Information Of Neighbor Nodes Research Articles

High-frequency and low-frequency dual-channel graph attention network

Most existing graph convolution layers use learnable or fixed weights to sum up neighbor features to aggregate neighbor information. Since the attention values are always positive, these graph convolution layers perform as low-pass filters, which may result in their poor performance on heterophilic graphs. In this paper, two graph convolutional layers are proposed, HLGAT and NGAT. NGAT is a convolution network using only negative attention values, which only make the aggregation of high-frequency information of neighbor nodes. HLGAT makes the aggregation of low-frequency and high-frequency information by two channels, respectively, and fuses two outputs by using a learnable way. On node-classification task, both NGAT and HLGAT offer significant performance improvement compared to existing methods. The results clearly show that: (1) High-frequency information of neighborhoods plays a decisive role in heterophilic graphs. (2) The aggregation of low-frequency and high-frequency information of neighbor nodes can significantly improve the performance on heterophilic graphs.

Multi-scale graph clustering network

Deep graph clustering, a fundamental yet formidable task in data analysis, aims to partition samples belonging to the same category into their respective clusters. Recently, significant advancements in graph self-supervised learning have been made through generative and contrastive learning methods. However, existing methods focus on directly aggregating neighboring node information during the feature extraction stage, thereby neglecting the crucial long-range correlations between nodes. Consequently, non-neighbor node information within the same category remains unexplored, leading to subpar performance in the clustering task. To address this issue, we propose a generative method named Multi-scale Graph Clustering Network (MGCN) to learn comprehensive and rich graph representations for deep graph clustering in the feature encoding stage. Specifically, we design a Multi-hop Adaptive Convolutional Module (MACM) integrated into MGCN, which effectively aggregates high-order neighbor node features in each layer of the network. Additionally, we develop an autoencoder to assist MACM in enhancing attribute information, which prevents the node's own features from being overshadowed in the multi-scale feature learning process. Experimental results demonstrate that our proposed MGCN method achieves significantly better clustering performance than existing methods on multiple public datasets.

MINDG: a drug-target interaction prediction method based on an integrated learning algorithm.

Drug-target interaction (DTI) prediction refers to the prediction of whether a given drug molecule will bind to a specific target and thus exert a targeted therapeutic effect. Although intelligent computational approaches for drug target prediction have received much attention and made many advances, they are still a challenging task that requires further research. The main challenges are manifested as follows: (i) most graph neural network-based methods only consider the information of the first-order neighboring nodes (drug and target) in the graph, without learning deeper and richer structural features from the higher-order neighboring nodes. (ii) Existing methods do not consider both the sequence and structural features of drugs and targets, and each method is independent of each other, and cannot combine the advantages of sequence and structural features to improve the interactive learning effect. To address the above challenges, a Multi-view Integrated learning Network that integrates Deep learning and Graph Learning (MINDG) is proposed in this study, which consists of the following parts: (i) a mixed deep network is used to extract sequence features of drugs and targets, (ii) a higher-order graph attention convolutional network is proposed to better extract and capture structural features, and (iii) a multi-view adaptive integrated decision module is used to improve and complement the initial prediction results of the above two networks to enhance the prediction performance. We evaluate MINDG on two dataset and show it improved DTI prediction performance compared to state-of-the-art baselines. https://github.com/jnuaipr/MINDG.

CR-TransR: A Knowledge Graph Embedding Model for Cultural Domain

As a combination of information computing technology and the cultural field, cultural computing is gaining more attention. The knowledge graph is also gradually applied as a particular data structure in the cultural area. Based on the domain knowledge graph data of the Beijing Municipal Social Science Project “Mining and Utilization of Cultural Resources in the Ancient Capital of Beijing,” this article proposes a graph representation learning model CR-TransR that integrates cultural attributes. Through the analysis of the data in the cultural field of the ancient capital of Beijing, a cultural feature dictionary is constructed, and a domain-specific feature matrix is constructed in the form of word vector splicing. The feature matrix is used to constrain the embedding graph model TransR, and then the feature matrix and the TransR model are jointly trained to complete the embedded expression of the knowledge graph. Finally, a comparative experiment is carried out on the Beijing ancient capital cultural knowledge graph dataset and the effects of the classic graph embedding algorithms TransE, TransH, and TransR. At the same time, we try to reproduce the embedding method with the core idea of neighbor node information aggregation as the core idea, and CRTransR are compared. The experimental tasks include link prediction and triplet classification, and the experimental results show that the CRTransR model performs better.

