Community detection is one of the popular research topics in the field of complex networks analysis. It aims to identify communities, represented as cohesive subgroups or clusters, where nodes in the same community link to each other more densely than others outside. Due to the interpretability, simplicity, flexibility, and generality, nonnegative matrix factorization (NMF) has become a very ideal model for community detection and lots of related methods have been presented. To facilitate research on NMF-based community detection, in this article, we make a comprehensive review on NMF-based methods for community detection, especially the state-of-the-art methods presented in high prestige journals or conferences. First, we introduce the basic principles of NMF and explain why NMF can detect communities and design a general framework of NMF-based community detection. Second, according to the applicable network types, we propose a taxonomy to divide the existing NMF-based methods for community detection into six categories, namely, topology networks, signed networks, attributed networks, multilayer networks, dynamic networks, and large-scale networks. We deeply analyze representative methods in every category. Finally, we summarize the common problems faced by all methods and potential solutions and propose four promising research directions. We believe that this survey can fully demonstrate the versatility of NMF-based community detection and serve as a useful guideline for researchers in related fields.

Agriculture is one of the main economic industries of a country. Application of information technologies in agriculture, smart agriculture aims to realize precision control of irrigation, fertilizer, diseases and insect pests prevention in the growing of crops. For the sake of obtaining the interest data, wireless sensor networks (WSNs) are used to collect the interest data in the farm field, and send the obtained data to the servers via wireless communication. Since the WSNs usually operate in the unlicensed spectrum, the available resource elements (REs) are scarce especially when a large number of sensor nodes are deployed in the farm field. To accommodate more sensor nodes and prolong the lifetime of the WSNs in agriculture, relay-aided non-orthogonal multiple access is introduced into the uplink transmission stage of the direct transmission from the sensor nodes to the sink node. NOMA can transmit multiple symbols simultaneously on the same RE by splitting them in the power domain and distinguish them according to diverse power levels of different symbols. The average sum data rate and outage probability of relay-aided NOMA in uplink transmission are theoretically analyzed. The numerical simulation results shows that the WSNs with relay-aided NOMA outperforms the traditional orthogonal multiple access (OMA) scheme in uplink transmission in WSNs in agriculture.

Community detection is of great help to understand the structures and functions of complex networks. It has become one of popular research topics in the field of complex networks analysis. Due to the simplicity, flexibility, effectiveness and better interpretability, Nonnegative Matrix Factorization (NMF)-based methods have been widely employed for community detection. However, most existing NMF-based community detection methods are linear and their performance is limited when facing networks with diversified structure information. In view of this, we propose a nonlinear NMF-based method named NMFGAAE, which is composed of two main modules: NMF and Graph Attention Auto-Encoder (GAAE). This approach can boost the performance of NMF-based community detection methods by the aid of graph neural networks and deep clustering. More specifically, GAAE introduces an attention mechanism directed by NMF-based community detection to learn the node representations, while NMF can simultaneously factor these representations to uncover the community structure. We design a unified framework to jointly optimize GAAE and NMF modules, which is very beneficial to obtain better community detection results. We conduct extensive experiments on synthetic and real-world networks. The results show that our NMFGAAE not only performs better than state-of-the-art NMF-based community detection methods, but also outperforms some network representation based baselines. More importantly, NMFGAAE indeed can boost the performance of NMF-based community detection methods.

As a new candidate waveform for the next generation of mobile communications, orthogonal chirp division multiplexing (OCDM) has attracted growing attention for its high spectrum efficiency and robustness to narrow-band interference or impulsive noise. Under vehicular communication channels with multiple lags and multiple Doppler shifts (MLMD), the signal suffers doubly selective (DS) fadings in the time and frequency domain, and data symbols modulated on orthogonal chirps interfere with each other. To address the problem of symbol detection of OCDM over MLMD channels, under the assumption that path attenuation factors, delays, and Doppler shifts of the channel are available, we first derive the closed-form channel matrix in the Fresnel domain and then propose a low-complexity method to approximate it as a sparse matrix. Based on the approximated Fresnel-domain channel, we propose a message-passing (MP) based detector to estimate the transmit symbols iteratively. Finally, under two MLMD channels (an underspread channel for terrestrial vehicular communications and an overspread channel for narrow-band underwater acoustic communications), Monte Carlo simulation results and analyses are provided to validate its advantages as a promising detector for OCDM.

In multimodal sentiment analysis, a significant challenge lies in quantifying the contribution of each modality and achieving effective modality fusion. This paper presents a Hierarchical Gating-Driven Transformer Fusion Model (HGTFM), which effectively achieves multimodal data fusion through an advanced transformer architecture and a Hierarchical Gated Fusion Module (HGFM). The model first encodes audio and video features using LSTM and CNN, respectively, while employing BERT to encode text features. This approach effectively enhances the model ability to manage long-range temporal dependencies. Furthermore, the model introduces HGFM, which dynamically integrates multimodal features across different levels after processing by the modality interaction network (MIN), thus intelligently adjusting the contribution weights of each modality. In particular, the model incorporates a Unimodal Label Generation Module (ULGM), which considers the differences between the modalities. This enables HGFM to more accurately identify the contributions of samples with significant modal variations, thus optimizing the module potential. The experimental results on several established benchmark datasets demonstrate that HGTFM achieves comparable performance improvements in multimodal sentiment analysis tasks.

As an emerging technique, index modulation (IM) can improve the system bit error rate (BER) performance due to the robustness of index bits over conventional modulated symbol bits. The existing IM aided orthogonal frequency division multiplexing (OFDM) schemes only employ one index modulator at the transmitter, which transmits a small amount of index bits in each transmission. In this paper, a novel IM technique, called cascade IM (CIM), is proposed to increase the proportion of the index bits in the transmission by combining the conventional IM with the multiple‐mode IM together. Subcarrier‐wise and subblock‐wise CIM schemes are proposed to achieve different spectral efficiency and diversity order for diverse scenarios in the next generation wireless communication networks. The optimal subcarrier‐wise maximum likelihood detector is proposed for OFDM‐CIM. To reduce the demodulation complexity, a novel tree search based and an iterative log‐likelihood ratio based detectors, which can avoid illegal index patterns in the search process, are developed for OFDM‐CIM. Monte Carlo simulations show that the proposed scheme achieves better BER performance than OFDM‐IM and appears as a competitive candidate of multi‐carrier transmission techniques for next generation wireless communication networks.