Complex Network Graph Research Articles

Study of critical deviation transfer for assembly of complex structural parts of aircraft

In current assembly process of the aircraft complex structural parts, there are problems such as nonlinear, multilevel strong coupling and large uncertainty transfer relationship between assembly quality and its deviation sources. In this paper, an assembly deviation transfer method based on transfer entropy network graph is proposed to address the challenges. Firstly, the source of assembly deviation is analysed by constructing a multi-level assembly coordination network with assembly deviation detection data. Secondly, an assembly deviation propagation model is established by combining transfer entropy and complex network graph theory. This model provides insights into the topological relationship of assembly deviations and introduces causal metrics for evaluation. The effectiveness of method is demonstrated through its application in the assembly of an aircraft nose, thereby verifying its practicality and utility.

A Radar Signal Deinterleaving Method Based on Complex Network and Laplacian Graph Clustering

A Radar Signal Deinterleaving Method Based on Complex Network and Laplacian Graph Clustering

Research on Traditional Chinese Medicine: Domain Knowledge Graph Completion and Quality Evaluation.

Knowledge graphs (KGs) can integrate domain knowledge into a traditional Chinese medicine (TCM) intelligent syndrome differentiation model. However, the quality of current KGs in the TCM domain varies greatly, related to the lack of knowledge graph completion (KGC) and evaluation methods. This study aims to investigate KGC and evaluation methods tailored for TCM domain knowledge. In the KGC phase, according to the characteristics of TCM domain knowledge, we proposed a 3-step "entity-ontology-path" completion approach. This approach uses path reasoning, ontology rule reasoning, and association rules. In the KGC quality evaluation phase, we proposed a 3-dimensional evaluation framework that encompasses completeness, accuracy, and usability, using quantitative metrics such as complex network analysis, ontology reasoning, and graph representation. Furthermore, we compared the impact of different graph representation models on KG usability. In the KGC phase, 52, 107, 27, and 479 triples were added by outlier analysis, rule-based reasoning, association rules, and path-based reasoning, respectively. In addition, rule-based reasoning identified 14 contradictory triples. In the KGC quality evaluation phase, in terms of completeness, KG had higher density and lower sparsity after completion, and there were no contradictory rules within the KG. In terms of accuracy, KG after completion was more consistent with prior knowledge. In terms of usability, the mean reciprocal ranking, mean rank, and hit rate of the first N tail entities predicted by the model (Hits@N) of the TransE, RotatE, DistMult, and ComplEx graph representation models all showed improvement after KGC. Among them, the RotatE model achieved the best representation. The 3-step completion approach can effectively improve the completeness, accuracy, and availability of KGs, and the 3-dimensional evaluation framework can be used for comprehensive KGC evaluation. In the TCM field, the RotatE model performed better at KG representation.

Exploring and Analyzing the Token Ecosystem: A Complex Network Analysis Perspective

The birth of Bitcoin created the era of cryptocurrency. At present, thousands of tokens are being published on blockchains, led by the popularity of Initial Coin Offerings (ICOs), launched by startups. Many have studied the token ecosystem from the perspective of economics. However, little is known about the characteristics of participants and tokens in the ecosystem. In order to fill this gap, and considering that more than 80% of ICOs are launched based on Ethereum, this article conducts a systematic survey of the Ethereum ERC20 and ERC721 token ecosystem to characterize the creators and holders of the tokens and transfer activities. By downloading the entire blockchain, and analyzing transaction records and event logs, we constructed three complex network graphs, a token creator graph, a token holder graph, and a token transfer graph. By analyzing these graphs, we are able to obtain many observations including token activeness distribution, token holder distribution, token relationship, and characteristics of token creation and decentralized exchanges. In addition, we propose an algorithm to reveal potential relationships between tokens and other addresses on Ethereum. The example shows that the proposed algorithm can effectively discover complex relationships between many accounts and find entities of addresses.