Multiagent Reinforcement Learning With Graphical Mutual Information Maximization.

Communication learning is an important research direction in the multiagent reinforcement learning (MARL) domain. Graph neural networks (GNNs) can aggregate the information of neighbor nodes for representation learning. In recent years, several MARL methods leverage GNN to model information interactions between agents to coordinate actions and complete cooperative tasks. However, simply aggregating the information of neighboring agents through GNNs may not extract enough useful information, and the topological relationship information is ignored. To tackle this difficulty, we investigate how to efficiently extract and utilize the rich information of neighbor agents as much as possible in the graph structure, so as to obtain high-quality expressive feature representation to complete the cooperation task. To this end, we present a novel GNN-based MARL method with graphical mutual information (MI) maximization to maximize the correlation between input feature information of neighbor agents and output high-level hidden feature representations. The proposed method extends the traditional idea of MI optimization from graph domain to multiagent system, in which the MI is measured from two aspects: agent features information and agent topological relationships. The proposed method is agnostic to specific MARL methods and can be flexibly integrated with various value function decomposition methods. Considerable experiments on various benchmarks demonstrate that the performance of our proposed method is superior to the existing MARL methods.

Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism

Heterogeneous information networks (HINs) contain a rich network structure and semantic information, which makes them commonly used in recommendation systems. However, most of the existing HIN-based recommendation systems rely on meta-paths for information extraction, lack meta-path information supplements, and rarely learn complex structure information in heterogeneous graphs. To address these issues, we develop a novel recommendation algorithm that integrates the attention mechanism, meta-paths, and neighbor node information (AMNRec). In the heterogeneous information network, the missing information of the meta-path is supplemented by extracting the information of users and items’ neighbor nodes. The rich interactions between nodes are captured through convolution, and the embedded representation of nodes and meta-paths is obtained through the attention mechanism. TOP-N recommendation is completed by combining users, items, neighbor nodes, and meta-paths. Experiments on three public datasets show that AMNRec not only has the best recommendation performance but also has good interpretability of the recommendation results compared with the six recommendation benchmark algorithms.

A New Method of Fuzzy Multicriteria Routing in Vehicle Ad Hoc Network

The internet of vehicles (IoVs) provides delay-sensitive services, but high-latency communication with roadside units causes service failures and high costs. Mobile edge computing (EC) is migrating cloud computing platforms from the core network to the edge of mobile networks, giving vehicles local access to numerous computing resources. The classic greedy boundary stateless routing (GPSR) method is widely used to meet the communication requirements of vehicle self-organizing networks. In order to solve the problems of GPSR&#x2019;s neighbor node position acquisition lag and single routing criterion, a new routing method based on the fuzzy logic system Geographic Routing method based on Velocity, Angle, and Density (GRVAD) is proposed. This method gets the relative velocity between the nodes and the angle between the current node, the neighbor node and the target node, and the node density of the neighbor node as the input of fuzzy logic, and the unscented Kalman filter is used to predict the location of the neighbor node to obtain more accurate location information of neighbor nodes. Simulation results show that the routing method compensates for some of the shortcomings of the GPSR method and reasonably considers the delivery rate and end-to-end delay of data packets and is more in line with the communication requirements of the vehicle <i>ad hoc</i> network.

Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network.

Using information technology to extract emergency decision-making knowledge from emergency plan documents is an essential means to enhance the efficiency and capacity of emergency management. To address the problems of numerous terminologies and complex relationships faced by emergency knowledge extraction of water diversion project, a multi-feature graph convolutional network (PTM-MFGCN) based on pre-trained model is proposed. Initially, through the utilization of random masking of domain-specific terminologies during pre-training, the model's comprehension of the meaning and application of such terminologies within specific fields is enhanced, thereby augmenting the network's proficiency in extracting professional terminologies. Furthermore, by introducing a multi-feature adjacency matrix to capture a broader range of neighboring node information, thereby enhancing the network's ability to handle complex relationships. Lastly, we utilize the PTM-MFGCN to achieve the extraction of emergency entity relationships in water diversion project, thus constructing a knowledge graph for water diversion emergency management. The experimental results demonstrate that PTM-MFGCN exhibits improvements of 2.84% in accuracy, 4.87% in recall, and 5.18% in F1 score, compared to the baseline model. Relevant studies can effectively enhance the efficiency and capability of emergency management, mitigating the impact of unforeseen events on engineering safety.

