Information Propagation Research Articles

Evaluation index system for carbon information disclosure quality in China's electric power sector based on a mutual information and back propagation neural network model

This paper employs the Mutual Information (MI) and Back Propagation (BP) neural network model to screen the preliminarily constructed evaluation index system for carbon information disclosure (CID) quality of public companies in China's electric power sector (EPS), which is subsequently incorporated into the fuzzy comprehensive evaluation (FCE) method for evaluation application. The results show that (1) after screening, 19 out of the 31 preliminarily constructed indicators constitute an optimal index set, and (2) the evaluation application of the screened index system validates the feasibility and applicability of the index system. Simultaneously, the evaluation results reveal a generally low CID quality in the EPS.

Adaptive physics-encoded graph neural network for health stage assessment of liquid-propellant rocket engines

The improvement of reliable health monitoring system for liquid-propellant rocket engines (LREs) is a crucial part for reusable launch vehicle, which contributes to providing competitive and cost-effective propulsion systems. Thus, it accentuates the need for reliable and quick health stage assessment of system and follow-up damage-mitigating control. In this paper, we propose a novel adaptive physics-encoded graph neural network for health stage assessment of LREs. Our approach embeds the relations of different sensors obtained through expert experience, which contributes to constructing a physical graph layer. To better capture the information contained in all the sensor data, a novel convolutional layer of adaptive auto-regressive moving average filters is designed, which considers the personalized information propagation needs of each neural network layer. The performance of the proposed method is quantified with data obtained from physics simulations and real-world engineering systems. The results show that our model has potential applicability for the health stage assessment of LREs with high accuracy.

Cascaded Adaptive Graph Representation Learning for Image Copy-Move Forgery Detection

In the realm of image security, there has been a burgeoning interest in harnessing deep learning techniques for the detection of digital image copy-move forgeries, resulting in promising outcomes. The generation process of such forgeries results in a distinctive topological structure among patches, and collaborative modeling based on these underlying topologies proves instrumental in enhancing the discrimination of ambiguous pixels. Despite the attention received, existing deep learning models predominantly rely on convolutional neural networks (CNNs), falling short in adequately capturing correlations among distant patches. This limitation impedes the seamless propagation of information and collaborative learning across related patches. To address this gap, our work introduces an innovative framework for image copy-move forensics rooted in graph representation learning. Initially, we introduce an adaptive graph learning approach to foster collaboration among related patches, dynamically learning the inherent topology of patches. The devised approach excels in promoting efficient information flow among related patches, encompassing both short-range and long-range correlations. Additionally, we formulate a cascaded graph learning framework, progressively refining patch representations and disseminating information to broader correlated patches based on their updated topologies. Finally, we propose a hierarchical cross-attention mechanism facilitating the exchange of information between the cascaded graph learning branch and a dedicated forgery detection branch. This equips our method with the capability to jointly grasp the homology of copy-move correspondences and identify inconsistencies between the target region and the background. Comprehensive experimental results validate the superiority of our proposed scheme, providing a robust solution to security challenges posed by digital image manipulations.

Characterizing Information Propagation in Fringe Communities on Telegram

Characterizing Information Propagation in Fringe Communities on Telegram

PGCF: Perception graph collaborative filtering for recommendation

Extensive studies have fully proved the effectiveness of collaborative filtering (CF) recommendation models based on graph convolutional networks (GCNs). As an advanced interaction encoder, however, GCN-based CF models do not differentiate neighboring nodes, which will lead to suboptimal recommendation performance. In addition, most GCN-based CF studies pay insufficient attention to the loss function and they simply select the Bayesian personalized ranking (BPR) loss function to train the model. However, we believe that the loss function is as important as the interaction encoder and deserves more attentions. To address the above issues, we propose a novel GCN-based CF model, named Perception Graph Collaborative Filtering (PGCF). Specifically, for the interaction encoder, we design a neighborhood-perception GCN to enhance the aggregation of interest-related information of the target node during the information aggregation process, while weakening the propagation of noise and irrelevant information to help the model learn better embedding representation. For the loss function, we design a margin-perception Bayesian personalized ranking (MBPR) loss function, which introduces a self-perception margin, requiring the predicted score of the user-positive sample to be greater than that of the user-negative sample, and also greater than the sum of the predicted score of the user-negative sample and the margin. The experimental results on five benchmark datasets show that PGCF is significantly superior to multiple existing CF models.

