Information In Complex Networks Research Articles

Distributed Nash equilibrium seeking in noncooperative game with partial decision information of neighbors.

In the real world, individuals may conceal some of their real decision information to their neighbors due to competition. It is a challenge to explore the distributed Nash equilibrium when individuals play the noncooperative game with partial decision information in complex networks. In this paper, we investigate the distributed Nash equilibrium seeking problem with partial decision information of neighbors. Specifically, we construct a two-layer network model, where players in the first layer engage in game interactions and players in the second layer exchange estimations of real actions with each other. We also consider the case where the actions of some players remain unchanged due to the cost of updating or personal reluctance. By means of the Lyapunov function method and LaSalle's invariance principle, we obtain the sufficient conditions in which the consensus of individual actions and estimations can be achieved and the population actions can converge to the Nash equilibrium point. Furthermore, we investigate the case with switched topologies and derive the sufficient conditions for the convergence of individual actions to Nash equilibrium by the average dwell time method. Finally, we give numerical examples for cases of fixed and switched topologies to verify our theoretical results.

A comprehensive survey on community detection methods and applications in complex information networks

A comprehensive survey on community detection methods and applications in complex information networks

Biodiversity and the Community Structure of Chromista Cavalier-Smith, 1981 in Nyong and Kienke River Mouths (South-Cameroon)

A survey was undertaken from March to June 2014 on the biodiversity and the community structure of Chromista Cavalier-Smith, 1981 in Nyong and Kienke River mouths (South-Cameroon). In each river, raw waters were collected from upstream to downstream at four sites. Cells were counted using the Malassez cells procedure and species were identified. A total of 10427.1x10<sup>5</sup> cells corresponded to three phyla, eight classes, 23 orders, 32 genera and 40 species (24 freshwater species (60.0% of total species richness and total collection respectively), three marine species (7.5% and 2.4% of the total species richness; and total collection respectively), and one brackish water specialist in Kienke (2.5% and 5.1%), 13 tolerant species (32.5% and 32.6%)). The trophic diatom index revealed undisturbed conditions with no or little alteration of human origin and a low organic pollution (oligotrophic or mesotrophic state) (Nyong: TDI=52.7; Kienke: TDI=69.7; pooled assemblage: TDI=65.0). A low species richness was detected (richness ratio in Nyong: d=0.008; Kienke: d=0.003; pooled rivers: d=0.004), a high species diversity (Shannon index close to maximum) (Nyong: H’=2.742 and H’<sub>max</sub>=2.996; Kienke: H’=2.685 and H’<sub>max</sub>=2.996; pooled rivers: H’=3.245 and H’<sub>max</sub>=3.689), a very low dominance by a few species (Berger-Parker index close to 0) (Nyong: I<SUB>BP</SUB>=0.156; Kienke: I<SUB>BP</SUB>=0.175; pooled rivers: I<SUB>BP</SUB>=0.134), and Hill’s ratio were close to 1 (Nyong: Hill=0.819; Kienke: Hill=0.803; pooled rivers: Hill=0.722). The community was highly even with a high value of the Pielou’s evenness close to 1 (Nyong: J=0.915; Kienke: J=0.896; pooled rivers: J=0.880). Two useful species and one harmful species to fish were rare in Kienke. Species exhibited in Kienke and pooled data in rainy season, a positive global net association while it was negative in Nyong. Assemblage fitted Preston’s model in Nyong with a high environmental constant in the dry season (m’=1.469), low constant in the rainy season (m’=0.947) and the pooled seasons (m’=0.853). In Kienke constants were low (dry season: m’=0.574; rainy season: m’=0.566; pooled seasons: m’=0.581) suggesting a evolved community in less disturbed environments where the majority of species showed moderate abundances. In the dry season, the pooled assemblage functionned on the basis of maintaining a complex information network (close to ecological balance) developed at spatio-temporal scales (ZM model) and it presented a low force of regeneration (fractal dimension of the distribution of individuals among species (1/γ)=0.925<1). The evolved oligotrophic state (close to natural balance) of the chromists’ community should be preserved and protected and the studied rivers classified as reference.

Finding the key nodes to minimize the victims of the malicious information in complex network

Safeguarding crucial nodes provides a direct approach to impede the dissemination of malicious information in complex networks, such as the Internet. However, determining the optimal budget size, represented as k, for protecting nodes is a challenging problem classified as NP-hard. In this study, we investigate the origin of the NP-hard property, known as the influence redundancy mechanism, as a means to address this problem. The influence redundancy characterizes the intricate interactions among key nodes. Subsequently, we introduce an objective function that allows for the optimization of the set of key nodes. Our objective is to minimize the spectral radius of the adjacency matrix after removing these key nodes. We mathematically prove that the objective function exhibits the submodular property, and our proposed method achieves an approximation ratio of (1−1/e) with a time complexity of O(NlogN), where N represents the size of the network. Experimental results show that the identified key nodes outperform classical methods in 20 empirical networks, specifically in the Susceptible-Infected-Recovered (SIR) model and Independent Cascade (IC) model, thus confirming their improved performance quality.

