The structural and functional characteristic features of nodes can be analyzed by visualizing community structure in complex networks. Community detection helps us to detect nodes having similar behavior in a system and organize the network into a network of closely connected groups or modules. Network embedding technique represents the nodes of the input graph into vector space and preserves their inherent and topological features and can contribute significantly to various applications in network analysis. In this paper, we propose a novel community detection method using network embedding technique. Firstly, nodes of the graph are embedded in feature space of d dimensions, and then low-rank approximation is applied to avoid the results from being affected by noise or outliers. Further, k-means clustering is employed to find the centroids of the clusters in the network and followed by a gravitational search algorithm to improve the results of centroids of clusters. Finally, we compute the effectiveness of detected communities using different performance measures. Our method serves as a universal framework towards applying and bench-marking various embedding techniques in graphs for performing community detection. We perform the test using various evaluation criteria on several real-life and synthetic networks and the obtained result reveals the utility of the proposed algorithm.

中文翻译：

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使用网络嵌入和引力搜索算法在复杂网络中进行社区检测

通过可视化复杂网络中的社区结构，可以分析节点的结构和功能特征。社区检测帮助我们检测系统中具有相似行为的节点，并将网络组织成一个由紧密相连的组或模块组成的网络。网络嵌入技术将输入图的节点表示到向量空间并保留其固有和拓扑特征，并且可以对网络分析中的各种应用做出重大贡献。在本文中，我们提出了一种使用网络嵌入技术的新型社区检测方法。首先将图的节点嵌入到d维的特征空间中，然后应用低秩近似来避免结果受到噪声或异常值的影响。更多，使用k-means聚类来寻找网络中簇的质心，然后采用引力搜索算法来改进簇质心的结果。最后，我们使用不同的性能度量来计算检测到的社区的有效性。我们的方法作为一个通用框架，用于在图形中应用和基准测试各种嵌入技术以执行社区检测。我们在几个现实生活和合成网络上使用各种评估标准进行测试，获得的结果揭示了所提出算法的实用性。我们的方法作为一个通用框架，用于在图形中应用和基准测试各种嵌入技术以执行社区检测。我们在几个现实生活和合成网络上使用各种评估标准进行测试，获得的结果揭示了所提出算法的实用性。我们的方法作为一个通用框架，用于在图形中应用和基准测试各种嵌入技术以执行社区检测。我们在几个现实生活和合成网络上使用各种评估标准进行测试，获得的结果揭示了所提出算法的实用性。