Progress in Spin Logic Devices Based on Domain-Wall Motion.

Spintronics, utilizing both the charge and spin of electrons, benefits from the nonvolatility, low switching energy, and collective behavior of magnetization. These properties allow the development of magnetoresistive random access memories, with magnetic tunnel junctions (MTJs) playing a central role. Various spin logic concepts are also extensively explored. Among these, spin logic devices based on the motion of magnetic domain walls (DWs) enable the implementation of compact and energy-efficient logic circuits. In these devices, DW motion within a magnetic track enables spin information processing, while MTJs at the input and output serve as electrical writing and reading elements. DW logic holds promise for simplifying logic circuit complexity by performing multiple functions within a single device. Nevertheless, the demonstration of DW logic circuits with electrical writing and reading at the nanoscale is still needed to unveil their practical application potential. In this review, we discuss material advancements for high-speed DW motion, progress in DW logic devices, groundbreaking demonstrations of current-driven DW logic, and its potential for practical applications. Additionally, we discuss alternative approaches for current-free information propagation, along with challenges and prospects for the development of DW logic.

Graph Transformer Network Incorporating Sparse Representation for Multivariate Time Series Anomaly Detection

Cyber–physical systems (CPSs) serve as the pivotal core of Internet of Things (IoT) infrastructures, such as smart grids and intelligent transportation, deploying interconnected sensing devices to monitor operating status. With increasing decentralization, the surge in sensor devices expands the potential vulnerability to cyber attacks. It is imperative to conduct anomaly detection research on the multivariate time series data that these sensors produce to bolster the security of distributed CPSs. However, the high dimensionality, absence of anomaly labels in real-world datasets, and intricate non-linear relationships among sensors present considerable challenges in formulating effective anomaly detection algorithms. Recent deep-learning methods have achieved progress in the field of anomaly detection. Yet, many methods either rely on statistical models that struggle to capture non-linear relationships or use conventional deep learning models like CNN and LSTM, which do not explicitly learn inter-variable correlations. In this study, we propose a novel unsupervised anomaly detection method that integrates Sparse Autoencoder with Graph Transformer network (SGTrans). SGTrans leverages Sparse Autoencoder for the dimensionality reduction and reconstruction of high-dimensional time series, thus extracting meaningful hidden representations. Then, the multivariate time series are mapped into a graph structure. We introduce a multi-head attention mechanism from Transformer into graph structure learning, constructing a Graph Transformer network forecasting module. This module performs attentive information propagation between long-distance sensor nodes and explicitly models the complex temporal dependencies among them to enhance the prediction of future behaviors. Extensive experiments and evaluations on three publicly available real-world datasets demonstrate the effectiveness of our approach.

LMFormer: Lightweight and multi-feature perspective via transformer for human pose estimation

The effectiveness of Token Mixer in visual tasks is well-established; however, its high computational complexity and a relatively singular spatial relationship modeling perspective present challenges. In this study, we propose LMFormer, a hybrid model based on CNN and Transformer architectures for human pose estimation. To achieve this, we first design a lightweight multi-feature perspective Token Mixer, using a lightweight feature reconstruction strategy to simultaneously aggregate the spatial and channel feature information, thereby enhancing the performance and generalization capabilities of the model. Subsequently, we explore multi-scale information interaction by developing an iterative multi-feature weighting module, coupled with the design of a multi-scale information propagation mechanism incorporated into the skip connections. Finally, we validate the effectiveness of the network on benchmark datasets, including COCO, MPII, and CrowdPose, utilizing a multi-scale deep supervision strategy. Extensive experiments demonstrate that LMFormer, with reduced computational complexity, comprehensively captures multi-scale features, resulting in significant performance improvements. Specifically, LMFormer-B achieves an AP score of 65.8 on the COCO val dataset, surpassing MobileNetV2 and ShuffleNetV2 by 1.0 and 5.6 points, respectively. Moreover, its parameters are merely 19.8% and 25% of MobileNetV2 and ShuffleNetV2, with corresponding GFLOPs at 43.8% and 50%. We aim to provide new insights into lightweight and efficient feature extraction strategies, as well as efficient Token Mixer designs.

