Complex Network Analysis Research Articles

Transmission Mechanism of Influencing Factors in the Promotion and Application of Whole-Process Engineering Consulting

As a project construction organization model, whole-process engineering consulting (WPEC) is currently being promoted in China’s engineering consulting industry. However, various factors create numerous obstacles that hinder this model’s effective promotion and application. One essential task is to deeply analyze the transmission mechanism of the influencing factors in the promotion and application of WPEC, and explore effective pathways for promoting and applying this model. This paper systematically organizes and summarizes 10 primary influencing factors and 38 secondary influencing factors for promoting WPEC. Using the complex network analysis method, a network of influencing factors for promoting WPEC, a network structure, and motif analyses are conducted. The results show that the influencing factors have a complex transmission mechanism, and complex network analysis can reveal the laws of their construction and their causes. This paper provides a systematic basis and theoretical support for the effective formulation of policies related to the promotion and application of WPEC.

Characterization of Z cluster connectivity in CuZr metallic glasses.

Previous studies have proposed that the backbone of metallic glasses consists mainly of high-centrosymmetric structures, particularly Z clusters, which are responsible for the strength of the glass matrix. However, exploring these networks involves medium-range order analysis, a topic still not fully understood in the literature. This study investigates the atomic connectivity of CuZr metallic glasses by analyzing Z clusters using complex networks to establish their relationship with the mechanical behavior. Our results reveal higher connectivity and larger network sizes in the sample exhibiting the most pronounced stress overshoot, while the opposite trend is observed in samples with less pronounced stress overshoot. Metrics, such as density and clustering coefficient, further validate the correlation between Z cluster connectivity and mechanical behavior. These findings underscore the critical role of Z cluster connectivity in understanding the mechanical response of metallic glasses. Molecular dynamics simulations were conducted using the LAMMPS software. Atomic interactions in Cu Zr metallic glasses were modeled using the embedded atom method, and compression tests were performed to assess the mechanical response. Atomic connectivity was examined through complex network analysis based on Z clusters, utilizing the NetworkX library for the Python programming language. Within this framework, parameters such as the average coordination number, network size, and network density were calculated, revealing the relationship between the interpenetrating Z cluster structure and the mechanical response of the samples.

Mapping analytical methods between input–output economics and network science

AbstractThe input–output (IO) model can be used to examine the flow of products and services within an economy, resembling a network with industries as nodes and transactions as links. Diverging significantly from commonly studied networks such as social, protein, and power grids, IO networks exhibit intricate interconnectivity, involving weighted nodes and both directional and weighted links. This uniqueness necessitates careful consideration when applying complex network analysis techniques to IO systems. We critically review current complex network metrics and attempt to link them with existing IO approaches. Based on our assessment, certain network metrics, such as degree centrality and eigenvector centrality, have been explicitly integrated into the IO theory. In contrast, there exist metrics whose definitions and interpretations expand when applied in the context of IO analysis, including closeness and betweenness centrality. Additionally, network metrics are usually used to study topological features, identify key sectors, and construct novel metrics to study related issues. Network metrics used in IO analysis can identify important driver and transmission sectors in resource flow and environmental emission network, facilitating the development of targeted and reliable strategies. Besides, network metrics are used to quantify topological features and structural changes of the IO network which help strengthen the supply chain and mitigate both direct and indirect impacts of disruptions. Our ultimate goal is to establish connections and offer a roadmap for developing network‐based tools in IO analysis.

