Community Detection In Multilayer Networks Research Articles

Multi-layer network community detection model based on attributes and social interaction intensity

With the rapid development of mobile communications and electronic technology, relevant network systems have become larger in scale and exhibited different hierarchical relationships. The complex relationships and links among different users make it more difficult to ultimately obtain data on a whole system. Even with the availability of powerful aggregation tools, most companies cannot afford the associated human and financial costs. However, existing multi-layer network community detection methods are well suited for attribute-based community detection. Therefore, this paper proposes a multi-layer local community detection model that is based on attribute and structure information. This model can effectively utilize node attribute information and the similarity strength information revealed by social exchanges to improve the accuracy of community detection in multi-layer networks. Unlike classical multi-layer and global community detection algorithms, this algorithm is robust on most datasets because of its modularity and computational efficiency.

Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization

Many complex systems are composed of coupled networks through different layers, where each layer represents one of many possible types of interactions. A fundamental question is how to extract communities in multi-layer networks. The current algorithms either collapses multi-layer networks into a single-layer network or extends the algorithms for single-layer networks by using consensus clustering. However, these approaches have been criticized for ignoring the connection among various layers, thereby resulting in low accuracy. To attack this problem, a quantitative function (multi-layer modularity density) is proposed for community detection in multi-layer networks. Afterward, we prove that the trace optimization of multi-layer modularity density is equivalent to the objective functions of algorithms, such as kernel $K$ -means, nonnegative matrix factorization (NMF), spectral clustering and multi-view clustering, for multi-layer networks, which serves as the theoretical foundation for designing algorithms for community detection. Furthermore, a S emi- S upervised j oint N onnegative M atrix F actorization algorithm ( S2-jNMF ) is developed by simultaneously factorizing matrices that are associated with multi-layer networks. Unlike the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the S2-jNMF algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method outperforms the state-of-the-art approaches for community detection in multi-layer networks.

Ensemble-based community detection in multilayer networks

The problem of community detection in a multilayer network can effectively be addressed by aggregating the community structures separately generated for each network layer, in order to infer a cons...

Local community detection in multilayer networks

The problem of local community detection in graphs refers to the identification of a community that is specific to a query node and relies on limited information about the network structure. Existing approaches for this problem are defined to work in dynamic network scenarios, however they are not designed to deal with complex real-world networks, in which multiple types of connectivity might be considered. In this work, we fill this gap in the literature by introducing the first framework for local community detection in multilayer networks (ML-LCD). We formalize the ML-LCD optimization problem and provide three definitions of the associated objective function, which correspond to different ways to incorporate within-layer and across-layer topological features. We also exploit our framework to generate multilayer global community structures. We conduct an extensive experimentation using seven real-world multilayer networks, which also includes comparison with state-of-the-art methods for single-layer local community detection and for multilayer global community detection. Results show the significance of our proposed methods in discovering local communities over multiple layers, and also highlight their ability in producing global community structures that are better in modularity than those produced by native global community detection approaches.