PeerRemove: An adaptive node removal strategy for P2P botnet based on deep reinforcement learning

Botnets have become one of the major intrusion threats to cybersecurity. P2P botnets have high concealment and resilience because of their distributed structure, which are difficult to be completely dismantled and destroyed. Existing methods based on traffic statistics and vulnerability tracing cannot effectively solve the problem of P2P botnet disintegration. Although P2P networks emphasize that each node is peer-to-peer, the difference in processing power, resource distribution, and node bandwidth can lead to a certain heterogeneity. The critical nodes bridge the underlying bot nodes and the upper control server. Traditional methods for ranking the importance of nodes mainly relies on classical graph-theoretic feature statistics method, such as degree, betweenness, clustering coefficient, feature vector centrality, PageRank, etc. In this paper, botnet defense strategies are investigated from the perspective of complex network graph theory, and graph embedding and deep reinforcement learning combination optimization methods are adopted to handle the critical nodes identification problem of P2P botnets. Then, a novel adaptive node removal model called PeerRemove is proposed. The model uses Structure2vec graph embedding to characterize the network structure information as a low-dimensional embedding space, and it uses n-step Q-learning to train the model to learn complex topological patterns to find the critical nodes that effectively disintegrate the network. To evaluate the effectiveness of the proposed method, the Area Under the Curve (AUC) of the Largest Connected Component (LCC) size during node removal is used as an evaluation indicator, and six different types real or synthetic P2P botnets are selected, namely Sality, ZeroAccess, NSIS, Mozi, Gnutella, and Peer sampling service. Experiments are conducted on many real and model networks with node sizes reaching thousands and tens of thousands, and our method is compared with five classical static or dynamic node attack methods of HAD, PageRank, CI, BPD, and HPRA. The experimental results show that the overall AUC curve of the PeerRemove method is lower than that of the benchmark method, which can minimize botnet resiliency at a small cost. The proposed method is superior to the existing node removal methods and shows good robustness and feasibility. To demonstrate the generality of this method, it is tested on a centralized topological dataset and good experimental results are obtained.

Simplified topological algorithm for forming a steady mode of electric power system

The paper presents the possibility of simplifying the algorithms for forming the parameters of the steady state mode of a complex electrical network, using the independence of the currents of its equivalent circuit. In this case, all independent currents of a complex circuit of an electrical network must be found in advance, which is, in the general case, a rather difficult task, since it requires additional calculations of the steady state. The matrix of current distribution coefficients, formalized on the basis of the topology of the electrical network, can be used to directly determine the currents in the branches of its equivalent circuit. This allows you to preliminarily estimate the values of pre-selected independent currents in the circuit branches of a complex electrical network and determine its simplified design scheme. In this case, a calculation model is established, consisting of some trees of a complex electrical network graph.

Study of spatialtemporal changes in Chinese forest eco-space and optimization strategies for enhancing carbon sequestration capacity through ecological spatial network theory

The conservation of forest ecosystems and the enhancement of carbon sequestration capacity play a crucial role in maintaining ecological balance and human development. However, with excessive deforestation, the flow of energy and information within the ecosystem has changed, which in turn has led to changes in the topological properties and carbon sequestration capacity of forest ecosystems. In order to better investigate the nature and carbon sequestration capacity of forest ecological space in mainland China during 2000–2018, we constructed a time-series Chinese forest ecological spatial network based on complex network theory and graph theory, combined with the modified minimal cumulative resistance model (MCR). By combining the net primary productivity (NPP) values obtained from the Boreal Ecosystem Productivity Simulator (BEPS) model of existing scholars, we further explored the relationship between topology and carbon sequestration capacity within forest ecosystems, and proposed strategies and suggestions for optimization. The results show that forest ecological sources and ecological corridors showed an increasing trend and resistance values decreased year by year during 2000–2018, especially in the western region, indicating that ecological restoration projects in western China have achieved certain effects. However, the stability of forest ecosystems has been decreasing year by year, and the forest carbon sequestration capacity in western China is also decreasing. Through correlation analysis, we found that carbon sequestration capacity showed highly significant positive correlation with closeness centrality, harmonic closeness centrality, clustering, and eigen centrality, and carbon sequestration capacity showed highly significant negative correlation with betweeness centrality. Through Principal Components Analysis (PCA), we suggest that consolidating small patches in the northeast, reducing the number of redundant ecological corridors, adding stepping stone patches to shorten the length of ecological corridors, and increasing ecological corridors in non-northeast areas are conducive to enhancing plant carbon sequestration capacity. This study provides theoretical support and ecological engineering recommendations for China to achieve its strategic goals of carbon neutrality and carbon peaking.

The use of PLANS and NetworkX in modeling power grid system failures

The theoretical and practical aspects and results of simulations based on a specialized tool that is used in the energy industry were adressed. The previously discussed cases in the literature by taking into account the worst case and critical states of networks in terms of complex networks were extended. Using the Monte-Carlo method, the vulnerability of the power grid to node failures was investigated, both in terms of the use of specialized software, which is used in the power industry, and a tool for the analysis of complex networks graphs. We present the results obtained and the observed analogy between the results of the analysis performed in specialized software and the complex network graph analysis tool. It has been shown that the results obtained coincide for both software packages, even though their application focuses on slightly different aspects of system operation. Moreover, further possibilities of extending the research in this direction are proposed, taking into account not only the improvement of the method used, but also a significant increase in the size of the tested structure model.

