Module identification has been studied extensively in order to gain deeper understanding of complex systems, such as social networks as well as biological networks. Modules are often defined as groups of vertices in these networks that are topologically cohesive with similar interaction patterns with the rest of the vertices. Most of the existing module identification algorithms assume that the given networks are faithfully measured without errors. However, in many real-world applications, for example, when analyzing protein-protein interaction networks from high-throughput profiling techniques, there is significant noise with both false positive and missing links between vertices. In this paper, we propose a new model for more robust module identification by taking advantage of multiple observed networks with significant noise so that signals in multiple networks can be strengthened and help improve the solution quality by combining information from various sources.

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positive highthroughput profiling systems model module identification algorithms proteinprotein interaction networks missing links modules biological

.Bayesian module identification from multiple noisy networks. 2016 (1),.

"Bayesian module identification from multiple noisy networks" 2016

"Bayesian module identification from multiple noisy networks"