Rules of acupoint selection and pattern-acupoint relationship in treatment with acupuncture and moxibustion for endometriosis based on complex network analysis technology

To explore the rules of acupoint selection and pattern-acupoint relationship in treatment with acupuncture and moxibustion for endometriosis (EMs) based on complex network analysis technology. The articles for clinical trial of EMs treated with acupuncture and moxibustion were searched from CNKI, Wanfang, VIP, SinoMed, PubMed, EMbase and Cochrane Library from the inception of the databases to December 14, 2022. Using Microsoft Excel 2019 software, the database was established to collect the use frequency of acupoint, meridian tropism, location and pattern-acupoint relationship. SPSS Modeler 18.0 Apriori algorithm was adopted to conduct the association rule analysis, Cytoscape3.7.2 software was used to plot the complex co-occurrence network map; and SPSS Statistics 26.0 was adopted to perform hierarchical cluster analysis on high-frequency acupoints and a tree diagram was drawn. A total of 163 articles were included, and 167 core acupoint prescriptions and 74 pattern-associated acupoint prescriptions were extracted, involving 92 acupoints, with a cumulative frequency of 1 223 times. The top five acupoints with the highest use frequency were Guanyuan (CV 4), Sanyinjiao (SP 6), Zhongji (CV 3), Zigong (EX-CA 1) and Qihai (CV 6). The selected acupoints were mostly distributed in the chest, abdomen and lower limbs; and the involved meridians included the conception vessel, the spleen meridian of foot-taiyin and the stomach meridian of foot-yangming. The acupoint compatibility of high frequency referred to Guanyuan (CV 4) - Sanyinjiao (SP 6), Guanyuan (CV 4) - Zhongji (CV 3), and Guanyuan (CV 4) - Zigong (EX-CA 1). The close association was presented among Guanyuan (CV 4), Sanyinjiao (SP 6), Qihai (CV 6) and Zhongji (CV 3), which had the strongest connection with the other acupoints; among the top 25 acupoints with the highest use frequency, 5 acupoint prescriptions with high frequency were obtained by the cluster analysis. Guanyuan (CV 4), Qihai (CV 6), Sanyinjiao (SP 6), Zigong (EX-CA 1) and Zhongji (CV 3) were selected for cold and blood stagnation; Guanyuan (CV 4), Sanyinjiao (SP 6), Zhongji (CV 3), Dahe (KI 12) and Taixi (KI 3) for kidney deficiency and blood stagnation; Zhongji (CV 3), Guanyuan (CV 4), Sanyinjiao (SP 6), Xuehai (SP 10) and Diji (SP 8) for qi and blood stagnation; Qihai (CV 6), Guanyuan (CV 4), Zusanli (ST 36), Xuehai (SP 10), and Zigong (EX-CA 1) for qi deficiency and blood stagnation; Sanyinjiao (SP 6), Fenglong (ST 40), Zhongliao (BL 33), Ciliao (BL 32) and Xialiao (BL 34) for interaction of phlegm and stasis; and Daheng (SP 15), Guanyuan (CV 4), Zhongji (CV 3), Qihai (CV 6) and Zhongwan (CV 12) for retention of damp and heat. The core acupoints are Guanyuan (CV 4), Sanyinjiao (SP 6), Zhongji (CV 3), Qihai (CV 6) and Zigong (EX-CA 1) in treatment of endometriosis with acupuncture and moxibustion. Six patterns/syndromes are involved in clinical practice. In terms of the properties, functions and indications, the supplementary acupoints are selected on the basis of the core acupoints for different patterns/sydnromes of the disease.

A Dynamic Evolutionary Analysis of the Vulnerability of Global Food Trade Networks

The global food trade network (FTN) is a critical infrastructure for achieving the Sustainable Development Goals (SDGs). The FTN’s vulnerability to geopolitical conflicts, public health crises, and climate change events directly impacts food security and the ability to meet the SDGs. This study aims to analyze the dynamic evolution of the vulnerability of FTN, focusing on the period from 2000 to 2022, to aim for strategies for enhancing the resilience and sustainability of the global food system. Based on complex network analysis, we examine the structural characteristics and evolution of FTN for four major crops: soybeans, wheat, rice, and maize. We identify a trend towards increased network density and regionalization, with a decline in average shortest path length (ASPL) and an increase in the average clustering coefficient (ACC). These changes indicate a shift towards a more interconnected and resilient FTN in response to various shocks, including the COVID-19 pandemic and the Russia–Ukraine conflict. The findings suggest that the global FTN has adapted to increase resilience, which is essential for achieving the SDGs related to food security and sustainable development. The study’s insights can guide policy interventions to further strengthen the network against future shocks and promote global food security.

