Given a set @math of community detection algorithms and a graph @math as inputs, we propose two ensemble methods @math and @math that (respectively) identify disjoint and overlapping communities in @math . @math transforms a graph into a latent feature space by leveraging multiple base solutions and discovers disjoint community structure. @math groups similar base communities into a meta-community and detects both disjoint and overlapping community structures. Experiments are conducted at different scales on both synthetically generated networks as well as on several real-world networks for which the underlying ground-truth community structure is available. Our extensive experiments show that both algorithms outperform state-of-the-art non-ensemble algorithms by a significant margin. Moreover, we compare @math and @math with a recent ensemble method for disjoint community detection and show that our approaches achieve superior performance. To the best of our knowledge, @math is the first ensemble approach for overlapping community detection.
@cite_33: The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics.
A consensus clustering'' @cite_33 approach was recently proposed which leverages a consensus matrix to produce a disjoint community structure which outperformed previous approaches. Our work differs from this approach in at least three significant ways: (i) they measure the number of times two vertices are assigned to the same community, thus ignoring the global similarity of vertices; whereas we capture the global similarity by representing the network within a feature space and grouping redundant base communities into meta communities; (ii) they either take multiple algorithms or run a particular algorithm multiple times for generating an ensemble matrix, whereas we consider both options; (iii) we are the first to show how aggregating multiple disjoint base communities can lead to discover both disjoint and overlapping community structures simultaneously. We show experimentally that beats consensus clustering.