Breadth-First Search
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Related Articles from SNS
Dynamic Breadth First Search with Predictions
arXiv:2606.01187v1 Announce Type: new Abstract: Given a graph $G(V,E)$ having $n$ vertices and $m$ edges, we maintain its Breadth-First Search (BFS) tree from source $s$ under an online sequence of edge updates in the prediction model. Our approach leverages a predicted update sequence aiding online processing.
Graph Traversal on Tensor Cores: A BFS Framework for Modern GPUs
arXiv:2606.05081v1 Announce Type: new Abstract: Modern GPUs have Tensor Cores (TCs) capable of extremely high-throughput matrix operations, yet graph algorithms remain difficult to accelerate because of their irregular and data-dependent execution patterns. This work presents BLEST, a TC-accelerated framework that reformulates Breadth-First Search (BFS) as a bit-level sparse matrix-vector computation while addressing the load imbalance, memory inefficiency, and synchronization overheads that...
Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
arXiv:2601.23207v2 Announce Type: replace Abstract: Understanding what graph neural networks can learn, especially their ability to learn to execute algorithms, remains a central theoretical challenge. In this work, we prove exact learnability results for graph algorithms under bounded-degree and finite-precision constraints. Our approach follows a two-step process.
A Novel Procedural Generation for Level Design of Mansions and Dungeons
arXiv:2606.03857v1 Announce Type: new Abstract: Procedural Content Generation (PCG) has become an essential technique in game development due to its ability to reduce production time and cost while increasing replayability and variety. However, when not aligned with level design principles, PCG can lead to incoherent spatial structures and poor gameplay experiences. This work proposes a PCG method guided by level design principles to generate structured indoor environments - such as houses,...
$O(n +f(k))$: Truly Linear FPT
Announce Type: new Abstract: Parameterized complexity has always been concerned with practical computing: by confining combinatorial explosion to a secondary parameter $k$, one can uncover why and how many NP-hard problems are effectively tackled in practice. Today, however, the scale of data has changed: scientists study Big Data, which is so large that even quadratic dependence in the total input size $n$ is unaffordable. Therefore, what constitutes a practical algorithm has also changed.