Muscle synergies and muscle networks in multiple frequency components in post-stroke patients

As a neural control strategy employed by the central nervous system to control movements, the extraction of muscle synergies from a broad range of electromyographic signals has been the focus. Nevertheless, recent coherence analysis demonstrates distinct roles played in low-frequency components in motor control. This suggests that muscle synergies extracted from the low-frequency components can capture movement characteristics as done in the full-band. To investigate this issue, muscle synergies were extracted from three distinct frequency components (20–450 Hz, 20–30 Hz, 30–450 Hz) during spatial reaching movements in a group of healthy and stroke subjects. Synergy similarities were computed both within frequencies and between groups, and complex network analysis was conducted based on the group-averaged synergies. Results showed that the number and structure of muscle synergies, as well as network metrics, identified from the full-band (20–450 Hz) and low-frequency (20–30 Hz) exhibited high similarity between groups. These patterns diverged from those observed in high-frequency (30–450 Hz). The stroke did not induce significant alterations in muscle synergies. However, higher network metrics were identified in the full-band for one synergy. This study demonstrated the capacity of low-frequency components to perform muscle synergy analysis. These findings can help better understand the neural drive and motor control, potentially advancing the assessment of motor function and rehabilitation strategies.

Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network

Feature selection is a crucial step in fault diagnosis. When rolling bearings are susceptible to compound faults, causal relationships are hidden within the signal features. Complex network analysis methods provide a tool for causal relationship modeling and feature importance assessment. Existing studies mainly focus on unweighted networks, overlooking the impact of the strength of causal relationships on feature selection. To address this issue, we propose a compound fault feature selection method based on the causal feature weighted network. First, we construct a weighted network using the incremental association Markov blanket discovery algorithm and Pearson correlation coefficient. Then, we quantify the importance of features by treating node strength as a centrality index and rank them to partition the feature subset. Finally, the optimal feature subset is obtained through a neural network with the accuracy of compound fault diagnosis as the threshold. Analysis of public datasets and comparative experiments demonstrate the advantages of our method. Compared to existing research, our method not only effectively reduces the number of optimal feature subsets to 11 but also improves the accuracy of compound fault diagnosis to 95.2%. Furthermore, we employ the SHapley Additive exPlanations to interpret the contribution of each feature in the optimal subset to the accuracy of compound fault diagnosis. This provides reference from both physical and network perspectives to feature selection and compound fault diagnosis in rolling bearings in practical working conditions.

Pain experience reduces social avoidance to others in pain: a c-Fos-based functional connectivity network study in mice.

Pain experience increases individuals' perception and contagion of others' pain, but whether pain experience affects individuals' affiliative or antagonistic responses to others' pain is largely unknown. Additionally, the neural mechanisms underlying how pain experience modulates individuals' responses to others' pain remain unclear. In this study, we explored the effects of pain experience on individuals' responses to others' pain and the underlying neural mechanisms. By comparing locomotion, social, exploration, stereotyped, and anxiety-like behaviors of mice without any pain experience (naïve observers) and mice with a similar pain experience (experienced observers) when they observed the pain-free demonstrator with intraperitoneal injection of normal saline and the painful demonstrator with intraperitoneal injection of acetic acid, we found that pain experience of the observers led to decreased social avoidance to the painful demonstrator. Through whole-brain c-Fos quantification, we discovered that pain experience altered neuronal activity and enhanced functional connectivity in the mouse brain. The analysis of complex network and graph theory exhibited that functional connectivity networks and activated hub regions were altered by pain experience. Together, these findings reveal that neuronal activity and functional connectivity networks are involved in the modulation of individuals' responses to others' pain by pain experience.

