Lee Conrad, Reid Fergal, McDaid Aaron, Hurley Neil
Clique Research Cluster, Complex and Adaptive Systems Laboratory, University College Dublin, 8 Belfield Office Park, Clonskeagh, Dublin 4, Ireland.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Jun;83(6 Pt 2):066107. doi: 10.1103/PhysRevE.83.066107. Epub 2011 Jun 15.
In some social and biological networks, the majority of nodes belong to multiple communities. It has recently been shown that a number of the algorithms specifically designed to detect overlapping communities do not perform well in such highly overlapping settings. Here, we consider one class of these algorithms, those which optimize a local fitness measure, typically by using a greedy heuristic to expand a seed into a community. We perform synthetic benchmarks which indicate that an appropriate seeding strategy becomes more important as the extent of community overlap increases. We find that distinct cliques provide the best seeds. We find further support for this seeding strategy with benchmarks on a Facebook network and the yeast interactome.
在一些社会和生物网络中，大多数节点属于多个社群。最近有研究表明，一些专门设计用于检测重叠社群的算法在这种高度重叠的情况下表现不佳。在这里，我们考虑这类算法中的一类，即那些通过使用贪婪启发式方法将种子扩展为一个社群来优化局部适应度度量的算法。我们进行了综合基准测试，结果表明，随着社群重叠程度的增加，合适的种子策略变得更加重要。我们发现不同的团提供了最佳的种子。我们通过在Facebook网络和酵母相互作用组上的基准测试进一步支持了这种种子策略。