A network-based investigation on the strong contact system of granular materials under isotropic and deviatoric stress states

The contact network is essential in linking the macroscale and microscale behaviors of granular materials. Particles and contacts are equivalent to nodes and links in the framework of the complex network graph, and then the network-based metrics can be applied to assess the network topology of granular materials. In this paper, several series of 3D DEM simulations are carried out, including isotropic compression and deviatoric loading tests for assemblies with different particle size distributions. The topological features, such as cluster size, connectivity, network distance and betweenness centrality, are captured for strong force subnetworks with different force thresholds in granular materials. It is found that the particle size distribution does not influence the general topological characteristics of granular materials, and the percolation is observed in the strong network with a proper force threshold for all granular samples. Chained features are consistently identified for strong networks extracted by around 1.5 times the average contact force at all stress states, and this force threshold also supports a giant cluster spanning the overall granular system. Assemblies under deviatoric states exhibit more identical features in strong network topology than that under isotropic states, giving a hint to understanding the structural uniqueness of granular mechanics.

Efficient link prediction model for real-world complex networks using matrix-forest metric with local similarity features

Abstract Link prediction in a complex network is a difficult and challenging issue to address. Link prediction tries to better predict relationships, interactions and friendships based on historical knowledge of the complex network graph. Many link prediction techniques exist, including the common neighbour, Adamic-Adar, Katz and Jaccard coefficient, which use node information, local and global routes, and previous knowledge of a complex network to predict the links. These methods are extensively used in various applications because of their interpretability and convenience of use, irrespective of the fact that the majority of these methods were designed for a specific field. This study offers a unique link prediction approach based on the matrix-forest metric and vertex local structural information in a real-world complex network. We empirically examined the proposed link prediction method over 13 real-world network datasets obtained from various sources. Extensive experiments were performed that demonstrated the superior efficacy of the proposed link prediction method compared to other methods and outperformed the existing state-of-the-art in terms of prediction accuracy.

DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features.

SummaryIn biology, graph layout algorithms can reveal comprehensive biological contexts by visually positioning graph nodes in their relevant neighborhoods. A layout software algorithm/engine commonly takes a set of nodes and edges and produces layout coordinates of nodes according to edge constraints. However, current layout engines normally do not consider node, edge or node-set properties during layout and only curate these properties after the layout is created. Here, we propose a new layout algorithm, distance-bounded energy-field minimization algorithm (DEMA), to natively consider various biological factors, i.e., the strength of gene-to-gene association, the gene’s relative contribution weight and the functional groups of genes, to enhance the interpretation of complex network graphs. In DEMA, we introduce a parameterized energy model where nodes are repelled by the network topology and attracted by a few biological factors, i.e., interaction coefficient, effect coefficient and fold change of gene expression. We generalize these factors as gene weights, protein–protein interaction weights, gene-to-gene correlations and the gene set annotations—four parameterized functional properties used in DEMA. Moreover, DEMA considers further attraction/repulsion/grouping coefficient to enable different preferences in generating network views. Applying DEMA, we performed two case studies using genetic data in autism spectrum disorder and Alzheimer’s disease, respectively, for gene candidate discovery. Furthermore, we implement our algorithm as a plugin to Cytoscape, an open-source software platform for visualizing networks; hence, it is convenient. Our software and demo can be freely accessed at http://discovery.informatics.uab.edu/dema.Supplementary information Supplementary data are available at Bioinformatics online.

A Software Vulnerability Detection Method Based on Complex Network Community

To find out whether there is any vulnerability in software programs where conditional judgment is ignored, this article proposes a software vulnerability detection method based on complex network community. First, the method abstracts the software system into a directed weighted graph by using the software algebraic component model and then preprocesses the directed weighted graph to get a complex network graph. Then, by using the partition algorithm, the complex network graph is divided into the communities, and the key nodes in communities are found by nRank algorithm. Finally, the graph of the key nodes with high influence is matched with the complex network graph that has been preprocessed. In order to evaluate the effectiveness of the community partition algorithm and the nRank algorithm, comparative experiments are carried out on two datasets. The experimental results show that the community partition algorithm is better than the comparison algorithm in precision, recall, and comprehensive evaluation index, and the nRank algorithm is closer to the result of degree centrality measurement index than the PageRank algorithm and the LeaderRank algorithm. The spring-shiro-training project is used to verify the vulnerability detection method based on complex network community, and the results show that the method is effective.