Collaboration network analysis for the Arctic issues based on a collection of international collaborative events

In recent years, global warming has accelerated the melting of Arctic ice. The value of the Arctic has been increasing in various aspects and has received widespread attention from the international community. The significance of globalization of the Arctic issue has become more and more prominent, the member states of the Arctic Council, observer states, and non-Arctic countries that have intensively developed and updated their respective Arctic strategies have actively participated about Arctic issues. The international collaboration has gradually become an effective way of addressing Arctic issues. In order to explore the pattern behind the collaborative events about Arctic issues, with the application of complex network analysis, the international collaborative events about Arctic issues from 2013 to 2023 are selected to construct the collaborative networks. Then the collaborative networks are quantitatively analyzed by the relevant parameters of complex network. These parameters for quantitative analysis include network density, average path length, clustering coefficient, degree centrality, closeness centrality and betweenness centrality. The results of the study reveal the changes in international collaboration about Arctic issues from 2013 to 2023.

Community hiding: Completely escape from community detection

Community detection algorithms play an important role in revealing and analyzing complex network structures. However, when these algorithms are put into practice, privacy concerns often arise, especially when community nodes wish to safeguard their identities from exposure. This interaction between network analysis and privacy issues leads to what we refer to as the “community hiding problem.” In the context of concealing specific target communities, popular methods primarily focus on preventing community detection algorithms from accurately identifying the entire original community, resulting in partial concealment. This study formally addresses the challenge of achieving complete concealment of target communities. We introduce key metrics such as escape scores, dispersion scores, and hiding scores to precisely define the problem. Additionally, we design a metric, denoted as M-value, to evaluate the effectiveness of concealing target communities from multiple perspectives. To tackle this challenge, we employ a genetic algorithm that leverages previous knowledge to maximize the concealment of the target community while minimizing changes to link connections. We validate our approach through extensive experiments with various real-world datasets, demonstrating that our algorithm outperforms several state-of-the-art baseline algorithms across multiple metrics. Visual representations of our method confirm its ability to effectively hide target communities while minimally affecting the broader network's community structure, strengthening the inherent effectiveness of our community hiding approach. Furthermore, we assess the adaptability of our modified network with different community detection algorithms, consistently demonstrating effective concealment even when these algorithms are extended. This emphasizes the robustness and generality of our proposed approach in various algorithmic scenarios.

Complex networks approach to study comorbidities in patients with unruptured intracranial aneurysms

The role of complex network analysis in patients with diagnosis of unruptured intracranial aneurysm is unexplored. The objective of this study is to assess the applicability of this methodology in aneurysm patients. We retrospectively analyze comprehensive unbiased local digital data of a large number of patients treated for any reason between January 2004 and July 2019. We apply an age-cohort approach to a total of 628,831 patients and construct the diagnostic history of each patient—and include the information how old the patient was when diagnosed for the first time with each diagnosis coded according to International Classification of Diseases. For each cohort of age within a 10 year interval and for each gender, we construct a statistically validated comorbidity network and focused on crucial comorbidity links that the aneurysm code has to other disease codes within the whole network. For all cohorts of different age and gender, the analysis shows that 267 diagnose codes have nearest neighbour statistically validated links to unruptured aneurysm ICD code. Among the 267 comorbidities, 204 (76%) were found in patients aged from 40 to 69-years old. Patterns of connectivity with aneurysms were found for smoking, hypertension, chronic obstructive pulmonary disease, dyslipidemia, and mood disorders. A few uncommon connections are also detected in cohorts of female patients. Our study explored the applicability of network analysis and statistical validation in aneurysm observational study.

