Complex Network Science Research Articles

Universal scaling hypothesis of quantum spatial search in complex networks

Since quantum spatial searches on networks have a strong network dependence, the question arises whether the universal perspective exists in this quantum algorithm for various networks. Here, we propose a universal scaling hypothesis of the quantum spatial search on complex networks such as small-world and scale-free networks. The average path length, a key quantity in the complex network science, is useful to expose this universal feature, where the collapse plot can be generated for the optimal time, the maximal finding probability, and the optimal hopping parameter. Based on the path integral method, we also clarify that the probability amplitude in the continuous-time quantum walk can be determined by the path length distribution. Published by the American Physical Society 2024

A novel approach to detecting epileptic patients: complex network-based EEG classification

Abstract Detection of epileptic seizures is important for early diagnosis and treatment. It is known that the behavioral patterns of the brain in electroencephalogram (EEG) signals have huge and complex fluctuations. Diagnosing epilepsy by analyzing signals are costly process. Various methods are used to classify epileptic seizures. However, the inadequacy of these approaches in classifying signals makes it difficult to diagnose epilepsy. Complex network science produces effective solutions for analyzing interrelated structures. Using methods based on complex network analysis, it is possible to EEG signals analyze the relationship between signals and perform a classification process. In this study proposes a novel approach for classifying epileptic seizures by utilizing complex network science. In addition, unlike the studies in the literature, classification processes were carried out with lower dimensional signals by using 1-s EEG signals instead of 23.6-s full-size EEG signals. Using the topological properties of the EEG signal converted into a complex network, the classification process has been performed with the Jaccard Index method. The success of the classification process with the Jaccard Index was evaluated using Accuracy, F1 Score, Recall, and K-Fold metrics. In the results obtained, the signals of individuals with epileptic seizures were separated with an accuracy rate of 98.15%.

A novel method for identifying key nodes in multi-layer networks based on dynamic influence range and community importance

Identifying key nodes in multi-layer networks is a hot research topic in complex network science and has broad application prospects, such as in mining enterprises that significantly affecting multi-layer industrial chains. Unlike single-layer networks, nodes in multi-layer networks exhibit heterogeneity due to varying connections and locations. There are also correlations between different network layers, which is particularly evident in industrial chains where companies operate across multiple layers of production, supply and distribution. It is necessary to consider the impact of these layers on the global performance of key node identification. In addition, due to changes in connections, the community structure of each network layer should be different, reflecting the dynamic nature of industrial collaborations and partnerships. However, existing research lacks the model that addresses the above problems. Therefore, this paper proposes a key node identification method based on Dynamic Influence Range and Community Importance (DIRCI), using both local and global information of the multi-layer network simultaneously. DIRCI determines the importance of nodes through three centrality measures: dynamic influence range-based centrality, network layer centrality and community-based centrality. Dynamic influence range-based centrality models node heterogeneity by combining the influence range of nodes and their neighbors with lower computational costs. Network layer centrality captures the corresponding importance for different network layers. Community-based centrality comprehensively considers the importance of community, the importance of each node within the community and between different communities. Experimental results for nineteen multi-layer networks show that DIRCI achieves better performance of key node identification than the latest algorithms.

Unraveling the Hemolytic Toxicity Tapestry of Peptides using Chemical Space Complex Networks.

Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.

Anomalous citations detection in academic networks

Citation network analysis attracts increasing attention from disciplines of complex network analysis and science of science. One big challenge in this regard is that there are unreasonable citations in citation networks, i.e., cited papers are not relevant to the citing paper. Existing research on citation analysis has primarily concentrated on the contents and ignored the complex relations between academic entities. In this paper, we propose a novel research topic, that is, how to detect anomalous citations. To be specific, we first define anomalous citations and propose a unified framework, named ACTION, to detect anomalous citations in a heterogeneous academic network. ACTION is established based on non-negative matrix factorization and network representation learning, which considers not only the relevance of citation contents but also the relationships among academic entities including journals, papers, and authors. To evaluate the performance of ACTION, we construct three anomalous citation datasets. Experimental results demonstrate the effectiveness of the proposed method. Detecting anomalous citations carry profound significance for academic fairness.

Global efficiency and network structure of urban traffic flows: A percolation-based empirical analysis.