KGCFRec: Improving Collaborative Filtering Recommendation with Knowledge Graph

Traditional collaborative filtering (CF)-based recommendation systems are often challenged by data sparsity. The recent research has recognized the potential of integrating new information sources, such as knowledge graphs, to address this issue. However, a common drawback is the neglect of the interplay between user–item interaction data and knowledge graph information, resulting in insufficient model performance due to coarse-grained feature fusion. To bridge this gap, in this paper, we propose a novel graph neural network (GNN) model called KGCFRec, which leverages both Knowledge Graph and user–item Collaborative Filtering information for an enhanced Recommender system. KGCFRec employs a dual-channel information propagation and aggregation mechanism to generate distinct representations for the collaborative knowledge graph and the user–item interaction graph. This is followed by an attention mechanism that adaptively fuses the knowledge graph with collaborative information, thereby refining the representations and narrowing the gap between them. The experiments conducted on three real-world datasets demonstrate that KGCFRec outperforms state-of-the-art methods. These promising results underscore the capability of KGCFRec to enhance recommendation accuracy by integrating knowledge graph information.

Information propagation of focus wave mode localized waves in anisotropic turbulent seawater

The evolution of the information transfer capability of an optical system for underwater focused wave mode localized wave (FWMLW) in anisotropic weakly turbulent absorbing seawater is studied. By developing the probability distribution function as well as the detection probability of the vortex modes carried by the FWMLW and the average bit error rate of the FWMLW underwater system, the information capacity of the FWMLW system with a pointing error is modeled. Through a numerical analysis of the effects of turbulent seawater and optical system parameters on the built light intensity, the detection probability, and the information capacity models, we find that the FWMLW system has an optimal delay time determined by the spectrum bandwidth when the spectrum bandwidth is greater than 1. The information capacity of the FWMLW system is higher than that of the X localized wave system under the same turbulent seawater channel condition, and FWMLW is a better optical signal source for vortex mode division multiplexing underwater systems than a Bessel–Gaussian beam.

A maize seed variety identification method based on improving deep residual convolutional network.

Seed quality and safety are related to national food security, and seed variety purity is an essential indicator in seed quality detection. This study established a maize seed dataset comprising 5877 images of six different types and proposed a maize seed recognition model based on an improved ResNet50 framework. Firstly, we introduced the ResStage structure in the early stage of the original model, which facilitated the network's learning process and enabled more efficient information propagation across the network layers. Meanwhile, in the later residual blocks of the model, we introduced both the efficient channel attention (ECA) mechanism and depthwise separable (DS) convolution, which reduced the model's parameter cost and enabled the capturing of more precise and detailed features. Finally, a Swish-PReLU mixed activation function was introduced globally to improve the overall predictive power of the model. The results showed that our model achieved an impressive accuracy of 91.23% in corn seed classification, surpassing other related models. Compared with the original model, our model improved the accuracy by 7.07%, reduced the loss value by 0.19, and decreased the number of parameters by 40%. The research suggested that this method can efficiently classify corn seeds, holding significant value in seed variety identification.

LPI radar waveform recognition based on semi-supervised model all mean teacher

Low probability of intercept (LPI) radar signal identification plays an important role in electronic warfare, but most existing algorithms are proposed under the condition of sufficient samples, ignoring the problem of a small amount of labeled data in the actual electromagnetic environment. To solve the problem, in this paper, a semi-supervised learning model All Mean Teacher (AMT) based on Mean Teacher (MT) is proposed. First, the LPI radar signal is transformed into Time-frequency images (TFIs) by using the Choi-Williams distribution, and Random Erasing is used for TFIs which improves the generalization ability of the model. Then the Multi-headed Self-Attention Network (MSA-Net) is aimed to extract features, combined with AMT to realize the automatic waveform recognition of radar signals. MSA-Net facilitates feature information propagation by computing contrast costs on TFIs between the student and teacher networks. It solves the problem that TFIs are not easy to train for small amounts of labeled data, improving the accuracy of signal recognition in semi-supervised learning scenarios. Experimental results show that the average recognition accuracy of the proposed method is up to 85.7% at a signal-to-noise ratio of -8 dB.

