Complex Network Approach Research Articles

Modeling emotional contagion in the COVID-19 pandemic: a complex network approach

During public health crises, the investigation into the modes of public emotional contagion assumes paramount theoretical importance and has significant implications for refining epidemic strategies. Prior research predominantly emphasized the antecedents and aftermath of emotions, especially those of a negative nature. The interplay between positive and negative emotions, as well as their role in the propagation of emotional contagion, remains largely unexplored. In response to this gap, an emotional contagion model was developed, built upon the foundational model and enriched from a complex network standpoint by integrating a degradation rate index. Stability analyses of this model were subsequently conducted. Drawing inspiration from topological structural features, an enhanced model was introduced, anchored in complex network principles. This enhanced model was then experimentally assessed using Watts-Strogatz’s small-world network, Barabási-Albert’s scale-free network, and Sina Weibo network frameworks. Results revealed that the rate of infection predominantly dictates the velocity of emotional contagion. The incitement rate and purification rate determine the overarching direction of emotional contagion, whereas the degradation rate modulates the waning pace of emotions during intermediate and later stages. Furthermore, the immunity rate was observed to influence the proportion of each state at equilibrium. It was discerned that a greater number of initial emotional disseminators, combined with a larger initial contagion node degree, can amplify the emotion contagion rate across the social network, thus augmenting both the peak and overall influence of the contagion.

Sentence Semantic Similarity based Complex Network approach for Word Sense Disambiguation

Word Sense Disambiguation is a branch of Natural Language Processing(NLP) that deals with multi-sense words. The multi-sense words are referred to as the polysemous words. The term lexical ambiguity is introduced by the multi-sense words. The existing sense disambiguation module works effectively for single sentences with available context information. The word embedding plays a vital role in the process of disambiguation. The context-dependent word embedding model is used for disambiguation. The main goal of this research paper is to disambiguate the polysemous words by considering available context information. The main identified challenge of disambiguation is the ambiguous word without context information. The discussed complex network approach is disambiguating ambiguous sentences by considering the semantic similarities. The sentence semantic similarity-based network is constructed for disambiguating ambiguous sentences. The proposed methodology is trained with SemCor, Adaptive-Lex, and OMSTI standard lexical resources. The findings state that the discussed methodology is working fine for disambiguating large documents where the sense of ambiguous sentences is on the adjacent sentences.

Directed recurrence networks.

Complex network approaches have attracted a growing interest in the analysis of nonlinear time series. Among other reconstruction methods, it has been shown that the recurrence plot can be used as the adjacency matrix for recurrence networks, expanding the applications of the already successful recurrence analysis. We study here the potential benefits of a directed formulation of recurrence networks through a simple modification of the recurrence plot. As it is directly related to the recurrence analysis field, this approach takes advantage of the progresses regarding the creation and treatment of the recurrence plot. It appears that directed recurrence networks provide more robust results than their undirected counterpart for transitions detection as well as temporal patterns discovery and clustering. New applications for network cleaning and data modeling are also demonstrated.

Resilience enhancement of water distribution networks under pipe failures: a hydraulically inspired complex network approach

Abstract The resilience of water distribution networks (WDNs) should be proactively evaluated to reduce the potential impacts of disruptive events. This study proposes a novel hydraulically-inspired complex network approach (HCNA) to assess and enhance WDN resilience in the case of single-pipe failure. Unlike conventional hydraulic-based models, HCNA requires no hydraulic simulations for resilience analysis. Instead, it quantifies the failure consequences of edges (pipes) on the WDN graph by incorporating topological attributes with flow redistribution triggered by failures. This HCNA procedure leads to the identification of critical edges (pipes), as well as impacted ones, representing edges more susceptible to the failure of others. The impacted edges are then systematically resized by integrating HCNA with a graph-based design approach, obtaining a wide range of resilience enhancement solutions. A comparative study between HCNA and a hydraulic-based model for three WDNs confirms HCNA's effectiveness in identifying the most critical pipes in various network sizes. Furthermore, HCNA provides comparable resilience enhancement solutions with a hydraulic-based evolutionary optimization but with significantly lower computational effort (1,400 times faster). Thus, it can efficiently be used for resilience enhancement of large-scale WDNs, where the application of conventional optimizations is limited due to the intensive computational workload.

