Dense subgraph detection is a fundamental problem in network analysis for which few worst-case guarantees are known, motivating its study through the lens of fixed-parameter tractability. But for what parameter? Recent work has proposed parameterizing graphs by their degree of triadic closure, with a $c$-closed graph defined as one in which every vertex pair with at least $c$ common neighbors are themselves connected by an edge. The special case of enumerating all maximal cliques (and hence computing a maximum clique) of a $c$-closed graph is known to be fixed-parameter tractable with respect to $c$ (Fox et al., SICOMP 2020). In network analysis, sufficiently dense subgraphs are typically as notable and meaningful as cliques. We investigate the fixed-parameter tractability (with respect to $c$) of optimization and enumeration in $c$-closed graphs, for several notions of dense subgraphs. We focus on graph families that are the complements of the most well-studied notions of sparse graphs, including graphs with bounded degree, bounded treewidth, or bounded degeneracy, and provide fixed-parameter tractable enumeration and optimization algorithms for these families. To go beyond the special case of maximal cliques, we use a new combinatorial bound (generalizing the Moon-Moser theorem); new techniques for exploiting the $c$-closed condition; and more sophisticated enumeration algorithms.
翻译:密度子图检测是网络分析中的一个根本问题,对此很少有最坏的保证,它可以通过固定参数的可移动性镜片进行研究。但是,对于什么参数?最近的工作已经根据三重封闭的程度提出图表参数化参数化。 最近的工作已经根据三重封闭的程度提出了一个以美元为单位的封闭式图表,它的定义是每个至少有美元共同邻居的顶层对子的封闭式图表本身都有一个边缘连接。将一个美元封闭式图表的所有最大晶片(从而计算出一个最大的细片)的特例,据了解,美元封闭式图表可以固定的参数(Fox等人,SICOMP 2020)。在网络分析中,足够密集的子图通常与cliques一样明显和有意义。我们调查了固定的硬度对美元封闭式图表的优化和查点的可移动性(相对于美元),以若干密度的子图为单位。我们关注的图形组,这是最精密的图表组合,包括有约束度的精密度、约束式的精密度、闭式的精确度的直径直径直径直径直径直径直径直径直径直径直径直径直径直的图表。