A distributed fault estimation method for a class of nonlinear Markov jump systems

This paper investigates the distributed fault estimation problem of a class of nonlinear Markov jump systems. The main goal is to design a distributed observer network to estimate system states and actuator fault simultaneously. For the two cases of completely known and general uncertain transition rates, we first use a virtual intermediate observer technique to remove the observer matching condition, which upgrades the flexibility in the process of design. Next, by using the information of some special neighboring nodes and adopting observability decomposition approach, a novel distributed observer network allowed to be with unobservable nodes is constructed. Then, the mentioned goal is achieved based on the observer network. The proposed method can compensate missing information of unobservable nodes, which solves the estimation problem in the case of partial information loss. Finally, an example is provided to demonstrate the effectiveness of the proposed method.

CAGM: A communicability-based adaptive gravity model for influential nodes identification in complex networks

Identifying influential nodes is a very hot and challenging issue in the field of complex system and network. A great deal of algorithms have been developed to address the influential nodes identification problem, but most of previous studies are compromising between result accuracy and time cost. In this work, we propose a communicability-based adaptive gravity model (CAGM) for influential nodes identification. The key idea of CAGM algorithm is that the importance of each node is evaluated by comprehensively considering the influence probability and influence intensity information of neighbor nodes located in its influence radius. More specifically, the communicability network matrix is introduced to depict the influence probability between each pair of nodes. By integrating k-shell, degree and distance information, the influence radius of each node can be determined uniquely so as to portray the inherent heterogeneity of nodes in complex networks. To verify the effectiveness and applicability of CAGM, several groups of simulated experiments on twelve real and artificial datasets are concluded. Experimental results show that CAGM performs better than eight popular algorithms in terms of top-10 nodes, discrimination ability, imprecision function and ranking accuracy.

AttG-BDGNets: Attention-Guided Bidirectional Dynamic Graph IndRNN for Non-Intrusive Load Monitoring

Most current non-intrusive load monitoring methods focus on traditional load characteristic analysis and algorithm optimization, lack knowledge of users’ electricity consumption behavior habits, and have poor accuracy. We propose a novel attention-guided bidirectional dynamic graph IndRNN approach. The method first extends sequence or multidimensional data to a topological graph structure. It effectively utilizes the global context by following an adaptive graph topology derived from each set of data content. Then, the bidirectional Graph IndRNN network (Graph IndRNN) encodes the aggregated signals into different graph nodes, which use node information transfer and aggregation based on the entropy measure, power attribute characteristics, and the time-related structural characteristics of the corresponding device signals. The function dynamically incorporates local and global contextual interactions from positive and negative directions to learn the neighboring node information for non-intrusive load decomposition. In addition, using the sequential attention mechanism as a guide while eliminating redundant information facilitates flexible reasoning and establishes good vertex relationships. Finally, we conducted experimental evaluations on multiple open source data, proving that the method has good robustness and accuracy.

KGANSynergy: knowledge graph attention network for drug synergy prediction.

Combination therapy is widely used to treat complex diseases, particularly in patients who respond poorly to monotherapy. For example, compared with the use of a single drug, drug combinations can reduce drug resistance and improve the efficacy of cancer treatment. Thus, it is vital for researchers and society to help develop effective combination therapies through clinical trials. However, high-throughput synergistic drug combination screening remains challenging and expensive in the large combinational space, where an array of compounds are used. To solve this problem, various computational approaches have been proposed to effectively identify drug combinations by utilizing drug-related biomedical information. In this study, considering the implications of various types of neighbor information of drug entities, we propose a novel end-to-end Knowledge Graph Attention Network to predict drug synergy (KGANSynergy), which utilizes neighbor information of known drugs/cell lines effectively. KGANSynergy uses knowledge graph (KG) hierarchical propagation to find multi-source neighbor nodes for drugs and cell lines. The knowledge graph attention network is designed to distinguish the importance of neighbors in a KG through a multi-attention mechanism and then aggregate the entity's neighbor node information to enrich the entity. Finally, the learned drug and cell line embeddings can be utilized to predict the synergy of drug combinations. Experiments demonstrated that our method outperformed several other competing methods, indicating that our method is effective in identifying drug combinations.

