在符号网络中寻找带容错的平衡子图的可扩展算法
Scalable Algorithm for Finding Balanced Subgraphs with Tolerance in Signed Networks
AI总结：
针对符号网络最大平衡子图问题，本文提出纳入不规则容错的广义模型及基于区域的可扩展启发式算法，在效果和效率上均优于现有方法。
摘要：
符号网络以标记为正或负的边为特征，能够提供无符号图所不具备的关于交互动态的细致洞察。其核心任务是识别最大平衡子图，这对社交网络中的极化社区检测和金融投资组合分析等应用至关重要。然而，传统模型受限于完美划分假设，无法反映真实世界数据的复杂性。为弥补这一差距，我们提出一种创新的广义平衡子图模型，纳入了对不规则情况的容错能力。我们针对这一NP难问题设计的基于区域的启发式算法，在低时间复杂度与高质量结果之间取得平衡。对比实验验证了其相对于领先解决方案的优越性能，在传统和广义情境下均提供更高的有效性（尤其是更大的子图规模）和效率（实现最高100倍加速）。
英文摘要：
Signed networks, characterized by edges labeled as either positive or negative, offer nuanced insights into interaction dynamics beyond the capabilities of unsigned graphs. Central to this is the task of identifying the maximum balanced subgraph, crucial for applications like polarized community detection in social networks and portfolio analysis in finance. Traditional models, however, are limited by an assumption of perfect partitioning, which fails to mirror the complexities of real-world data. Addressing this gap, we introduce an innovative generalized balanced subgraph model that incorporates tolerance for irregularities. Our proposed region-based heuristic algorithm, tailored for this NP-hard problem, strikes a balance between low time complexity and high-quality outcomes. Comparative experiments validate its superior performance against leading solutions, delivering enhanced effectiveness (notably larger subgraph sizes) and efficiency (achieving up to 100x speedup) in both traditional and generalized contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