Hierarchical spatial attention-based cross-scale detection network for Digital Works Supervision System (DWSS)

Vision-based on-site detection is a crucial component of the Digital Works Supervision System (DWSS). However, accurate detecting cross-scale objects in complicated construction sites remains a challenge. In this research, a hierarchical spatial attention-based cross-scale detection network is designed to address these challenges and provide accurate on-site information for the DWSS. Firstly, a convolutional neural network is constructed to extract multi-scale feature maps with different levels of information. Furthermore, a hierarchical spatial attention mechanism is proposed to facilitate information propagation and complement between multi-scale feature maps. Secondly, the cascade detection mechanism is designed to improve the detection performance of cross-scale objects. Large-scale feature maps with more detailed information are used for the detection of small-scale objects such as workers. Small-scale feature maps with semantic features are used for the detection of large-scale objects such as construction machines. Finally, the on-site information is automatically extracted from detection results and converted into suitable data formats to generate on-site reports for the DWSS. Experiments demonstrate that the proposed method can achieve SOTA detection performance on both MOCS and SODA dataset, especially for the small-scale objects and complicated construction scenarios.

Efficient Deformation Learning of Varied Garments with a Structure-Preserving Multilevel Framework

Due to the highly nonlinear behavior of clothing, modelling fine-scale garment deformation on arbitrary meshes under varied conditions within a unified network poses a significant challenge. Existing methods often compromise on either model generalization, deformation quality, or runtime speed, making them less suitable for real-world applications. To address it, we propose to incorporate multi-source graph construction and pooling to achieve a novel graph learning scheme. We first introduce methods for extracting cues from different deformation correlations and transform the garment mesh into a comprehensive graph enriched with deformation-related information. To enhance the learning capability and generalizability of the model, we present structure-preserving pooling and unpooling strategies for the mesh deformation task, thereby improving information propagation across the mesh and enhancing the realism of deformation. Lastly, we conduct an attribution analysis and visualize the contribution of various vertices in the graph to the output, providing insights into the deformation behavior. The experimental results demonstrate superior performance against state-of-the-art methods.

Modeling dynamic spatio-temporal correlations and transitions with time window partitioning for traffic flow prediction

Predicting traffic flow represents a critical undertaking within the domain of Intelligent Transportation Systems (ITS), given its pivotal role in optimizing traffic management strategies. Nevertheless, this endeavor is fraught with challenges, stemming from the inherent complexity and dynamic nature of traffic systems. Existing methods still suffer from the following three main limitations: (i) modeling the spatio-temporal correlations in a static way, restricting the ability to learn dynamic correlations; (ii) only capturing short-term local temporal features while neglecting long-term temporal patterns; (iii) cannot learn the latent relationships between traffic nodes at different time steps. Therefore, we propose a novel method named InOutformer based on In-Time-Window self-attention and Out-Time-Window self-attention to predict traffic flow. This method can effectively capture both short-term local temporal features and model long-term global temporal patterns, while also capturing dynamic spatial correlations between nodes. Specifically, we first propose a novel time window partitioning mechanism to replace the conventional sliding time window approach, in order to capture dynamic temporal features. Second, two specially designed temporal self-attention modules are used to learn short-term local temporal features and model long-term global temporal patterns, respectively. Then, a 2D-attention method is devised to capture the spatial correlations of nodes at different time steps. Moreover, by exploiting transition flow data, the detrimental effects due to spatial information propagation delays are effectively mitigated. Extensive experiments on four real-world traffic datasets demonstrate that the proposed model InOutformer outperforms the baselines, including several state-of-the-art methods.

Multi-AUV underwater static target search method based on consensus-based bundle algorithm and improved Glasius bio-inspired neural network

This research introduces a hierarchical strategy for static target searches with multi-autonomous underwater vehicles (AUVs) to optimize cumulative search rewards. The approach comprises two primary elements: task allocation and path planning. A Voronoi diagram segments regions based on peak detection via a maximum filter in the task allocation stage. Then, a consensus-based bundling algorithm ensures the load-balanced distribution of peak sub-regions across AUVs, while a dynamic cooperation mechanism allows for dynamic adjustment of task allocation, thereby increasing the system's operational flexibility. Path planning employs an improved Glasius bio-inspired neural network, leveraging analogies to convolution processes and incorporating mean pooling, multiple convolutions, and resampling. This method enhances global information propagation and optimizes path point selection through a discounted reward function evaluating adjacent nodes, thus boosting the search efficiency of individual AUVs. Simulation experiments validate the method's effectiveness and robustness in multi-AUV static target searches, demonstrating its potential to improve search efficiency.