Making the connection between the function and structure of networked systems is one of the fundamental issues in complex systems and network science. Urban traffic flows are related to various problems in cities and can be represented as a network of local traffic flows. To identify an empirical relation between the function and network structure of urban traffic flows, we construct a time-varying traffic flow network of a megacity, Seoul, and analyze its global efficiency with a percolation-based approach. Comparing the real-world traffic flow network with its corresponding null-model network having a randomized structure, we show that the real-world network is less efficient than its null-model network during rush hour, yet more efficient during non-rush hour. We observe that in the real-world network, links with the highest betweenness tend to have lower quality during rush hour compared to links with lower betweenness, but higher quality during non-rush hour. Since the top betweenness links tend to be the bridges that connect the network together, their congestion has a stronger impact on the network's global efficiency. Our results suggest that the spatial structure of traffic flow networks is important to understand their function.

Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference

In complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of a system and for obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is limited by the difficulty of system model construction; DEMATEL (Factual Decision Trial and Evaluation Laboratory) is inevitably influenced by subjective factors. In view of this, we propose a novel model based on heuristic causal inference. By combining the network analysis in complex network science, the model defines the global/local causal pathway and the causal pathway’s length in the causal network and takes the causal pathway contribution degree as an indicator to measure the approximate causal effects. The model includes steps such as causal network learning, causal pathway contribution degree calculation, and key influencing factor identification. The model uses the Fast Causal Inference (FCI) algorithm with prior knowledge to learn the global causal network of the complex system and uses the heuristic causal inference to calculate the causal pathway contribution degree. The heuristic method draws on the idea of complex network topology analysis and measures the influence degree between variables by the number and distance of causal pathways. The key influencing factors are finally identified according to the causal pathway contribution degree. Based on the SECOM dataset, we carried out simulation experiments and demonstrated the feasibility and effectiveness of the proposed method.

Complex systems and network science: a survey

Complex systems and network science: a survey

Key Points-in-Time Identification of Gold Futures Market: A Complex Network Approach

Important nodes can determine the internal structure of complex networks and reveal the internal relationships of real-world systems, and identifying key nodes in complex networks is one of the important research areas of complex network science. As the king of commodities, changes in the price of gold significantly impact the economic development of various countries. Especially in the early stages of the outbreak of war between Russia and Ukraine, the price of gold futures has been greatly impacted, and the systemic risks are gradually spreading. In this paper, a gold future price series is mapped into a visibility graph (VG), the characteristics of the gold price time series and key points-in-time, have been explored from the perspective of complex network. First, according to the data structure characteristics of gold futures, this paper converts the closing prices of gold futures of the New York Mercantile Exchange into a complex network through the VG model. Then, by using the complex network model to further delve into the price of gold futures, it is found that the degree distribution of the gold futures network follows a power-law distribution, and has obvious scale-free characteristics. Finally, this paper uses the visual network node shrinking algorithm and the technique for order preference by similarity to ideal solution (TOPSIS) analysis method to identify the key nodes of the gold futures visual map to find the key time nodes in the timeline of gold futures market. Analysis of the key time nodes of this market by four methods reveals that the repetition rate of the key time nodes in the methods’ top 10 ranking is as high as 82.5%, indicating that the results obtained in this paper are robust. This study introduces a new model to describe the characteristics of gold futures price series, one which can find key time nodes in gold futures prices and provide potential help for predicting gold futures prices.

Neural Network Optimization Based on Complex Network Theory: A Survey

Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex network theory for the study of network topology, it is obvious that complex network topology models can be applied to enhance artificial neural network models. In this paper, we provide an overview of the most important works published within the past 10 years on the topic of complex network theory-based optimization methods. This review of the most up-to-date optimized neural network systems reveals that the fusion of complex and neural networks improves both accuracy and robustness. By setting out our review findings here, we seek to promote a better understanding of basic concepts and offer a deeper insight into the various research efforts that have led to the use of complex network theory in the optimized neural networks of today.

The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis.

Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research results in molecular biology, biochemistry, or pharmacology. The problem is that there is much uncertainty in these drug datasets-we know the drug-drug or drug-target interactions reported in the research papers, but we cannot know if the not reported interactions are absent or yet to be discovered. This uncertainty hampers the accuracy of such bioinformatics applications. We use complex network statistics tools and simulations of randomly inserted previously unaccounted interactions in drug-drug and drug-target interaction networks-built with data from DrugBank versions released over the plast decade-to investigate whether the abundance of new research data (included in the latest dataset versions) mitigates the uncertainty issue. Our results show that the drug-drug interaction networks built with the latest dataset versions become very dense and, therefore, almost impossible to analyze with conventional complex network methods. On the other hand, for the latest drug database versions, drug-target networks still include much uncertainty; however, the robustness of complex network analysis methods slightly improves. Our big data analysis results pinpoint future research directions to improve the quality and practicality of drug databases for bioinformatics applications: benchmarking for drug-target interaction prediction and drug-drug interaction severity standardization.

