جستجوی مقالات مرتبط با کلیدواژه "community detection" در نشریات گروه "مواد و متالورژی"

In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping and disjoint detection of community in networks. In recent years, many researchers have concentrated on feature learning and network embedding methods for node clustering. These methods map the network into a lower-dimensional representation space. We propose a model in this research for learning graph representation using deep neural networks. In this method, a nonlinear embedding of the original graph is fed to stacked auto-encoders for learning the model. Then an overlapping clustering algorithm is performed to obtain overlapping communities. The effectiveness of the proposed model is investigated by conducting experiments on standard benchmarks and real-world datasets of varying sizes. Empirical results exhibit that the presented method outperforms some popular community detection methods.

Detecting communities plays a vital role in studying group level patterns of a social network and it can be helpful in developing several recommendation systems such as movie recommendation, book recommendation, friend recommendation and so on. Most of the community detection algorithms can detect disjoint communities only, but in the real time scenario, a node can be a memberof more than one community at the same time, that leads to overlapping communities. A novel approach is proposed to detect such overlapping communities by extending the definition of newman’s modularity for overlapping communities. The proposed algorithm is tested on LFR benchmark networks with overlapping communities and on real-world networks. The performance of the algorithm is evaluated using popular metrics such as ONMI, Omega Index, F-score and Overlap modularity and the results are compared with its competent algorithms. It is observed that extended modularity gain can detect highly modular structures in complex networks with overlapping communities.