An embedding model for temporal knowledge graphs with long and irregular intervals

As the basis of various downstream applications such as automated question answering and knowledge reasoning, Temporal Knowledge Graph (TKG) embedding learning has attracted many interests. Recent works have explored several methods to learn dynamic embedding for TKGs and led to success in many applications, however, they are difficult to be optimized. In order to accurately catch semantic information propagation of entity-to-entity and entity-to-relation, as well as track dynamics of entities and relations in continuous time, we proposed a novel TKG embedding model ODETKGE. Instead of using recurrent neural network to model dynamics of semantic representations in continuous time, we leverage neural ordinary differential equations to model the dynamic evolution, which enable us to capture long-range dependencies between embeddings at different timestamps, track the dynamics of TKGs with irregular intervals and remain computationally efficient. Furthermore, to effectively model semantics of knowledge graph structure, we combine entity-relation collaboratively updated graph convolutional networks and neural ordinary differential equations. Additionally, to acquire important semantics such as creation and disappearance of relations, we utilize a graph transition layer. Comparing with RNN based models, the proposed ODETKGE can use fewer parameters to achieve deeper layers, and make the transformation of representations between two timestamps smoother. Extensive experiments have been executed on real-world datasets and the results verify that ODETKGE is superior than the state-of-the-art models in link forecasting, long-interval link forecasting, irregular-interval link forecasting, ablation study and training time.

Relation-aware heterogeneous graph neural network for entity alignment

Entity alignment refers to finding equivalent entities from different knowledge graphs. Most of the existing entity alignment methods are studied based on homogeneous graphs. However, the knowledge graph is a heterogeneous graph containing many types of nodes, such as entities, relations, and attributes. Therefore, we propose introducing a heterogeneous graph neural network to model entities and relations simultaneously and propose an iterative fusion method to enhance the interaction between these two semantic nodes. Since not all datasets contain relation information, this paper does not directly introduce a feature representation of relations. The generalizability of the approach is improved by utilizing a relation-aware strategy to obtain information about the relation. Specifically, the information propagation of the head and tail entities in the triplet is utilized to obtain the feature representation of the relation. Experimental results show that the present method performs better on three cross-lingual datasets DBP15K and two large-scale datasets DWY100K.

Modeling Tree-like Heterophily on Symmetric Matrix Manifolds.

Tree-like structures, characterized by hierarchical relationships and power-law distributions, are prevalent in a multitude of real-world networks, ranging from social networks to citation networks and protein-protein interaction networks. Recently, there has been significant interest in utilizing hyperbolic space to model these structures, owing to its capability to represent them with diminished distortions compared to flat Euclidean space. However, real-world networks often display a blend of flat, tree-like, and circular substructures, resulting in heterophily. To address this diversity of substructures, this study aims to investigate the reconstruction of graph neural networks on the symmetric manifold, which offers a comprehensive geometric space for more effective modeling of tree-like heterophily. To achieve this objective, we propose a graph convolutional neural network operating on the symmetric positive-definite matrix manifold, leveraging Riemannian metrics to facilitate the scheme of information propagation. Extensive experiments conducted on semi-supervised node classification tasks validate the superiority of the proposed approach, demonstrating that it outperforms comparative models based on Euclidean and hyperbolic geometries.

The impact of the marginal utility behavior on single-layer networks with limited contact

The information propagation on the social network has been an important research topic, with a focus on the significant influence of individual behaviors. The marginal utility behavior can affect the process of information propagation. But most previous research ignore it. Besides, the process can also be influenced by limited-contact capacity, which increase the complexity of networks. In this paper, the marginal utility behavior model on the single-layer network with limited-contact capacity is proposed first. Then the edge-based compartmental (EBC) method is used to explore the novel information propagation mechanism. Through experiments, it was found that when individuals show an increasing marginal utility behavior, with the propagation probability increasing, the final spreading scope shows a discontinuous increase pattern by weakening behavior. However, the final spreading scope shows no outbreak by strengthening behavior. In contrast, when individuals show a diminishing marginal utility behavior, with the propagation probability increasing, the final spreading scope shows a continuous increase pattern by strengthening. Nevertheless, the final spreading scope shows a discontinuous increasing by weakening. What’s more, the limited-contact capacity and the degree distribution heterogeneity can also change the information propagation pattern. Besides, the experimental results are in agreement with the theoretical results.