Link Prediction of Complex Network Based on Eigenvector Centrality

As one of the important methods connecting complex network and computer science, Link prediction deals with the most basic problems in information science. Therefore it is of great importance to probe into it. But how to improve the prediction accuracy is one of the focus problems we are facing. Most of the current link prediction methods are related to the indicators based on the similarity of nodes, and the importance of the neighbor nodes of nodes in the network is often determined by the similarity of nodes. indicators are ignored. Considering the aforementioned problems, we propose a link prediction algorithm based on eigenvector centrality calculated by node importance based on the eigenvector. The algorithm mainly uses the information of eigenvector centrality and considers Common Neighbor (CN), Adamic-Adar (AA) The similarity index of and Resource Allocation (RA), and the AUC value and the exact value are used as a reference for the pros and cons of the index, The results of simulation experiments are reported on two different network data sets, and the final results indicate that the algorithm based on eigenvector centrality is more accurate than the algorithm based on node importance in the link prediction of complicated networks.

Estimating wider economic impacts of transport infrastructure Investment: Evidence from accessibility disparity in Hong Kong

Much recent work on transport and the economy has focused on ‘Wider Economic Impacts’ (WEIs) of infrastructure investment, the impacts other than time savings benefiting those actually using the transport network. Differential effects of transportation infrastructure by mode such as urban rail and road are relatively well known. However, impacts of other mode such as walking are scarce. This paper estimates wider economic impacts related to productivity from full rail, road and walking transport networks in Hong Kong in 2016. To the best of our knowledge, this is the first paper that makes use of complex network science indicators with spatial cognition-weighted accessibility combining full urban rail network, road network, and pedestrian network in capturing wider economic impacts. We use an instrumental variable approach to identify the causal effect of transport network centralities on productivity measured by gross value added. Our identification strategy largely relies on the exogenous variations from historical planned and existing transport networks. A first specification confirms the significance of urban rail and road. Specification with pedestrian network shows that pedestrian and rail networks can statistically significantly increase productivity in Hong Kong while roads play a less significant but still meaningful role. Our findings are robust to a variety of sensitivity tests such as using night-time light intensity and residential wage as alternative measures for productivity. The research suggests a key planning policy implication: place-based policies in a dense city require improvement in pedestrian and rail network structure that impacts local and global transport accessibility.

On hypercomplex networks

The concept of ‘complexity’ plays a central role in complex network science. Traditionally, this term has been taken to express heterogeneity of the node degrees of a therefore complex network. However, given that the degree distribution is not enough to provide an invertible representation of a given network, additional measurements of its topology are required in order to complement its characterization. In the present work, we aim at obtaining a new model of complex networks, called hypercomplex networks — HC, that are characterized by heterogeneity not only of the degree distribution, but also of a relatively complete set of complementary topological measurements including node degree, shortest path length, clustering coefficient, betweenness centrality, matching index, Laplacian eigenvalue and hierarchical node degree. The proposed model starts with uniformly random networks, namely Erdős–Rényi structures, and then applies optimization to change the network structure so as to increase a complexity index relatively to a set of reference networks. Gradient descent has been adopted for implementing this optimization. We also propose a complexity index that expresses the dispersion of the several considered measurements. Several interesting results are reported and discussed, including the fact that the HC network undergoes, as the optimization proceeds, a trajectory in the principal component space of the measurements that tends to depart from the considered theoretical models (Erdős–Rényi, Barabási–Albert, Waxman, Random Geometric Graph and Watts–Strogatz), heading to an empty portion of the feature space (low density of cases). We observed that, after a considerably large number of optimization steps, peripheral branching tends to appear that further enhances the complexity of these networks.