Artificial Intelligence and Complex Network Approaches Reveal Potential Gene Biomarkers for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, and the number of cases is constantly increasing. Early and accurate HCC diagnosis is crucial to improving the effectiveness of treatment. The aim of the study is to develop a supervised learning framework based on hierarchical community detection and artificial intelligence in order to classify patients and controls using publicly available microarray data. With our methodology, we identified 20 gene communities that discriminated between healthy and cancerous samples, with an accuracy exceeding 90%. We validated the performance of these communities on an independent dataset, and with two of them, we reached an accuracy exceeding 80%. Then, we focused on two communities, selected because they were enriched with relevant biological functions, and on these we applied an explainable artificial intelligence (XAI) approach to analyze the contribution of each gene to the classification task. In conclusion, the proposed framework provides an effective methodological and quantitative tool helping to find gene communities, which may uncover pivotal mechanisms responsible for HCC and thus discover new biomarkers.

Ants Sleep, Plants do not: The Variation in Species’ Activity Influences the Topology of Interaction Networks

The emergence of graph theory allowed using the complex network approach to aggregate detailed information about interactions between species. Although the use of the complex network approach has improved the understanding about community structuring, few aspects such as the temporal variation in the species’ activity pattern in the networks’ topology were explored so far. The current study used the ecological network approach to investigate ants interacting in the extrafloral nectary (EFN) of plants in order to test the hypothesis that the temporal variation in the foraging behavior of these animals affects the networks’ topology. In order to assess the temporal effect on the interaction networks, 24-hour collections divided in two 12-hour shifts (day and night) were performed in 20 plots, thus totaling 288 collection hours over 6 months. The ant-plant interaction networks presented similarity among the topological metrics assessed throughout the day. Different ant species presented distinct foraging times. Thus, two modules referring to the day and night shifts emerged from the network and presented specific species at each foraging shift. On the other hand, the plants kept on providing the resource (active EFNs) throughout the day. The results found in the current study have shown that ecological networks keep their structures constant; however, the ecological processes ruling these networks can better respond to the effects caused, for example, by the temporal variation in species’ activity. Therefore, it is worth always taking into consideration the importance of ecological processes at the time to analyze interactions in the nature.

Cruise shipping network of ports in and around the emission control areas: a network structure perspective

PurposeThe rapid growth in cruise shipping coupled with increasing public awareness of climate change has led to increasing concerns about the impact cruise shipping poses on the environment, especially regarding air emissions. This study analyses the cruise shipping network of ports in and around the emission control areas (ECAs) to understand the structural properties of the network and ports.Design/methodology/approachA complex network approach was used to analyse the network data of 239 voyages serviced by 14 international cruise lines, visiting 127 ports across 44 countries in the Caribbean Sea.FindingsIt is found that the network has a small-world property with a short average path length and a high clustering coefficient. The regulations affect connections among ports, in which most ports in ECAs have lower connections than ports outside ECAs. A few ports in ECAs play important key roles, but many ports outside ECAs play a more important role in the network because the regulations are barriers for cruise ships entering the ports.Originality/valueThe findings of this study have drawn useful guidelines for cruise lines and port authorities to improve their operations. Constrictive recommendations are suggested to policymakers for designing reasonable regulations to attract more cruise shipping to travel in ECAs.

Systemic risk assessment using complex networks approach: Evidence from the Brazilian (re)insurance market

In this paper, we assess the systemic risk of the Brazilian (re)insurance market using static and dynamic complex network approach, where the companies are connected to each other through co-insurance, reinsurance and retrocession transactions. We found that some local reinsurers and non-life insurers act as bridges connecting the local and international markets. Furthermore, the Brazilian (re)insurance market is robust to extreme scenarios of insolvency, although there are points of vulnerability: a few local reinsurers and non-life insurers. Finally, this study contributes to the creation of a stress testing framework that relies solely on publicly available data.

Random Network Model for Assessing the Topological Performance of Water Distribution Systems

A complex network approach enables identifying various topological characteristics of critical infrastructure such as water distribution networks (WDNs). These infrastructure networks are designed based on site-specific conditions and also change over time, so at some point, their topological properties may require improvement, especially in terms of their functions. This research aims to analyze WDNs by establishing a random network with varying proportions of grids (pg). To generate random networks, the properties of the real WDN of a city in South Korea were used as a reference point. Our results indicate that, as the WDN shifted from a loop to a branched structure, the system functioning changed non-linearly with the existence of a threshold below which a network may be effectively improved with low resource inputs. More specifically, the threshold pgs were identified as 0.40 and 0.44 for network efficiency and decentralization. Additionally, the threshold pgs were around 0.22 and 0.23 with respect to availability and normalized efficiency when vulnerability was assessed by removing 1% of nodes whereas those were 0.40 and 0.45 when 10% of nodes were removed. On the other hand, the pg of the real WDN was 0.02 which was significantly lower than the thresholds, resulting in low network efficiency, more centralization and increased vulnerability. This suggests that an increase in pg is required to improve the performance of the WDN in the target area. Our study provides a novel framework for identifying a threshold for the functioning of WDNs. This threshold can be used for designing a new network, as well as for improving existing networks for better topological performance.