Link prediction based on higher-order structure extraction and autoencoder learning in directed networks

Link prediction, which aims to estimate the likelihood of links based on the observed links and/or node attributes, is a common task in complex network analysis and graph data mining. Modeling and exploring high-order structure, also called network motif, are essential for understanding the fundamental structure that control and mediate the behavior of various complex systems. Most existing link prediction methods do not fully consider the high-order structure in the network. In addition, they mainly focus on undirected networks and therefore ignore the directionality of links. To this end, we propose a novel Autoencoder model based on high-order structure to solve the problems of existing link prediction methods. Specifically, we first provide an efficient motif adjacency matrix learning algorithm, which can be used to extract high-order structure in directed networks, and construct multiple motif adjacency matrices. Then, we extend the Graph Autoencoder (GAE) and Variational Autoencoder (VAE) frameworks to solve the link prediction problem in directed networks, which consists of three core modules. (i) First (Node Embedding): a novel encoder scheme is presented to learn the node embeddings from each motif adjacency matrix; (ii) Second (Information Fusion): a robust attention scheme is further introduced to aggregate information of node embeddings learned from multiple motif adjacency matrices to form the final semantically rich node embedding matrix; (iii) Third (Link Prediction): an efficient decoder scheme is finally proposed to reconstruct the target directed network in a multiscale way, by jointly utilizing node embeddings and PageRank centrality, the directions of links can be effectively inferred based on the classic gravitational heuristic formula. Extensive experiments were applied on various types of directed networks to validate the performance of our proposed approach through comparisons with the state-of-the-art link prediction technologies.

E-function for Fuzzy Clustering in Complex Networks

In many real-life situations, data consists of entities and the connections between them, which are naturally described by a complex network (graph). The structure of the network is often such that it is possible to group nodes based on the existence of connections between them, where such groups are called clusters (communities, modules). If the nodes are allowed to partially belong to clusters, they are called fuzzy (overlapping) clusters. There is a huge number of algorithms in the literature that perform fuzzy clustering, that is finds overlapping clusters, so a mechanism is needed to evaluate such clustering. The function that assesses the quality of a performed clustering is called the cluster quality function. One of the latest proposed quality functions is the E-function. The E-function is based on a comparison of the internal structure of a cluster, i.e., the connection between nodes within a cluster and the connection of its nodes with the nodes of other clusters. Due to its exponential nature, the E-function is sensitive to small changes in the membership degrees to which the nodes belong to clusters. As such, it has shown good results in evaluating clustering on known data sets. In this paper, the experimental results that the modified E-function achieves in the case of overlapping clusters are presented. Also, some possibilities for fuzzy clustering by optimizing the E-function are displayed.

Near-surface PM2.5 prediction combining the complex network characterization and graph convolution neural network

Near-surface PM2.5 prediction combining the complex network characterization and graph convolution neural network

Brain network features based on theta-gamma cross-frequency coupling connections in EEG for emotion recognition

Emotion recognition is a hot topic in the field of cognitive neuroscience and interpersonal interaction, and EEG feature selection is an important classification technology. At present, the mainstream method of EEG feature selection is to extract non-interactive features of channels such as power spectral density, or correlation features among local multi-channels. With the application of complex network graph theory, the connection network between multiple brain regions is gradually included in feature selection. However, in the process of brain network construction, most of the current connections adopt simple signal phase or amplitude synchronization. In recent years, it has been found that in the process of emotion, memory, learning, and other advanced cognitive processes, the large-scale connection and communication between the brain regions are mainly completed by the cross-frequency coupling(CFC) between the low-frequency phase and the high-frequency amplitude of neural oscillations. Based on this, we use CFC to update the connection mode, reconstruct the brain network, and extract features for emotion recognition research. Our results show that the EEG network based on CFC performs better than other EEG synchronization networks in emotion classification. Moreover, the combination of global features and local features of the brain network, as well as the dynamic network features with continuous time-windows, can effectively improve the accuracy of emotion recognition. This study provides a new idea of network connection for the follow-up study of emotion recognition and other advanced cognitive activities and makes a pioneering exploration for further research on feature selection of emotion recognition and related neural circuits at the brain network level of functional connectivity.