Functional connectivity characteristics of the brain network involved in prickle perception of single fiber stimulation.

The complex network connections, information transmission and organization play key roles in brain cognition on sensory stimulation. Previous studies showed that several brain regions of somatosensory, motor, emotional, cognitive, etc. are linked to fabric-evoked prickle. But the functional connectivity characteristics of the brain network involved in prickle perception is still unclear. In the present study, resting state fMRI (functional magnetic resonance imaging) with functional connectivity analysis was adopted to build the initial brain functional network, and task fMRI with psychophysiological interaction analysis was employed to investigate modulation features of prickling task to functional connections in the brain network. The results showed that, in resting state, six groups or sub-networks can be identified in the prickle network, and when the subjects performed the prickling task, functional connectivity strength between some seed regions (e.g., somatosensory regions and precuneus, emotional regions and the prefrontal cortex, etc.) in the network increased. Combining resting-state fMRI with task fMRI is a feasible and promising method to study functional connectivity characteristics of the brain network involved in prickle perception. It is inferred that the "itch" ingredient of prickle sensation was transmitted from somatosensory cortices to precuneus, and emotional attribute (e.g., pain) from somatosensory cortices to the prefrontal cortex and at last to emotional regions.

A computational method for small molecule-RNA binding sites identification by utilizing position specificity and complex network information

Some computational methods have been given for small molecule-RNA binding site identification due to that it plays a significant role in revealing biology function researches. However, it is still challenging to design an accurate model, especially for MCC. We designed a feature extraction technology from two aspects (position specificity and complex network information). Specifically, complex network was employed to express the space topological structure and sequence position information for improving prediction effect. Then, the features fused position specificity and complex network information were input into random forest classifier for model construction. The AUC of 88.22%, 77.92% and 81.46% were obtained on three independent datasets (RB19, CS71, RB78). Compared with the existing method, the best MCC were obtained on three datasets, which were 8.19%, 0.59% and 4.35% higher than the state-of-the-art prediction methods, respectively. The outstanding performances show that our method is a powerful tool to identify RNA binding sites, helping to the design RNA-targeting small molecule drugs. The data and resource codes are available at https://github.com/Kangxiaoneuq/PCN_RNAsite.

A novel fusion technology utilizing complex network and sequence information for FAD-binding site identification

Flavin adenine dinucleotide (FAD) binding sites play an increasingly important role as useful targets for inhibiting bacterial infections. To reveal protein topological structural information as a reasonable complement for the identification FAD-binding sites, we designed a novel fusion technology according to sequence and complex network. The specially designed feature vectors were combined and fed into CatBoost for model construction. Moreover, due to the minority class (positive samples) is more significant for biological researches, a random under-sampling technique was applied to solve the imbalance. Compared with the previous methods, our methods achieved the best results for two independent test datasets. Especially, the MCC obtained by FADsite and FADsite_seq were 14.37 %–53.37 % and 21.81 %–60.81 % higher than the results of existing methods on Test6; and they showed improvements ranging from 6.03 % to 21.96 % and 19.77 %–35.70 % on Test4. Meanwhile, statistical tests show that our methods significantly differ from the state-of-the-art methods and the cross-entropy loss shows that our methods have high certainty. The excellent results demonstrated the effectiveness of using sequence and complex network information in identifying FAD-binding sites. It may be complementary to other biological studies. The data and resource codes are available at https://github.com/Kangxiaoneuq/FADsite.