A complex network perspective on spatiotemporal propagations of extreme precipitation events in China

Extreme precipitation events (EPEs) cause catastrophic losses to human lives and social economy. Focusing on the wet season from May to October, this paper formulates the complex network to characterize the spatiotemporal propagations of EPEs over 218 river basins in China. Specifically, based on the daily Multi-Source Weighted-Ensemble Precipitation version 2, the event synchronization along with significance test facilitates the complex network of EPEs from 1979 to 2020. By characterizing the topology of EPEs, the results show that synchronous EPEs generally occur over the Hetao Area of Yellow River Basin, Haihe River Basin and Northeast China. The complex network of EPEs classifies the 218 river basins into six communities. In May and June, more than 50 % of EPEs generally occur in South China. From July to September, over 60 % of EPEs tend to be observed in the Huang-Huai-Hai Plain, upper-middle reaches of Yellow River Basin, Northeast China and Southwest China. In October, about 65 % of EPEs happen in South and Southwest China. In the meantime, less than 10 % of EPEs occur in Northwest China. Among the synchronous EPEs starting in late June or early July and peaking in late July to early August, there exist propagations along the Yellow River, Haihe, Liaohe and Songhua River Basin within one to eight days. Eastward moisture transport along this pathway within four to six days drives the propagation of synchronous EPEs. The complex network analysis of this paper provides useful information for early warning and forecasting of EPEs and flood hazards.

The competitiveness of mega liner shipping companies on Eurasia routes: consider the impact of the CR-Express services

ABSTRACT The CR-Express has enhanced international container transport services and altered the competitive landscape of mega liner shipping companies (MLSCs) in the Eurasia container transport market. Given the continuous growth of the CR-Express’s market share, this study establishes a novel evaluation system to assess the competitiveness of the MLSCs, measuring their performance in the Eurasia container transport market and analyzing how their competitiveness transforms when they offer CR-Express service. To accurately calculate the four competitiveness indicators—namely spatial coverage, level of route customization, directness of shipping, and alternative routes—we constructed complex networks representing the Eurasia container transport system of the evaluated MLSCs across various topologies and conducted complex network analysis. The evaluation results indicate that Maersk, CMA CGM, and COSCO are the three most competitive liner shipping companies in the Eurasia container transport market before Maersk and COSCO’s involving in the CR-Express. The CR-Express has significantly bolstered the competitiveness of Maersk and COSCO, with COSCO surpassing Maersk and claiming the top globally after offering CR-Express service. However, the CR-Express, launched incrementally by the MLSCs, have not been seamlessly integrated into their overall maritime shipping networks, resulting in a sparse overall container transport network.

A View on technology and controls for epidemic preparedness and the growth of COVID-19 disease in Saudi Arabia

This work emphasizes that the best chance of ending the pandemic lies in the Measures that can help us respond to these epidemic threats more effectively than before in the scope of protection from epidemic risks that we were not aware of before the outbreak of the disease, which is to benefit from the study of previous experiences. The work show the importance of technology about how to prepare for and handle any pandemic by discussing a successful case from the past. We examine Saudi Arabia's COVID-19 pandemic response experiences. We proposed a case evolution study from the southern region of Jazan to demonstrate the consequences of these techniques. Data on the daily Jazan COVID-19 infection curve collected from July 1st to September 30th, 2020, was processed using multiple Python software modules, such as encoder-decoder LSTM, 1D-CNN, and Complex network analysis of time series to identify changes within the infection curve's structure. A Gaussian modeling study was performed to compare the data movement with the protection from epidemic risks computed by stratification based on the data group.The network analysis shows that hubs are stable while medium and low-degree nodes are unstable. Also, from the perspective of Gaussian modeling, the COVID-19 infection curve parts indicate that the pandemic in Saudi Arabia is about to peak and move into the second (declining) half of the bell-shaped distribution.Technology in many areas and internet access make Saudi Arabia's social separation strategies a pandemic-eradication success story

How do learners’ content network characteristics evolve in cMOOC from the perspective of clustering and comparing learners?

