DISC 2025 · 39th International Symposium on Distributed Computing, Berlin, Germany, October 2025
Algorithms with advice have received ample attention in the distributed and online settings, and they have recently proven useful also in dynamic settings. In this work we study local computation with advice: the goal is to solve a graph problem $\Pi$ with a distributed algorithm in $T(\Delta)$ communication rounds, for some function $T$ that only depends on the maximum degree $\Delta$ of the graph, and the key question is how many bits of advice per node are needed.
Some of our results regard Locally Checkable Labeling problems (LCLs), which is an important family of problems that includes various coloring and orientation problems on finite-degree graphs. These are constraint-satisfaction graph problems that can be defined with a finite set of valid input/output-labeled neighborhoods.
Our main results are:
Our work shows that for many problems the key threshold is not whether we can achieve, say, $1$ bit of advice per node, but whether we can make the advice arbitrarily sparse. To formalize this idea, we develop a general framework of composable schemas that enables us to build algorithms for local computation with advice in a modular fashion: once we have (1) a schema for solving $\Pi_1$ and (2) a schema for solving $\Pi_2$ assuming an oracle for $\Pi_1$, we can also compose them and obtain (3) a schema that solves $\Pi_2$ without the oracle. It turns out that many natural problems admit composable schemas, all of them can be solved with only $1$ bit of advice, and we can make the advice arbitrarily sparse.