The effects of different forms of FDI on the carbon emissions of multinational enterprises: A complex network approach

The carbon emissions of multinational enterprises (MNEs) through FDI are causing a shift in the emissions burden and threatening mitigation targets. The purpose of this paper is to investigate the effects of the FDI stock, greenfield FDI, and M&As on carbon emissions of MNEs from 2005 to 2016 via quadratic assignment procedure (QAP) network analysis. Through QAP network analysis, we study the FDI-carbon emissions linkage considering the interactions among agents. The results show that the “high-income region to low-income region” mode is gradually becoming the driving force in the global carbon flow, producing lots of carbon emissions with a lower investment. The investments in this mode are dirtier. Second, financial institutions have become the key emitters of global carbon emissions through FDI. This investment model makes global carbon transfer indirect and concealed. Third, the significance of the impacts of the three forms of FDI on the carbon emissions of MNEs is positive, which confirms the pollution haven effect. The greatest contributor is the FDI stock, which comes from historical investments in high-income regions. Greenfield FDI is more influential than M&As in both high- and low-income regions. This paper is a valuable reference for understanding the environmental effects of FDI.

A critical node identification approach for complex networks combining self-attention and ResNet

Identifying critical nodes in complex networks is a challenging topic. There are already various crucial node identification methods based on deep learning. However, these methods ignore the interactions between nodes and neighbors when learning node representations, which result in node features learnt insufficient. To solve this problem, we propose a critical node identification model that combines self-attention and ResNet. First, we take degree centrality, closeness centrality, betweenness centrality and clustering coefficient as the features of nodes and use a novel neighbor feature polymerization approach to generate a feature matrix for each node. Then, the susceptible infection recovery (SIR) model is used to simulate the propagation ability of the nodes, and the nodes are categorized based on their propagation ability to acquire their labels. Finally, the feature matrix and labels of the nodes are used as inputs to the model to learn the hidden representation of the nodes. We evaluate the model with accuracy, precision, recall, the F1 index, the ROC curve, and the PR curve in five real networks. The results show that the method outperforms benchmark methods and can effectively identify critical nodes in complex networks.

The role of atmospheric rivers in the distribution of heavy precipitation events over North America

Abstract. Atmospheric rivers (ARs) are filaments of extensive water vapor transport in the lower troposphere that play a crucial role in the distribution of freshwater but can also cause natural and economic damage by facilitating heavy precipitation. Here, we investigate the large-scale spatiotemporal synchronization patterns of heavy precipitation events (HPEs) over the western coast and the continental regions of North America (NA), during the period from 1979 to 2018. In particular, we use event synchronization and a complex network approach incorporating varying delays to examine the temporal evolution of spatial patterns of HPEs in the aftermath of land-falling ARs. For that, we employ the SIO-R1 catalog of ARs that landfall on the western coast of NA, ranked in terms of intensity and persistence on an AR-strength scale which varies from level AR1 to AR5, along with daily precipitation estimates from ERA5 with a 0.25∘ spatial resolution. Our analysis reveals a cascade of synchronized HPEs, triggered by ARs of level AR3 or higher. On the first 3 d after an AR makes landfall, HPEs mostly occur and synchronize along the western coast of NA. In the subsequent days, moisture can be transported to central and eastern Canada and cause synchronized but delayed HPEs there. Furthermore, we confirm the robustness of our findings with an additional AR catalog based on a different AR detection method. Finally, analyzing the anomalies of integrated water vapor transport, geopotential height, upper-level meridional wind, and precipitation, we find atmospheric circulation patterns that are consistent with the spatiotemporal evolution of the synchronized HPEs. Revealing the role of ARs in the precipitation patterns over NA will lead to a better understanding of inland HPEs and the effects that changing climate dynamics will have on precipitation occurrence and consequent impacts in the context of a warming atmosphere.