A Parallel Fusion Graph Convolutional Network for Aspect-Level Sentiment Analysis

Sentiment analysis has always been an important basic task in the NLP field. Recently, graph convolutional networks (GCNs) have been widely used in aspect-level sentiment analysis. Because GCNs have good aggregation effects, every node can contain neighboring node information. However, in previous studies, most models used only a single GCN to learn contextual information. The GCN relies on the construction method of the graph, and a single GCN will cause the model to focus on a certain relationship of nodes that depends on the construction method and ignore other information. In addition, when the GCN aggregates node information, it cannot determine whether the aggregated information is useful, so it will inevitably introduce noise. We propose a model that fuses two parallel GCNs to learn different relational features between sentences at the same time, and we add a gate mechanism to the GCN to filter the noise introduced by the GCN when aggregating information. Finally, we validate our model on public datasets, and the experiments show that compared to state-of-the-art models, our model performs better.

CLIP-Based Adaptive Graph Attention Network for Large-Scale Unsupervised Multi-Modal Hashing Retrieval

With the proliferation of multi-modal data generated by various sensors, unsupervised multi-modal hashing retrieval has been extensively studied due to its advantages in storage, retrieval efficiency, and label independence. However, there are still two obstacles to existing unsupervised methods: (1) As existing methods cannot fully capture the complementary and co-occurrence information of multi-modal data, existing methods suffer from inaccurate similarity measures. (2) Existing methods suffer from unbalanced multi-modal learning and data semantic structure being corrupted in the process of hash codes binarization. To address these obstacles, we devise an effective CLIP-based Adaptive Graph Attention Network (CAGAN) for large-scale unsupervised multi-modal hashing retrieval. Firstly, we use the multi-modal model CLIP to extract fine-grained semantic features, mine similar information from different perspectives of multi-modal data and perform similarity fusion and enhancement. In addition, this paper proposes an adaptive graph attention network to assist the learning of hash codes, which uses an attention mechanism to learn adaptive graph similarity across modalities. It further aggregates the intrinsic neighborhood information of neighboring data nodes through a graph convolutional network to generate more discriminative hash codes. Finally, this paper employs an iterative approximate optimization strategy to mitigate the information loss in the binarization process. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms several representative hashing methods in unsupervised multi-modal retrieval tasks.

CDA-SKAG: Predicting circRNA-disease associations using similarity kernel fusion and an attention-enhancing graph autoencoder.

Circular RNAs (circRNAs) constitute a category of circular non-coding RNA molecules whose abnormal expression is closely associated with the development of diseases. As biological data become abundant, a lot of computational prediction models have been used for circRNA-disease association prediction. However, existing prediction models ignore the non-linear information of circRNAs and diseases when fusing multi-source similarities. In addition, these models fail to take full advantage of the vital feature information of high-similarity neighbor nodes when extracting features of circRNAs or diseases. In this paper, we propose a deep learning model, CDA-SKAG, which introduces a similarity kernel fusion algorithm to integrate multi-source similarity matrices to capture the non-linear information of circRNAs or diseases, and construct a circRNA information space and a disease information space. The model embeds an attention-enhancing layer in the graph autoencoder to enhance the associations between nodes with higher similarity. A cost-sensitive neural network is introduced to address the problem of positive and negative sample imbalance, consequently improving our model's generalization capability. The experimental results show that the prediction performance of our model CDA-SKAG outperformed existing circRNA-disease association prediction models. The results of the case studies on lung and cervical cancer suggest that CDA-SKAG can be utilized as an effective tool to assist in predicting circRNA-disease associations.

IcaGCN: Model Intents via Coactivated Graph Convolution Network for Recommendation

In this era of information overload, to better provide personalized content services to users, recommendation systems have greatly improved the efficiency of information distribution. Graph Convolution Network(GCN), which is one of the representative works of graph structure aggregation processing, works by node convolution with the help of the Laplacian matrix of the graph and weighted combination of neighbor node information according to the outgoing and incoming degrees of neighbor nodes to obtain the representation of the current node. However, the mainstream GCN models nowadays do not take into account data augmentation of metadata and the fact that each node plays different roles with different importance and weights, thus making the recommendation performance limited. To better solve the above problems, we propose the IcaGCN model, which can perform data augmentation and calculate node weights in modules, and is a convenient plug-and-play method. Finally, extensive experimental results on four real-world datasets have shown the effectiveness and robustness of the proposed model. Especially on the Amazon-Book dataset, our IcaGCN has improved by 6.32%, 42.29%, and 12.38% in Recall@20, MRR@20, and NDCG@20, respectively, compared to other existing state-of-the-art models. We also provide source code and data to reproduce the experimental results <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>a</i></sup> .

Human migration-based graph convolutional network for PM2.5 forecasting in post-COVID-19 pandemic age.