A local rewiring strategy for accelerating information propagation under complex propagation mechanism

Controlling the spread of information or rumors in complex networks has always been a subject of extensive attention and research. Generally speaking, taking the most influential node in the network as the propagation spreader or cutting off the most important link in the propagation process can maximize the promotion or suppression of information transmission. Therefore, many scholars have put forward many measurement methods to identify specific nodes and links in the network, and their theories are becoming more and more mature. However, in the face of large-scale networks, most of these methods are exposed to high computational complexity, low accuracy, and only applicable to specific networks. Some scholars try to enhance (inhibit) the dissemination of information by adding or deleting links, but the structural characteristics of the original network will be changed to a large extent, which also means that some basic functions related to the network have been changed. In this work, we propose a rewiring strategy to accelerate information propagation under complex propagation mechanism, which increases the number of redundant links by rewiring the second-order neighborhood of the initial propagation node. At the same time, the rewiring strategy preserves the degree distribution of nodes and modularization feature of the original network to a large extent. We apply our strategy to simulate the complex propagation of the information propagation (IST(I)) model on different networks. The simulation results show that the rewired network can accelerate the propagation of information more effectively, and the number of spreaders in the network increases significantly, and the propagation rate of information is enhanced significantly. In addition, this paper also proposed for the first time an index that quantifies both the transmission ability and infectivity of the information at the micro level, namely the number of regenerated infections which was optimally estimated by statistical inference. We also compare the changes of various topological characteristics of the network before and after the rewiring. We find that the structural characteristics related to the propagation dynamics of the network after the rewiring are significantly enhanced.

Smart Contact Lenses as Wearable Ophthalmic Devices for Disease Monitoring and Health Management.

The eye contains a complex network of physiological information and biomarkers for monitoring disease and managing health, and ocular devices can be used to effectively perform point-of-care diagnosis and disease management. This comprehensive review describes the target biomarkers and various diseases, including ophthalmic diseases, metabolic diseases, and neurological diseases, based on the physiological and anatomical background of the eye. This review also includes the recent technologies utilized in eye-wearable medical devices and the latest trends in wearable ophthalmic devices, specifically smart contact lenses for the purpose of disease management. After introducing other ocular devices such as the retinal prosthesis, we further discuss the current challenges and potential possibilities of smart contact lenses.

A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization.

Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, the limited computing power and storage space of IoT devices restrict the feasibility of deploying resource-intensive intrusion detection systems on them. This article introduces the DL-BiLSTM lightweight IoT intrusion detection model. By combining deep neural networks (DNNs) and bidirectional long short-term memory networks (BiLSTMs), the model enables nonlinear and bidirectional long-distance feature extraction of complex network information. This capability allows the system to capture complex patterns and behaviors related to cyber-attacks, thus enhancing detection performance. To address the resource constraints of IoT devices, the model utilizes the incremental principal component analysis (IPCA) algorithm for feature dimensionality reduction. Additionally, dynamic quantization is employed to trim the specified cell structure of the model, thereby reducing the computational burden on IoT devices while preserving accurate detection capability. The experimental results on the benchmark datasets CIC IDS2017, N-BaIoT, and CICIoT2023 demonstrate that DL-BiLSTM surpasses traditional deep learning models and cutting-edge detection techniques in terms of detection performance, while maintaining a lower model complexity.

Attributed Graph Embedding with Random Walk Regularization and Centrality-Based Attention

Graph-embedding learning is the foundation of complex information network analysis, aiming to represent nodes in a graph network as low-dimensional dense real-valued vectors for the application in practical analysis tasks. In recent years, the study of graph network representation learning has received increasing attention from researchers, and, among them, graph neural networks (GNNs) based on deep learning are playing an increasingly important role in this field. However, the fact that higher-order neighborhood information cannot be used effectively is a problem of most existing graph neural networks. Moreover, it tends to ignore the influence of latent representation and structural properties on graph embedding. In hopes of solving these issues, we introduce centrality encoding to learn the node properties, add an attention mechanism consideration to better distinguish the significance of neighboring nodes, and introduce random walk regularization to make sample neighbors that consistently satisfy predetermined criteria. This allows us to learn a representation of a potential node. We tested the performance of our model on node-clustering and link prediction tasks using three widely recognized benchmark datasets. The outcomes of our experiments demonstrate that our model significantly surpasses the baseline method in both tasks, indicating that the graph embedding it generates is highly expressive.

Measuring Academic Representative Papers Based on Graph Autoencoder Framework

Objectively evaluating representative papers in a specific scientific research field is of great significance to the development of academia and scientific research institutions. Representative papers on achievements in scientific research can reflect the academic level and research characteristics of researchers and research institutions. The existing research methods are mainly based on external feature indicators and citation analysis methods, and the method of combining artificial intelligence is in its infancy. From the perspective of scientific research institutions, this paper proposes a graph autoencoder framework based on heterogeneous networks for the measurement of paper impact, named GAEPIM. Specifically, we propose two versions of GAEPIM based on a graph convolutional network and graph transformer network. The models rank papers in a specific research field and find the most representative papers and their scientific institutions. The proposed framework constructs a heterogeneous network of papers, institutions, and venues and simultaneously analyzes the semantic information of papers and the heterogeneous network structural information. Finally, based on the complex network information diffusion model, the proposed method performs better than several widely used baseline methods.

