dimension and scalar curvature in the required communication rate. For uniformly distributed sources on compact homogeneous Riemannian manifolds, we prove that the bounds coincide, yielding an exact characterization of the limiting GRDP function. We further specialize this result to unit spheres, obtaining an explicit expression that relates the minimum communication rate to the geodesic distortion radius and intrinsic dimension, revealing that for the $d$-dimensional unit sphere with $d\ge2$, increasing the communication rate by $d$ bits per source symbol can reduce the distortion radius by more than half.

Abstract: We introduce and study the adaptive covering complexity C adapt S (s), defined as the minimum number of contiguous factor blocks required to partition a string s when each block is either a factor of a fixed external dictionary S or a factor of the target's already decoded prefix. This hybrid admissibility model formalizes the structural interaction between static dictionary matching and dynamic nonoverlapping self-referencing. Our main theoretical result is the exact logarithmic law which is tight and proves that dynamic self-reference can reduce phrase count by at most an exponential factor over static dictionary covering alone. We establish structural equivalences showing that the measure bridges word-segmented Relative Lempel-Ziv and non-overlapping LZ77, and we prove exact preset-history LZ77 sandwich bounds. Furthermore, we establish the optimality of the longest-match greedy parse via an exchange lemma and present an optimal implementation based on dual suffix automata running in O(M + n) time and space for a constant alphabet. These analytical results are complemented by counting bounds, stationary-ergodic asymptotic invariance, a dictionaryselection NP-hardness result, and a concise empirical illustration.

Abstract: In the context of graph reconstruction studies, this paper addresses the problem defined as follows: given a sample of vertex-coloured graphs, each associated with a number, does there exist a connected graph containing exactly the given number of induced subgraphs isomorphic to each graph in the sample, such that every edge of the solution graph is included in at least one of these subgraphs. This paper first shows that this problem is $Σ$______-complete. The associated optimisation problem is to determine the minimum number of vertices in a solution graph, if one exists. For instances consisting of two paths, we prove NP-completeness in general and give a polynomial-time dynamic programming algorithm when one path is monochromatic.

Abstract: In error correction, the difference between a codeword and a received word, a candidate noise sequence, carries information on the confidence of the codeword as a proposed decoding. For additive channels, it is known that if noise sequences are ranked in decreasing order of likelihood, a lower rank correlates with higher confidence in a decoding. Here we substantially expand considerations by exploring the relationship between the likelihood ranks arising in list decoding and the posterior probability of decoding correctness, establishing that the ranks associated with subsequent list entries carry reliability information that is not obtainable from the first rank alone. Building on the recent development of Soft-Output Guessing Random Additive Noise Decoding, we first show that, under a random codebook model, soft-output expressions for list decoding coincide with the exact finite-blocklength conditional probabilities of decoding correctness. For hard-decision maximum likelihood decoding, we characterize the asymptotic reliability of the first list entry. We identify a decision function whose sign determines whether the posterior correctness probability of the first entry in the list converges to one or zero and whose absolute value gives the exponential rate of convergence. Perhaps surprisingly, the asymptotic reliability of the first list entry depends only on the first, second and last ranks. For list size one, the result recovers the previously known likelihood-rank threshold for reliable decoding. For list size two, a sufficiently late second entry can imply that the first entry is asymptotically correct even when its rank, viewed in isolation, would indicate otherwise.

Abstract: The Matching-Match puzzle asks whether the vertices of a fixed graph can be colored so that the multiset of color pairs induced by its edges is exactly a prescribed multiset. We study how the complexity of this realization problem depends on the host graph. On the dense side, we give a polynomial-time algorithm for complete $k$-partite graphs for every fixed number $k$ of parts, with arbitrary precoloring and an arbitrary number of colors. We prove a sharp complement-degree threshold: the problem is polynomial-time solvable when $Δ(\overline G)\le1$, but NP-complete on completely uncolored graphs already when $Δ(\overline G)=2$. This yields a dichotomy for uniform complete multipartite graphs, and connected diameter two already suffices for NP-completeness. We also prove W[1]-hardness on cographs parameterized by the number of colors. On the sparse side, completely uncolored paths and cycles admit a linear-time characterization by Euler trails and circuits, while counting feasible colorings is $\#P$-complete on both classes. Counting is nevertheless polynomial-time solvable on stars and complete graphs, even with arbitrary precoloring. A decomposition-transfer theorem yields NP-completeness already at tree-depth two. A separate path-decomposition reduction gives a maximum-degree threshold between one and two for completely uncolored disconnected host graphs with unrestrictedly many colors. Components with at most two edges are tractable, while a disjoint union of $P_4$'s is NP-complete. Finally, precoloring restores tractability in several cases: star forests are polynomial when every center is precolored, and two broad precoloring regimes on length-two spiders are polynomial even when the number of colors is unbounded.