ABSTRACT The content network of cMOOC learners reflects the interactions between learners and valuable external information, and the learners in connectivist learning are diverse. Therefore, it's necessary to make a clear understanding of learners' participation types and content network characteristics. This study proposed a content network characteristics analysis model that included five indexes and corresponding computing methods. 251 learners were included and their interaction data were analyzed through cluster analysis, natural language processing and complex network analysis. The results demonstrated that there were four types of learners, namely “Connecters”, “Followers”, “Likers”, and “Commentators”. There were differences in the content network characteristics among the four types of learners, and the evolution patterns varied. Overall, the values of content network characteristics of Connectors were greatest, and the values of Likers were the lowest. Except for Likers, the content network characteristics of the other three types of learners all exhibited periodic evolution patterns accompanied by the key teaching events, but they varied in evolution trends, specifically manifesting as a spiral upwards trend for Connecters, a gradual trend for Followers, and a steady trend for Commentators. Finally, this study provides valuable suggestions for cMOOC designers and facilitators to support different types of cMOOC learners.

Identifying Communities with Modularity Metric Using Louvain and Leiden Algorithms

Over the past 20 years, there has been a significant increase in publication in complex network analysis research, especially in community detection. Many methods were proposed to identify community structure. Each community identification algorithm has strengths and weaknesses due to the complexity of information. Among them, the optimisation methods are widely focused on. This paper focuses on an empirical study of two community detection algorithms based on agglomerative techniques using modularity metric: Louvain and Leiden. In this regard, the Louvain algorithm has been shown to produce a bad connection in the community and disconnected when executed iteratively. Therefore, the Leiden algorithm is designed to successively resolve the weaknesses. Performance comparisons between the two and their concept were summarised in detail, as well as the step-by-step learning process of the state-of-the-art algorithms. This study is important and beneficial to the future study of interdisciplinary data sciences of network analysis. First, it demonstrates that the Leiden method outperformed the Louvain algorithm in terms of modularity metric and running time. Second, the paper displays the use of these two algorithms on synthetic and real networks. The experiment was successful as it identified better performance, and future work is required to confirm and validate these findings.

An integrative dynamical perspective for graph theory and the analysis of complex networks.

Built upon the shoulders of graph theory, the field of complex networks has become a central tool for studying real systems across various fields of research. Represented as graphs, different systems can be studied using the same analysis methods, which allows for their comparison. Here, we challenge the widespread idea that graph theory is a universal analysis tool, uniformly applicable to any kind of network data. Instead, we show that many classical graph metrics-including degree, clustering coefficient, and geodesic distance-arise from a common hidden propagation model: the discrete cascade. From this perspective, graph metrics are no longer regarded as combinatorial measures of the graph but as spatiotemporal properties of the network dynamics unfolded at different temporal scales. Once graph theory is seen as a model-based (and not a purely data-driven) analysis tool, we can freely or intentionally replace the discrete cascade by other canonical propagation models and define new network metrics. This opens the opportunity to design-explicitly and transparently-dedicated analyses for different types of real networks by choosing a propagation model that matches their individual constraints. In this way, we take stand that network topology cannot always be abstracted independently from network dynamics but shall be jointly studied, which is key for the interpretability of the analyses. The model-based perspective here proposed serves to integrate into a common context both the classical graph analysis and the more recent network metrics defined in the literature which were, directly or indirectly, inspired by propagation phenomena on networks.

Utilizing data mining techniques to analyze traditional Chinese medicine patterns in patients with acute cardiorenal syndrome