A rule-based maritime traffic situation complex network approach for enhancing situation awareness of vessel traffic service operators

The development of the shipping industry has led to increasingly busy traffic near many port areas, which leads to higher demands for vessel traffic service operators (VTSOs), and challenges to maintain situational awareness. However, an effective rule-based method for analyzing waterway traffic from a systemic perspective to improve situational awareness is lacking. Therefore, this article proposes a method to assess the maritime traffic situation complexity for this purpose. Referring to the COLREGs, a rule-based maritime traffic situation complex network (R-MTSCN) model is established by taking ships as nodes, and the collision avoidance relationship between ships as the directed edges. In addition, network topology features such as degree, vertex strength, and vertex strength distributions are used to statistically characterize the evolution of the complexity of maritime traffic systems. By analyzing the AIS data of ships navigating the Yangtze River estuary, the concept of the proposed model is demonstrated, thus providing a pathway to develop intelligent, digital, and data-driven Vessel Traffic Services, aiming to enhance efficiency of vessel traffic management and improve maritime safety.

Analysis on the structure and economic resilience capacity of China’s regional economic network

ABSTRACT This paper analyzes the heterogeneous influence of economic networks on urban economic resilience capacity from different network characteristics, providing an innovative research perspective and theoretical framework for the economic resilience capacity of urban clusters. The urban economic elasticity is measured by multiple indicators and constructed economic network using night light data, the complex network approach was combined with spatial econometrics to analyse the contribution of economic network linkages and economic network locations to regional economic resilience capacity. The results show that: urban economic network linkage has a significant positive impact on urban economic resilience capacity, and the polycentric pattern of urban clusters has a driving effect on this impact; in contrast, the influence of urban network location on economic resilience capacity has an inverted u-shaped curve. Compared with non-smart cities, regions in smart cities are better able to promote urban economic resilience capacity through economic networks, and the impact of economic networks on regional economic resilience capacity has network external spillover effects beyond geographical distance, The results of the study infer that cities in urban agglomeration should rationalize network locations, and build a coordinated urban agglomeration network structure to enhance economic resilience capacity of the regional economy.

Analysis on the evolution characteristics of barite international trade pattern based on complex networks

Barite is widely used in oil and gas drilling and strategic emerging industries, and its strategic position is becoming increasingly prominent. Based on a complex network approach, this paper constructs a global barite trade network (BTN) from 2000 to 2020, analyzes the evolutionary characteristics of the global barite trade pattern, and identifies the key trading countries and main trading communities. The findings show that the global BTN has become more connected and efficient. The import status of European countries has weakened, but there is still a high degree of activity in the barite trade. The import status of barite in North American countries has always been high, and the import status of barite in South American countries has been significantly enhanced. Asia is an important region for barite export trade. China and Turkey, important exporting countries in Asia, have become more active in barite export trade. The discourse and control of barite trade are more in the hands of developed countries in Europe and the United States, but emerging economies such as China and India are increasingly playing an important role. Further research revealed that the main trading communities of barite gradually evolved from two main trading communities dominated by China and Germany to six main trading communities dominated by China, India, Turkey, USA, Germany, and Morocco. The traditional dominant powers, China and Germany, have weakened in their communities, and the emerging dominant powers, the United States, India, and Turkey, have increased their power in their communities.

International trade evolution and competition prediction of boron ore: Based on complex network and link prediction

As an important non-metallic strategic mineral resource, boron plays an important role in the development of national emerging strategic industries. In this study, a complex network approach, core-periphery model and link prediction model are used to analyze the evolution of international trade and potential future competition of boron ore. The study found that: (1) the international demand and trade volume of boron ore showed a growing trend yearly, with increasingly closer trade relations and higher trade efficiency. (2) Boron ore exports are closely related to resource endowment. Turkey, as the world's largest country in terms of reserves and production, holds the absolute export initiative in the boron ore international trade. Boron ore imports are mainly affected by the level of economic development, China and the United States belong to the control trade importers; European countries are important transit countries. (3) The import competition of boron ore international trade is becoming more and more intense and concentrated, mainly forming a competitive relationship with Turkey as the common import source. And at present, it is mostly a game between economically developed countries, the core of which is the competition of “China-US-India-Europe”; other import competitions are influenced by geographical location. (4) In the future, the potential import competition will gradually develop in emerging economies, and the competition center will gradually move from Europe and Asia to Latin America and Africa. Countries with geographical advantages such as coastal, ports and proximity to boron ore areas will have a greater degree of trade options and competitive development potential.

Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials' Discovery.

Microbial biofilms cause several environmental and industrial issues, even affecting human health. Although they have long represented a threat due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs), including their antibiofilm activity and their potential to target multiple microbes, has motivated the synthesis of AMPs and their relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that have allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has not yet been explored as an assistant tool for this aim. Herein, a kind of similarity network called the half-space proximal network (HSPN) is applied to represent/analyze the chemical space of ABFPs, aiming to identify privileged scaffolds for the development of next-generation antimicrobials that are able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated with the ABFPs, such as origin, other activities, targets, etc., in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks' mining, a reduced but informative set of 66 ABFPs was extracted, representing the original antibiofilm space. This subset contained the most central to atypical ABFPs, some of them having the desired properties for developing next-generation antimicrobials. Therefore, this subset is advisable for assisting the search for/design of both new antibiofilms and antimicrobial agents. The provided ABFP motifs list, discovered within the HSPN communities, is also useful for the same purpose.

Performance assessment of a communication infrastructure with redundant topology: A complex network approach

The physical assets within a critical infrastructure system are pivotal to its efficient performance and protection and that of other dependent systems. This is particularly the case for communication systems where network protection strategies usually involve asset redundancy. Although such redundancy is well-modelled in the literature, there is a gap in knowledge from a network science perspective in terms of its implications for network modelling and performance assessment. This paper presents a multilayer complex network framework that takes into account the heterogeneity of the redundant infrastructure for realistic network modelling and further analysis, a step change from using a single network model. Key performance indicators (KPIs) for communication networks (i.e., latency and jitter, bandwidth and throughput, queue depth and packet drops) are redefined to evaluate key important features of a long-haul backbone network such as network capacity and average use. In addition, these KPIs are adapted to deal with the aforementioned redundancy and so inform network managers with values defined over a model closer to the real system. The paper analyses the use case of a nationwide core and metro network infrastructure of one of the main UK internet service providers. The results of the analysis of KPIs showcase the advantage of the proposed multilayer complex network framework over the traditional single network model. Critical network elements within different dimensions of a communication network are identified based on their performance for prioritising network management measures.

A graph-based superframework for mixture model estimation using EM: an analysis of US wholesale electricity markets

A fully unsupervised graph-based superframework is proposed to handle the EM initialization problem for estimating mixture models on financial time series. Using a complex network approach that links time series and graphs, the graph-structured information derived from the observed data is exploited to produce a meaningful starting point for the EM algorithm. It is shown that structural information derived by complex graphs can definitely capture time series behavior and nonlinear relationships between different observations. The proposed methodology is employed to estimate Gaussian mixture models on US wholesale electricity market prices using two different configurations of the superframework. The obtained results show that the proposed methodology performs better than conventional initialization methods, such as K-means based techniques. The improvements are significant on the overall representation of the empirical distribution of log-returns and, in particular, on the first four moments. Moreover, this approach has a high degree of generalization and flexibility, exploiting graph manipulation and employing functional operating blocks, which can be adapted to very different empirical situations.

Characterizing the statistical complexity of nonlinear time series via ordinal pattern transition networks

Complex network approaches have lately emerged as innovative and complementary notions of nonlinear time series analysis, capable of revealing numerous aspects that more classic analytic methods fail to reveal. In this paper, we concentrate on ordinal pattern transition networks (OPTN) to characterize the statistical complexity of time series by considering each pattern as a network node and the probability of transition between patterns as a directed weighted edge between nodes. The traditional complexity measures of time series, permutation entropy (PE) and transition entropy (TE), have been first explained via OPTN, and a new statistical complexity technique termed permutation weighted statistical transition entropy (PWSTE) is suggested to address some drawbacks of some existing complexity measures. In the context of OPTN, the proposed PWSTE enhances PE and TE by taking into account both static information (i.e., network node probability distributions) and dynamic information (i.e., time series transition probabilities represented by nodes). It combines with the imbalance of OPTN, which enables a more accurate measurement of the statistical complexity of nonlinear time series. The suggested new technique for assessing statistical complexity is thoroughly described based on the simulated time series, and the findings demonstrate that the proposed algorithm is more sensitive and effective in identifying dynamic changes. When the proposed algorithm is used to medical data, the EEG time series of epileptic patients are examined to define the changes in EEG data over various stages. The findings demonstrate that the proposed PWSTE algorithm outperforms conventional methods in identifying the stage in which a patient’s EEG data is located.