Abstract: A dictionary is a data structure which stores pairs (key, value). The operation query on input key returns value if the dictionary contains a pair (key, value) and nil otherwise. A dynamic dictionary also supports the insert and delete operations. The model with cells of bitsize $n + m + 1$ is used, where $n$ and $m$ are the bitsizes of keys and values. We give 2 dictionaries that use $(1+o(1))K$ cells to store $K$ pairs and in which query makes \emph{non-adaptive probes} to the data structure: a static one in which query makes $\smash{\widetilde O(n^2)}$ probes, and a dynamic one in which it makes $\smash{\widetilde O(n^2 \log K)}$ probes. Also, for the first time, a dictionary is given in which all 3 operations are non-adaptive, but the size is larger: $O(nK)$ cells. The constructions are explicit and, consequently, all operations run in time $\poly(n,m)$. 1-bitprobes storage schemes store a set $S \subseteq \{0,1\}^n$ and answer a membership query by reading a single bit. We introduce 1-bitprobes that are {\em dynamic}. Such a scheme is given whose size is smaller than in all previous constructions which are static. The \textsf{query} operation has runtime $n^{O(1)}$. The operations delete and insert have runtime $n^{O(\log n)}$. The proofs rely on a strategy for an online matching game played on a given bipartite graph. An opponent switches left nodes {\em on} and {\em off}. The strategy needs to assign an irrevocable match to a node when it is turned {\em on}. Such games were introduced in the companion paper~\cite{companion-Hall}. The applications in this paper rely on efficient strategies assuming that each node is turned {\em off} after a polynomial number of rounds. We present efficient strategies for (lossless) expanders.

Abstract: This paper studies fundamental graph optimization problems under differential privacy (DP) and shows new, reconstruction-based lower bounds. We consider a graph $G = (V, E, \vec{w})$ where the vertex set $V$ and edges $E$ are public and the weights $\mathbf{w}:E\rightarrow \mathbb{R}$ must be kept differentially private under an $\ell_1$ neighboring relation. For the problems of releasing a minimum-weight spanning tree and a minimum-weight perfect matching, we show new, tight error bounds of $Ω(n\cdot\log(m/n)/ε)$ on worst-case graphs with $n$ vertices and $m>2n$ edges. The upper bounds are known pure DP algorithms while the new lower bound holds even under approximate $(\varepsilon,δ)$-DP as long as $δ\leq (n/m)^{Ω(1)}$. Our lower bounds improve the $Ω(n/ε)$ lower bounds of Sealfon (PODS~'16). The fact that approximate DP does not reduce error for MST under the $\ell_1$ neighboring relation contrasts with the recent upper bound of Pagh et al. (PODS~'25) which shows that approximate DP allows much better error under the $\ell_\infty$ neighboring relation. Going beyond worst-case graphs, we give lower bounds for large families of sparse graphs with expansion properties. We show a lower bound of $Ω(n / ε)$ for the minimum spanning tree for any graph where the minimum cut is at least $Ω(\log(n))$. Finally, we consider the problem of private hierarchical clustering under Dasgupta's cost function (STOC~'16) and show the first approximate DP lower bound parameterized by the minimum weight of a balanced cut. This extends lower bounds of Deng et al. (ICLR~'25) to general graphs and to approximate DP.

Abstract: In decentralized decision-making, several agents connected according to a network graph aim at solving a classification problem by collecting streaming observations. Due to decentralization, they run an iterative algorithm where, at each iteration, they can only exchange information locally with their neighbors. While decentralized estimation solutions have been shown to match the performance of optimal centralized systems, we show here that surprisingly this conclusion does not hold for decentralized decision-making. Specifically, we prove that the error probability for the best decentralized decision strategy exhibits an irreducible loss with respect to the optimal centralized classifier. This result establishes a fundamental limit for the performance of any decentralized decision strategy. We obtain an analytical relation showing that this limit is related to the interplay between decentralization and classification. The first aspect appears through the distances between the nodes in the graph, while the second aspect plays through the moment generating functions of the likelihood ratios that describe the decision problem. By applying the derived closed-form relation to different network topologies and inference problems, we observe some interesting and perhaps unexpected behavior emerging. In particular, we characterize the scaling law (with the network size) for the loss over popular network topologies, showing that the error probabilities might differ by orders of magnitude; and we examine how performance is affected by the relative distance between informative and uninformative agents over the graph.