arXiv subjects

Grigoriy Bokov

Publications and source records attributed to Grigoriy Bokov.

3 recordsLinked to original sources

arXiv2026

LD-Leiden: Local Parallel Community Detection in Large Dynamic Networks

Dynamic community detection must update high-quality modularity partitions after edge batches, yet full Leiden reruns make small changes scale with the whole snapshot. Existing dynamic methods reduce work but often alter Leiden refinement, keep limited hierarchy state, or restrict graph support. This paper presents LD-Leiden, a local dynamic Leiden method for weighted directed and undirected graphs that preserves the move-refine-aggregate pipeline and updates only repaired affected regions. Its novelty is the combination of an affected-frontier rule after statistic repair, exact subtract-add aggregate repair, and conflict-filtered parallel local moves; together these mechanisms bound update cost by the visited frontier rather than the full graph. On real streams and streamed static graphs with up to 214M vertices and 3.30B edges, LD-Leiden is 48.77x faster than warm-started Leidenalg in 100-batch runs while preserving a 0.996 final modularity ratio. On the common undirected benchmark set, it is 6.94x faster than DF-Leiden and 9.73x faster than NetworKit while obtaining higher final modularity; synthetic sequences support the predicted local edge-volume scaling.

cs.SI↗

arXiv2026

ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection

Dynamic community detection is commonly addressed either by full-snapshot recomputation or by solver-specific dynamic procedures. Full recomputation preserves the semantics of mature static solvers, but it repeatedly processes unchanged graph regions when updates are small. Solver-specific dynamic methods can reduce this cost, but their update rules often have limited transferability across objectives, feature representations, and implementations. In addition, localizing computation only by graph distance may omit community context needed by high-quality solvers. We introduce ComNetX, a solver-agnostic hierarchical adaptation framework for local dynamic updates. ComNetX maintains a multi-level community state, expands the updated region, closes it over affected communities, and contracts these communities into compact local instances. This affected-community closure and contraction preserve solver context while restricting computation to the changed part of the graph. The same interface can wrap modularity heuristics, graph-clustering models that use node features, and native dynamic solvers as local backends. We evaluate ComNetX through a multi-backend study on six real networks, longer real-data streams for topology-based backends, and controlled dynamic stochastic block model stress streams. The results show that ComNetX can preserve the quality of strong modularity-based solvers while reducing update time on large graphs: in paired runs on the largest real graph, Local Leiden keeps final modularity within 0.006 of full-snapshot recomputation while achieving a 41.9 +/- 0.2x speedup. The combined protocols also identify regimes where locality breaks down and a full refresh is preferable.

cs.SI↗

arXiv2026

A Complete Complexity Dichotomy for Cyclic Attractor Detection in Boolean Networks with Restricted Local Rules

Boolean networks (BNs) are finite models of interacting systems whose long-term behaviour is organised by attractors. After a transient phase, every synchronously updated BN reaches either a fixed state or a cyclic attractor. Therefore, deciding whether a prescribed cyclic behaviour exists is a basic computational task in finite-state network dynamics. Here we study this task for networks with restricted local rules: each node is updated by a Boolean rule from a fixed closed rule family, and the period is fixed in advance. We prove a complete dichotomy for every fixed period at least two. For each closed rule family, cyclic-attractor detection is either polynomial-time decidable or $\mathrm{NP}$-complete. The intractable cases are precisely those that can implement majority-like self-dual behaviour or mixed monotone conjunction-disjunction mechanisms. In the tractable cases, affine, purely conjunctive, or purely disjunctive structure reduces the problem to linear algebra or graph reachability. These criteria identify which restricted local rule families preserve computational tractability in finite Boolean network models.

cs.CC↗