Forecasting Networks Links with Laplace Characteristic and Geographical Information in Complex Networks

Forecasting links in a network is a crucial task in various applications such as social networks, internet traffic management, and data mining. Many studies on forecasting links in social networks and on other networks have been conducted over the last decade. In this paper, we propose a novel method based on graph Laplacian eigenmaps for predicting the geographic location of nodes in complex networks. Our method utilizes the adjacency matrix of the network and generates a scoring matrix that captures the similarity between nodes in terms of their geographic location. By transforming the distance matrices into score matrices using exponential decay, we show that the method achieves consistently high performance across various real-world datasets, surpassing other state-of-the-art methods. Our experiments on real-world networks demonstrate that The LCG method proposed in this study exhibits consistently high performance across most of the evaluated datasets, with an average score of 0.95%, surpassing the other methods.

Hypernetwork Link Prediction Method Based on Fusion of Topology and Attribute Features

Link prediction aims at predicting missing or potential links based on the known information of complex networks. Most existing methods focus on pairwise low-order relationships while ignoring the high-order interaction and the rich attribute information of entities in the actual network, leading to the low performance of the model in link prediction. To mine the cross-modality interactions between the high-order structure and attributes of the network, this paper proposes a hypernetwork link prediction method for fusion topology and attributes (TA-HLP). Firstly, a dual channel coder is employed for jointly learning the structural features and attribute features of nodes. In structural encoding, a node-level attention mechanism is designed to aggregate neighbor information to learn structural patterns effectively. In attribute encoding, the hypergraph is used to refine the attribute features. The high-order relationship between nodes and attributes is modeled based on the node-attribute-node feature update, which preserves the semantic information jointly reflected by nodes and attributes. Moreover, in the joint embedding, a hyperedge-level attention mechanism is introduced to capture nodes with different importance in the hyperedge. Extensive experiments on six data sets demonstrate that this method has achieved a more significant link prediction effect than the existing methods.

Retraction Note: Intelligent analysis framework for healthy environment spatial model of BIM horticultural therapy based on complex network information model

Retraction Note: Intelligent analysis framework for healthy environment spatial model of BIM horticultural therapy based on complex network information model

Volatile Chemical Variation of Essential Oils and Their Correlation with Insects, Phenology, Ontogeny and Microclimate: Piper mollicomum Kunth, a Case of Study.

The aim of this study was to monitor the volatile chemical composition from leaves and reproductive organs of Piper mollicomum Kunth (PM), in its reproduction period, as well as register inflorescence visitors, microclimate and phenological information. The essential oils (EOs) obtained from the different fresh organs by hydrodistillation were identified and quantified by Gas Chromatography/Mass Spectrometry (GC/MS) and by GC coupled to a Flame Ionization Detector (GC/FID), respectively. The cercentage content of some volatiles present in reproductive organs, such as limonene, 1,8-cineole, linalool and eupatoriochromene, increased during the maturation period of the inflorescences, and decreased during the fruiting period, suggesting a defense/attraction activities. Furtermore, a biosynthetic dichotomy between 1,8-cineole (leaves) and linalool (reproductive organs) was recorded. A high frequency of bee visits was registered weekly, and some correlations showed a positive relationship between this variable and terpenes. Microclimate has an impact on this species' phenological cycles and insect visiting behavior. All correlations between volatiles, insects, phenology and microclimate allowed us to present important data about the complex information network in PM. These results are extremely relevant for the understanding of the mechanisms of chemical-ecological plant-insect interactions in Piperaceae, a basal angiosperm.

Analytical solution to the [formula omitted]-core pruning process

k-core decomposition is a widely-used method in ranking nodes or extracting important information of complex networks. It is a pruning process in which we recursively remove the vertices with degree less than k to obtain the core of a complex network. The simplicity and effectiveness of this approach has led to a variety of applications in many scientific fields, including bioinformatics, neurosciences, computer sciences, economics, and network sciences. However, the analytical theory of the k-core pruning process is still lacking. Here we find that in every pruning step of any given network, the Non-Backtracking Expansion Branch (NBEB) is directly related to the remaining k-core. Using this NBEB method, we obtain the analytical results of the k-core pruning process and its detailed critical behaviour.

