通过在约束条件下传播标签来检测网络社区
Detecting network communities by propagating labels under constraints
摘要：
我们研究了最近提出的标签传播算法（LPA）识别网络社区。我们重新制定的LPA作为一个等价的优化问题，给出了一个目标函数，其最大值对应于社区的解决方案。通过考虑目标函数的性质，我们发现了标签传播方法的概念和实际缺陷，最重要的是增加目标函数的值和提高社区质量之间的差距。为了解决这些缺陷，我们修改了优化问题中的目标函数，产生了各种受约束的标签传播算法;特别令人感兴趣的是最大限度地提高社区质量的模块化衡量标准的一种变体。所提出的算法的性能和实施细节进行了讨论。双方以及unipartite网络被视为。
英文摘要：
We investigate the recently proposed label-propagation algorithm (LPA) for identifying network communities. We reformulate the LPA as an equivalent optimization problem, giving an objective function whose maxima correspond to community solutions. By considering properties of the objective function, we identify conceptual and practical drawbacks of the label propagation approach, most importantly the disparity between increasing the value of the objective function and improving the quality of communities found. To address the drawbacks, we modify the objective function in the optimization problem, producing a variety of algorithms that propagate labels subject to constraints; of particular interest is a variant that maximizes the modularity measure of community quality. Performance properties and implementation details of the proposed algorithms are discussed. Bipartite as well as unipartite networks are considered.
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