发表机构
AI Research Center, Lomonosov Moscow State University（莫斯科罗蒙诺索夫国立大学AI研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ComNetX是与求解器无关的局部动态社区检测框架，通过多级社区状态的闭合与收缩实现高效更新，在保留求解器质量的同时大幅降低大图更新时间，适配多种后端模型。
AI 中文摘要
动态社区检测通常通过两种方式解决：全快照重新计算或求解器专属的动态流程。全快照重新计算保留了成熟静态求解器的语义，但在图更新较小时会重复处理未改变的图区域；求解器专属的动态方法可降低该成本，但其更新规则在不同目标、特征表示及实现间的可迁移性有限，且仅通过图距离限定计算范围可能会遗漏高质量求解器所需的社区上下文。本文提出ComNetX，一种与求解器无关的局部动态更新层次自适应框架：它维护多级社区状态，扩展更新区域，闭合受影响社区，并将这些社区收缩为紧凑的局部实例；这种受影响社区的闭合与收缩在将计算限制在图的变化部分的同时，保留了求解器上下文，且同一接口可将模块度启发式、使用节点特征的图聚类模型及原生动态求解器封装为局部后端。我们通过多后端实验对6个真实网络、基于拓扑后端的更长真实数据流及受控动态随机块模型压力流评估ComNetX，结果显示：ComNetX可在保留强模块度求解器质量的同时降低大图上的更新时间；在最大真实图的配对运行中，Local Leiden的最终模块度与全快照重新计算的差值在0.006以内，同时实现了41.9±0.2倍的加速；该组合协议还能识别出局部性失效、更适合全刷新的场景。
英文摘要
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.
Comments10 pages, 3 figures
