The Maximum Independent Set problem is fundamental for extracting conflict-free structure from large graphs, with applications in scheduling, recommendation, and network analysis. However, existing heuristics can stagnate when search schedules are fixed and information from past solutions is underused, leading to wasted effort in low-quality regions of the search space. We present ARCIS, an efficient algorithm for mining large independent sets on massive graphs. ARCIS couples two main components. The first is an adaptive restart policy that refreshes exploration when progress slows. The second is Consensus-Guided Vertex Fixing, which restricts the search to the non-consensus region of the graph by fixing vertices consistently observed within a round. The consensus is maintained as a running intersection within each round, and because it is recomputed at every restart, the fixing is reversible. Vertices that later lose support are automatically unfixed and their neighborhoods re-enter the working graph, which corrects occasional mistakes while preserving progress. Experiments on 222 graphs from four benchmark suites show that ARCIS attains the best or tied-best solution quality in most instances while delivering competitive runtime and low variability. Ablation studies isolate the impact of each component, indicating that ARCIS is a practical and robust method for large-scale graph mining.

翻译：最大独立集问题是从大规模图中提取无冲突结构的基础性问题，在调度、推荐和网络分析等领域具有重要应用。然而，现有启发式算法在搜索调度固定且未充分利用历史解信息时容易陷入停滞，导致搜索空间低质量区域的计算资源浪费。本文提出ARCIS算法，一种用于大规模图上挖掘大独立集的高效算法。ARCIS耦合两个核心组件：其一是自适应重启策略，在搜索进展缓慢时刷新探索过程；其二是共识引导顶点固定机制，通过固定每轮迭代中持续观测到的顶点，将搜索限制在图的非共识区域。共识通过每轮内的运行交集进行维护，由于每次重启时重新计算，该固定机制具有可逆性。后续失去支持的顶点会自动解除固定，其邻域重新进入工作图，从而在保持搜索进度的同时修正偶然错误。在四个基准测试集的222个图上进行的实验表明，ARCIS在多数实例中取得最优或并列最优的解质量，同时具有竞争力的运行时间和较低的结果波动性。消融实验验证了各组件的影响，表明ARCIS是一种实用且鲁棒的大规模图挖掘方法。