Due to the coronavirus disease 2019 pandemic, local authorities always implanted non-pharmaceutical interventions, such as maintaining social distance to reduce human migration. Besides, previous studies have proved that human migration highly influenced air pollution concentration in an area. Therefore, this study aims to explore whether human migration can work as a significant factor in the post-pandemic age to help PM2.5 concentration forecasting. In this work, we first analyze the variations of PM2.5 in 11 cities of Hubei from 2015 to 2020 and further compare PM2.5 trends with the migration trends of Hubei province in 2020. Experimental results indicate that the human migration indirectly affected the urban PM2.5 concentration. Then, we established a graph data structure based on the migration network describing the migration flow size between any two areas in the Hubei province and proposed a migration attentive graph convolutional network (MAGCN) for forecasting PM2.5. Combined with the migration data. The proposed model can attentively aggregate the information of neighbor nodes through migration weights. Experimental results indicate that the proposed MAGCN can forecast PM2.5 concentration accurately.

Edge-Aware Graph Neural Network for Multi-Hop Path Reasoning over Knowledge Base.

Multi-hop path reasoning over knowledge base aims at finding answer entities for an input question by walking along a path of triples from graph structure data, which is a crucial branch in the knowledge base question answering (KBQA) research field. Previous studies rely on deep neural networks to simulate the way humans solve multi-hop questions, which do not consider the latent relation information contained in connected edges, and lack of measuring the correlation between specific relations and the input question. To address these challenges, we propose an edge-aware graph neural network for multi-hop path reasoning task. First, a query node is directly added to the candidate subgraph retrieved from the knowledge base, which constructs what we term a query graph. This graph construction strategy makes it possible to enhance the information flow between the question and the nodes for the subsequent message passing steps. Second, question-related information contained in the relations is added to the entity node representations during graph updating; meanwhile, the relation representations are updated. Finally, the attention mechanism is used to weight the contribution from neighbor nodes so that only the information of neighbor nodes related to the query can be injected into new node representations. Experimental results on MetaQA and PathQuestion-Large (PQL) benchmarks demonstrate that the proposed model achieves higher Hit@1 and F1 scores than the baseline methods by a large margin. Moreover, ablation studies show that both the graph construction and the graph update algorithm contribute to performance improvement.

An Entity Linking Algorithm Derived from Graph Convolutional Network and Contextualized Semantic Relevance

In the era of big data, a large amount of unstructured text data springs up every day. Entity linking involves relating the mentions found in the texts to the corresponding entities, which stand for objective things in the real world, in a knowledge base. This task can help computers understand semantics in the texts correctly. Although there have been numerous approaches employed in research such as this, some challenges are still unresolved. Most current approaches utilize neural models to learn important features of the entity and mention context. However, the topic coherence among the referred entities is frequently ignored, which leads to a clear preference for popular entities but poor accuracy for less popular ones. Moreover, the graph-based models face much noise information and high computational complexity. To solve the problems above, the paper puts forward an entity linking algorithm derived from the asymmetric graph convolutional network and the contextualized semantic relevance, which can make full use of the neighboring node information as well as deal with unnecessary noise in the graph. The semantic vector of the candidate entity is obtained by continuously iterating and aggregating the information from neighboring nodes. The contextualized relevance model is a symmetrical structure that is designed to realize the deep semantic measurement between the mentions and the entities. The experimental results show that the proposed algorithm can fully explore the topology information of the graph and dramatically improve the effect of entity linking compared with the baselines.

Fan-Shaped Flooding in Wireless Sensor Networks

In a wireless sensor network, data flows in two main directions. There are flooding that transfers data from the sink node to the entire node and routing that transfers data sensed by each sensor node to the sink node. Transferring data from the sink node of the wireless sensor network to the entire sensor node is called flooding. In an energy-constrained environment, a more efficient method has been developed, because the most basic flooding technique contains a lot of data redundancy. In this paper, the combination of the distance-based approach and the neighboring node information method is proposed as a more energy-efficient method. Flood data can be transmitted by adjusting the angle of the transmission line within the transmission radius to the shape of a fan and limiting the distance within the communication radius. The redundancy and connectivity of data were compared and examined according to angle and distance values, and the experiment proved that the proposed method can provide connectivity to the entire sensor node while reducing data redundancy. It was confirmed that the optimal angle and distance could change according to the density and communication radius of the network. Additional research will be needed, such as a method that can dynamically calculate the optimal angle and distance while taking into account factors like network density and communication radius.