Abstract
We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightforwardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and intensity weight(s) over the network each. Our least-squares additive criterion allows us to search for communities one-by-one and to find each community by adding entities one by one. A focus of this paper is that the feature-space data part is converted into a similarity matrix format. The similarity/link values can be used in either of two modes: (a) as measured in the same scale so that one may can meaningfully compare and sum similarity values across the entire similarity matrix (summability mode), and (b) similarity values in one column should not be compared with the values in other columns (nonsummability mode). The two input matrices and two modes lead us to developing four different Iterative Community Extraction from Similarity data (ICESi) algorithms, which determine the number of communities automatically. Our experiments at real-world and synthetic datasets show that these algorithms are valid and competitive.
Highlights
Based on a thorough computational experiment, our choices can be described as follows: the combination of Range standardization and Uniform methods is the combination of data pre-processing techniques for all the Iterative Community Extraction from Similarity data (ICESi) methods at almost all synthetic data generated, except for the following cases
We explore whether the doubly-greedy least-squares approach proposed in [34] can be successfully applied to feature-rich networks at which the feature-related part is converted to a similarity matrix format
Similarity data are considered as measured in the same scale so that one can meaningfully compare and sum similarity values across the entire similarity matrix
Summary
Community detection is a popular research subject. A community is a group of relatively densely inter-connected nodes that are similar in the feature space too. A number of papers with various approaches to identifying communities in feature-rich networks have been published. We follow [3] to divide community detection methods according to the stage of the process of finding communities at which the two data types, network and features, are merged together.
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