AbstractObjectiveThis study intends to investigate the medication patterns of traditional Chinese medicine (TCM) for the treatment of acute cardiorenal syndrome (ACRS) through clinical data mining.MethodsA retrospective study was conducted at China‐Japan Friendship Hospital, encompassing hospitalized patients with ACSR admitted from June 1, 2013, to June 30, 2022. Patient treatment records, including TCM prescriptions, were gathered to establish a comprehensive prescription database. The prescriptions underwent analysis using the ancient and modern medical case cloud platform, incorporating the frequency, properties and flavors and channel tropism of each herbs, and clustering patterns, combination relationships, and complex network analysis of the whole.ResultsA total of 330 prescriptions from eligible hospitalized patients were included, which totally involves 324 kinds of different herbs. The therapeutic effects primarily emphasized descending qi and eliminating turbidity, promoting diuresis and reducing swelling, drying dampness and dispelling phlegm, and promoting the ascension of Yang Qi. The properties were largely warm, netural, slightly cool and cold, with warm being predominant. The flavors were chiefly sweet, spicy, and bitter, with sweetness being the most common. The major channel tropism were the lung and spleen meridians, followed by the stomach, liver, heart, and kidney meridians. Combination analysis identified 30 pairs of combinations, with higher associations found in combinations such as Jiegeng, Huangqi, Chaihu, Baizhu, Zhimu, and Dangshen. Cluster analysis categorized high‐frequency herbs into 4 groups: Gualou–Fabanxia–Chuanxiong–Chishao, Fuling–Baizhu–Zhigancao–Chenpi, Maidong–Guizhi–Huangqi–Chaihu–Shengma–Zhimu–Shanzhuyu, and Huangqi–Dangshen–Jiegeng–Chaihu–Shengma–Zhimu–Shanzhuyu. Complex network analysis revealed core prescriptions for treating ACRS, including 17 herbs.ConclusionsIn the treatment of ACRS, TCM herbs with the properties of warm and neturality, and the flavors of sweet and spicy are commonly utilized. The prescribed herbal formulas have the effect of descending qi and eliminating turbidity, tonifying qi, activating blood and resolving stasis, resolving phlegm and promoting water.

Towards a unified medical microbiome ecology of the OMU for metagenomes and the OTU for microbes

BackgroundMetagenomic sequencing technologies offered unprecedented opportunities and also challenges to microbiology and microbial ecology particularly. The technology has revolutionized the studies of microbes and enabled the high-profile human microbiome and earth microbiome projects. The terminology-change from microbes to microbiomes signals that our capability to count and classify microbes (microbiomes) has achieved the same or similar level as we can for the biomes (macrobiomes) of plants and animals (macrobes). While the traditional investigations of macrobiomes have usually been conducted through naturalists’ (Linnaeus & Darwin) naked eyes, and aerial and satellite images (remote-sensing), the large-scale investigations of microbiomes have been made possible by DNA-sequencing-based metagenomic technologies. Two major types of metagenomic sequencing technologies—amplicon sequencing and whole-genome (shotgun sequencing)—respectively generate two contrastingly different categories of metagenomic reads (data)—OTU (operational taxonomic unit) tables representing microorganisms and OMU (operational metagenomic unit), a new term coined in this article to represent various cluster units of metagenomic genes.ResultsThe ecological science of microbiomes based on the OTU representing microbes has been unified with the classic ecology of macrobes (macrobiomes), but the unification based on OMU representing metagenomes has been rather limited. In a previous series of studies, we have demonstrated the applications of several classic ecological theories (diversity, composition, heterogeneity, and biogeography) to the studies of metagenomes. Here I push the envelope for the unification of OTU and OMU again by demonstrating the applications of metacommunity assembly and ecological networks to the metagenomes of human gut microbiomes. Specifically, the neutral theory of biodiversity (Sloan’s near neutral model), Ning et al.stochasticity framework, core-periphery network, high-salience skeleton network, special trio-motif, and positive-to-negative ratio are applied to analyze the OMU tables from whole-genome sequencing technologies, and demonstrated with seven human gut metagenome datasets from the human microbiome project.ConclusionsAll of the ecological theories demonstrated previously and in this article, including diversity, composition, heterogeneity, stochasticity, and complex network analyses, are equally applicable to OMU metagenomic analyses, just as to OTU analyses. Consequently, I strongly advocate the unification of OTU/OMU (microbiomes) with classic ecology of plants and animals (macrobiomes) in the context of medical ecology.

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.