The impact of graph symmetry on the number of driver nodes in complex networks

This study investigates on a strong correlation between complex network (CN) controllability (characterized by the number of required driver nodes) and graph symmetry (described by automorphism groups) in undirected and unweighted networks. Based on the properties of permutation products of elementary automorphisms, novel necessary conditions for CN controllability are presented which are computationally more effective than previous method. In addition, a novel index of symmetry is proposed upon which a more meaningful understanding of symmetry impact on CN controllability can be comprehended. Based on this new index, a modification strategy is suggested aiming to satisfy CN controllability with a lower number of driver nodes. The study shows that the proposed modification approach can result in a minimal set of driver nodes with a reasonable computational complexity. Further, the critical components of complex networks, in terms of their impact on the number of required driver nodes, are identified. The results of the proposed methodologies have been verified for several synthetic and real test systems including small, medium, and large power networks.

Using K-core Decomposition on Class Dependency Networks to Improve Bug Prediction Model's Practical Performance

In recent years, Complex Network theory and graph algorithms have been proved to be effective in predicting software bugs. On the other hand, as a widely-used algorithm in Complex Network theory, k-core decomposition has been used in software engineering domain to identify key classes. Intuitively, key classes are more likely to be buggy since they participate in more functions or have more interactions and dependencies. However, there is no existing research uses k -core decomposition to analyze software bugs. To fill this gap, we first use k -core decomposition on Class Dependency Networks to analyze software bug distribution from a new perspective. An interesting and widely existed tendency is observed: for classes in k -cores with larger k values, there is a stronger possibility for them to be buggy. Based on this observation, we then propose a simple but effective equation named as top-core which improves the order of classes in the suspicious class list produced by effort-aware bug prediction models. Based on an empirical study on 18 open-source Java systems, we show that the bug prediction models’ performances are significantly improved in 85.2 percent experiments in the cross-validation scenario and in 80.95 percent experiments in the forward-release scenario, after using top-core . The models’ average performances are improved by 11.5 and 12.6 percent, respectively. It is concluded that the proposed top-core equation can help the testers or code reviewers locate the real bugs more quickly and easily in software bug prediction practices.

Complex network graph embedding method based on shortest path and MOEA/D for community detection

As one of the main applications of graph embedding, community detection has always been a hot issue in the field of complex network data mining. This paper presents a complex network graph embedding method based on the shortest path matrix and decomposition multi-objective evolutionary algorithm (SP-MOEA/D) for community detection, which can better reflect the network structure at the level of network community structure. Firstly, by calculating the shortest path matrix between nodes in the network, the node relationship matrix is obtained by adding the node similarity. Next, aiming at the problem of community detection in disconnected networks, a decomposition-based multi-objective optimization method is proposed to assign distances to unrelated nodes. Then, the network similarity matrix is calculated based on the relationship matrix of network nodes, and the low-dimensional vector representation of nodes is obtained by random surfing strategy and multi-dimensional scaling method. Finally, the community structure of the network can be detected based on the obtained node representation structure. Starting from the essence of network structure and the tightness between nodes, this method can reflect the relationship characteristics of network nodes more effectively, and then obtain the vector representation of nodes which can more accurately reflect the information of community structure in networks. The test results on 11 networks show that the node vector representation results obtained by this method can better reflect the community structure information in complex networks.

Mapping quantum circuits on 3D nearest-neighbor architectures

In some quantum technologies, an interaction is only allowed between physically adjacent qubits, hence the nearest-neighbor requirement is needed. In such technologies, quantum gates are limited to operate on adjacent qubits. To make a quantum circuit compliant with the nearest-neighbor requirement, SWAP gates are inserted into the circuit to move the interacting qubits of a gate to be adjacent to each other. The mapping of qubits on the physical environment has an important role on reducing the number of SWAP gates and thus the circuit latency. Focusing on this issue, in this paper, a method is proposed that maps a quantum circuit onto a 3D physical hardware such as a 3D optical lattice. A new methodology for this mapping problem is proposed based on a complex network spectral clustering algorithm and graph theory, which are suitable for very large scale networks. It includes three steps: finding the order of qubit mapping, placing physical qubits, and routing. Simulation results show that the proposed mapping approach not only decreases the average number of SWAP gates by about 37% but also improves the average runtime by about 87% for the 2D architecture compared to PAQCS. Moreover, it reduces the average number of SWAP gates for the 3D architecture by 17.1% compared to the best studies in the literatures.