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Yann Bourreau

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STOC Conference 2025 Conference Paper

Faster Distributed Δ-Coloring via Ruling Subgraphs

Yann Bourreau
Sebastian Brandt 0002
Alexandre Nolin

Brooks’ theorem states that all connected graphs but odd cycles and cliques can be colored with Δ colors, where Δ is the maximum degree of the graph. Such colorings have been shown to admit non-trivial distributed algorithms [Panconesi and Srinivasan, Combinatorica 1995] and have been studied intensively in the distributed literature. In particular, it is known that any deterministic algorithm computing a Δ-coloring requires Ω(log n ) rounds in the LOCAL model [Chang, Kopelowitz, and Pettie, FOCS 2016], and that this lower bound holds already on constant-degree graphs. In contrast, the best upper bound in this setting is given by an O (log 2 n )-round deterministic algorithm that can be inferred already from the works of [Awerbuch, Goldberg, Luby, and Plotkin, FOCS 1989] and [Panconesi and Srinivasan, Combinatorica 1995] roughly three decades ago, raising the fundamental question about the true complexity of Δ-coloring in the constant-degree setting. We answer this long-standing question almost completely by providing an almost-optimal deterministic O (log n log * n )-round algorithm for Δ-coloring, matching the lower bound up to a log * n -factor. Similarly, in the randomized LOCAL model, we provide an O (loglog n log * n )-round algorithm, improving over the state-of-the-art upper bound of O (log 2 log n ) [Ghaffari, Hirvonen, Kuhn, and Maus, Distributed Computing 2021] and almost matching the Ω(loglog n )-round lower bound by [BFHKLRSU, STOC 2016]. Our results make progress on several important open problems and conjectures. One key ingredient for obtaining our results is the introduction of ruling subgraph families as a novel tool for breaking symmetry between substructures of a graph, which we expect to be of independent interest.

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NeurIPS Conference 2024 Conference Paper

Efficient Streaming Algorithms for Graphlet Sampling

Yann Bourreau
Marco Bressan
T-H. Hubert Chan
Qipeng Kuang
Mauro Sozio

Given a graph $G$ and a positive integer $k$, the Graphlet Sampling problem asks to sample a connected induced $k$-vertex subgraph of $G$ uniformly at random. Graphlet sampling enhances machine learning applications by transforming graph structures into feature vectors for tasks such as graph classification and subgraph identification, boosting neural network performance, and supporting clustered federated learning by capturing local structures and relationships. A recent work has shown that the problem admits an algorithm that preprocesses $G$ in time $O(nk^2 \log k + m)$, and draws one sample in expected time $k^{O(k)} \log n$, where $n=|V(G)|$ and $m=|E(G)|$. Such an algorithm relies on the assumption that the input graph fits into main memory and it does not seem to be straightforward to adapt it to very large graphs. We consider Graphlet Sampling in the semi-streaming setting, where we have a memory of $M = \Omega(n \log n)$ words, and $G$ can be only read through sequential passes over the edge list. We develop a semi-streaming algorithm that preprocesses $G$ in $p={O}(\log n)$ passes and samples $\Theta(M k^{-O(k)})$ independent uniform $k$-graphlets in $O(k)$ passes. For constant $k$, both phases run in time $O((n+m)\log n)$. We also show that the tradeoff between memory and number of passes of our algorithms is near-optimal. Our extensive evaluation on very large graphs shows the effectiveness of our algorithms.

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