Co-developing a tailored vaccination intervention with Congolese migrants: a participatory study

Abstract Background Disparities in vaccination uptake among migrant populations are well documented. WHO and ECDC have sought renewed focus on participatory research that engages migrants in co-producing tailored initiatives to address vaccination inequities and increase coverage. Methods This community-based participatory research study aims to engage Congolese migrants in co-developing a tailored approach to increase vaccine uptake. Phase 1 used poster walls and in-depth interviews with Congolese migrants (n = 32) to explore COVID-19 vaccination beliefs, experiences, and preferred information sources and communication methods, analysed iteratively and thematically in NVivo. Preliminary results Institutional distrust has shaped this population’s interpretation of the pandemic response and enabled vaccine misinformation and conspiracy theories to take hold. We found complex information networks and preference for Francophone, African and social media. Limited English proficiency and preference for the oral tradition restricted engagement with official public health messaging. Suspicion of government motives, low knowledge, and culturally specific perceptions about vaccination contributed to belief that breakthrough infections and need for COVID-19 boosters imply the vaccine is not effective. The population felt coerced by vaccination reminders and mandates, and were resultantly more hesitant to accept COVID-19 vaccination. Conclusions The population’s specific characteristics suggest that existing and trusted interpersonal networks and oral communication in first languages should be harnessed to spread credible information and encourage vaccine uptake, and mandate policies are unlikely to be effective. Training local role models to facilitate vaccination dialogues and myth-bust may be effective at changing behaviour. The next phases will gather more information from key stakeholders and engage migrants in workshops to co-design insight-driven, tailored interventions. Key messages • Global policy-setting organisations have called urgently for participatory research that engages migrants in the co-production of tailored initiatives to address vaccination inequalities. • Populations with strong interpersonal networks and low trust in public institutions may be receptive to tailored, community-centred dialogue approaches using local messengers and role models.

Bridge Node Detection between Communities Based on GNN

In a complex network, some nodes are relatively concentrated in topological structure, thus forming a relatively independent node group, which we call a community. Usually, there are multiple communities on a network, and these communities are interconnected and exchange information with each other. A node that plays an important role in the process of information exchange between communities is called an inter-community bridge node. Traditional methods of defining and detecting bridge nodes mostly quantify the bridging effect of nodes by collecting local structural information of nodes and defining index operations. However, on the one hand, it is often difficult to capture the deep topological information in complex networks based on a single indicator, resulting in inaccurate evaluation results; on the other hand, for networks without community structure, such methods may rely on community partitioning algorithms, which require significant computing power. In this paper, considering the multi-dimensional attributes and structural characteristics of nodes, a deep learning-based framework named BND is designed to quickly and accurately detect bridge nodes. Considering that the bridging function of nodes between communities is abstract and complex, and may be related to the multi-dimensional information of nodes, we construct an attribute graph on the basis of the original graph according to the features of the five dimensions of the node to meet our needs for extracting bridging-related attributes. In the deep learning model, we overlay graph neural network layers to process the input attribute graph and add fully connected layers to improve the final classification effect of the model. Graph neural network algorithms including GCN, GAT, and GraphSAGE are compatible with our proposed framework. To the best of our knowledge, our work is the first application of graph neural network techniques in the field of bridge node detection. Experiments show that our designed framework can effectively capture network topology information and accurately detect bridge nodes in the network. In the overall model effect evaluation results based on indicators such as Accuracy and F1 score, our proposed graph neural network model is generally better than baseline methods. In the best case, our model has an Accuracy of 0.9050 and an F1 score of 0.8728.

Research on spatial structure and resilience of complex urban network: A case study of Jing-Jin-Ji Urban Agglomeration

With the long-term impact of economic globalization, the accelerated cross-border flow of resource elements between cities is increasingly important for the development of urban regions. In this context, the central place theory, which emphasizes the vertical hierarchical relationship between cities, has obvious limitations in interpreting urban interaction. This paper takes the Beijing Tianjin Hebei Urban Agglomeration (including Beijing, Tianjin. and 11 prefecture-level cities in Hebei Province) as the research object, constructs three complex networks of economy, information, and economic information coupling connection, and analyzes the resilience of the network structure. The research results show that: 1) The spatial distribution of the three network structures of Jing-Jin-Ji Urban Agglomeration presents a spatial pattern with Beijing as the core and radiating outward. 2) In terms of weighted degree distribution, Beijing, as an arrow city, has strong competitiveness in the economy and information network, but the hierarchy of cities with balanced information economy development is low. 3) In terms of weighted degree correlation, among the three networks, the cities with a high weighted degree prefer to connect with cities with a low weighted degree, and the mismatch is ranked as information network > information economy coupling network > economic connection network. 4) In terms of network transmission, information network > economy connection network > information economy coupling network, and Beijing and Tianjin have a greater impact. The above results have strong guidance and practical significance for the formulation and adjustment of territorial spatial planning and regional optimization.