Network Origins of the Global Economy: East vs. West in a Complex Systems Perspective

Network Origins of the Global Economy: East vs. West in a Complex Systems Perspective

Modular interdependency analysis for water distribution systems

Complexity in water distribution systems (WDSs) poses a challenge for analysis and management of the systems. To reduce the complexity, the recent development of complex network science provides a system decomposition technique that converts a complex WDS with a large number of components into a simple system with a set of interconnected modules. Each module is a subsystem with stronger internal connections than external connections. Thus far, the topological features of the modular structure in WDS have been extensively studied but not the behavioural features, e.g. the hydraulic interdependencies among modules. Therefore, this paper aims to quantitatively measure and graphically visualize the module interdependency in WDSs, which helps understanding the behavioural complexity of WDSs and thus various WDS analyses, such as pipe maintenance, model calibration, rehabilitation, and District Metered Areas planning. Specifically, this study first identifies the WDS's modular structure then measures how changes in the state of one module (i.e. any single pipe failure or perturbed demand within each module) affect the state of another module. Modular interdependencies are summarized in an interdependency matrix and visualized by the digraph. Four real-world systems are analysed, and three of them shows low interdependencies among most of the modules and there are only a few critical modules whose status changes will substantially affect a number of other modules. Hence, highly interconnected topologies may not result in strong and complex module interdependency, which is a fact that simplifies several WDS analysis for practical applications as discussed in this paper.

Exploring the rich-club characteristic in internal migration: Evidence from Chinese Chunyun migration

Regional development differences are universal economic development process problems of countries around the world. Unbalanced development theories have been used to interpret population migration; conversely, population migration patterns can also reflect unbalanced regional development. Inspired by the growth pole and pole-axis theories, we infer that a rich-club phenomenon in internal migration caused by unbalanced regional development exists. We propose a progressive methodological framework combining complex network science and geographic information science applied to Chinese Chunyun migration to empirically research this potential rich-club phenomenon in an intercity population migration network and investigate the functions of prominent cities in intra/interregional migration. We use the global rich-club coefficient to examine whether the rich-club phenomenon exists in the population migration network. Then, the community detection method and assortativity coefficient are applied to further understand the connections between cities. Finally, the influence and functions of prominent cities are explored from multiple perspectives and scales. The results indicate that the rich-club phenomenon existed in intercity migration during the Chinese Chunyun period and presents the positive role of rich-club cities in intra/interregional migration. An imbalance in urban and regional development is also found. This paper can be used to guide urban planning, transportation planning, and population research.

On Complexity and the Prospects for Scientific Advancement

With the onset of areas such as complex systems, network science, and artificial intelligence, efforts have been invested in modeling science itself. In the present work, we report a related approach to modeling the influence of the complexity of knowledge on the respective prospects for scientific advancement. More specifically, we focus on the question of how much the topological complexity of the knowledge network can influence the prospects for scientific advancement. Once the knowledge has been represented as a complex network, we consider one of its subnetworks, the nucleus, as representing the currently known portion of that network. The relative number of nodes adjacent to the nucleus, and the ratio between this quantity and the quantity of edges interconnecting the nucleus with the remainder of the network, are taken as quantifications of the potential for scientific advancement and the efficiency with which these advances can take place. Subsequent nucleus sizes are considered in both a simpler network (Erdos-Renyi) and a more complex model (Barabasi-Albert). The results surprisingly tended to vary little between these two models, suggesting that the complexity of the knowledge network may have little effect on the prospects for scientific advancement as modeled in the present approach.

Estimating Wider Economic Impacts of Transport Infrastructure Investment: Evidence from Accessibility Disparity in Hong Kong

This paper estimates wider economic impacts related to productivity from transport networks in Hong Kong in 2016. Specifically, we use an instrumental variable approach to identify the causal effect of transport network centralities on productivity measured by gross value added. Our identification strategy largely relies on the exogenous variations from historical planned and existing transport networks. To the best of our knowledge, this is the first paper that makes use of complex network science indicators of accessibility combining urban rail, road network, and pedestrian network in capturing wider economic impacts. Our results confirm that pedestrian and rail networks can statistically significantly increase productivity in Hong Kong while roads play a less significant but still meaningful role. The research suggests a key planning policy implication: place-based policies in a dense city require a combination of local and global transport accessibility improvement which facilitates both agglomeration and decentralization.

“The persistent myth of lost hegemony,” revisited: structural power as a complex network phenomenon

This article resuscitates the idea of structural power in world politics by linking it to modern complex network science, presents a theoretical framework for understanding how global structures develop and change, and empirically analyzes the prominence of leading states within global finance, trade, security, and knowledge networks. It argues that the “fitness plus preferential attachment” (FPA) model of complex network evolution provides a logical explanation for the durability of American influence even as some of its advantages in country-level capabilities has eroded, and it introduces a network methodology that is capable of empirically analyzing the organizational complexity that exists within and across domains in world politics. It argues that the rise of China and other emerging powers has been overstated in some ways, but that a redistribution of structural prominence is taking place, in some domains, as emerging markets increase their transnational connections; this has mostly come at the relative expense of Europe, however, rather than the United States.