Uncovering underlying community structures in complex networks has received considerable attention because of its importance in understanding structural attributes and group characteristics of networks. The algorithmic identification of such structures is a significant challenge. Local expanding methods have proven to be efficient and effective in community detection, but most methods are sensitive to initial seeds and built-in parameters. In this paper, we present a local expansion method by density-based clustering, which aims to uncover the intrinsic network communities by locating the structural centers of communities based on a proposed structural centrality. The structural centrality takes into account local density of nodes and relative distance between nodes. The proposed algorithm expands a community from the structural center to the border with a single local search procedure. The local expanding procedure follows a heuristic strategy as allowing it to find complete community structures. Moreover, it can identify different node roles (cores and outliers) in communities by defining a border region. The experiments involve both on real-world and artificial networks, and give a comparison view to evaluate the proposed method. The result of these experiments shows that the proposed method performs more efficiently with a comparative clustering performance than current state of the art methods.

Community structure is an important mesoscale topological characteristic of complex networks, which is significant for understanding structural features and organizational functions in networks. Local expansion methods have been proved to be efficient and effective for community detection. However, it has been shown that there are inherent drawbacks for these methods to uncover overlapping communities. Most methods are sensitive to initial seeds and built-in parameters, while others are inadequate to reveal the pervasive overlaps. In this paper, we propose a new local expansion method for uncovering overlapping communities based on structural centrality. The key idea of our approach is to locate structural centers of communities with the structural centrality and then expand these structural centers with a weighted strategy and a local search procedure. Experimental results both on artificial and real-world networks demonstrate that our method is effective and promising in term of finding overlapping community structures. We also show that our local expansion strategies are efficient in uncovering cohesive clusters and producing stable clustering results.

The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi–Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB.

Radar signal recognition plays a crucial role in modern electronic reconnaissance systems. With the increasing complexity of electromagnetic environments, radar signals are susceptible to noise interference under low signal-to-noise ratio (SNR) conditions, posing a challenge to accurate radar signal recognition. To address this issue, we propose a multilayer decomposition denoising empowered convolutional neural network (CNN) for radar signal recognition. Specifically, the original radar signals are first processed by multilayer decomposition denoising, which consists of variational mode decomposition (VMD), local mean decomposition (LMD), and wavelet thresholding (WT) in sequence, termed as VMD-LMD-WT. Then we use the Choi-Williams distribution (CWD) to convert the denoised signals into time-frequency images (TFIs). Finally, an improved CNN with dilated convolution is employed for radar signal recognition. Experiments demonstrate that the multilayer decomposition denoising method effectively improves the SNR of the original signal, which is beneficial for the subsequent recognition task. The recognition accuracy is improved by about 11% compared to that directly using the original signal. Furthermore, compared to the current network models, the proposed CNN network can efficiently extract the signal features and improve the detection accuracy of low probability of intercept (LPI) radar signals. The recognition accuracy reaches 75.3% for 12 signals when the SNR is low to -14dB.