Framework Of Network Theory Research Articles

Deciphering the topological landscape of glioma using a network theory framework

Glioma stem cells have been recognized as key players in glioma recurrence and therapeutic resistance, presenting a promising target for novel treatments. However, the limited understanding of the role glioma stem cells play in the glioma hierarchy has drawn controversy and hindered research translation into therapies. Despite significant advances in our understanding of gene regulatory networks, the dynamics of these networks and their implications for glioma remain elusive. This study employs a systemic theoretical perspective to integrate experimental knowledge into a core endogenous network model for glioma, thereby elucidating its energy landscape through network dynamics computation. The model identifies two stable states corresponding to astrocytic-like and oligodendrocytic-like tumor cells, connected by a transition state with the feature of high stemness, which serves as one of the energy barriers between astrocytic-like and oligodendrocytic-like states, indicating the instability of glioma stem cells in vivo. We also obtained various stable states further supporting glioma’s multicellular origins and uncovered a group of transition states that could potentially induce tumor heterogeneity and therapeutic resistance. This research proposes that the transition states linking both glioma stable states are central to glioma heterogeneity and therapy resistance. Our approach may contribute to the advancement of glioma therapy by offering a novel perspective on the complex landscape of glioma biology.

Longitudinal relations between non-suicidal self-injury and both depression and anxiety among senior high school adolescents: a cross-lagged panel network analysis.

Comorbidity between non-suicidal self-injury (NSSI) and depression and anxiety was common. In the framework of network theory, the examination of directionality and gender differences in longitudinal relationships at the symptom level made a significant contribution to the understanding of comorbidity. Therefore, this study employed cross-lagged panel network analysis to investigate the longitudinal interrelations between NSSI and depression and anxiety in Chinese adolescents, with a focus on gender differences. The study was conducted with a sample of 884 senior high school students (F/M: 481/403; mean age: 15.19±0.48 years) from Jinchang City, Gansu Province, China. All respondents completed the Adolescent Non-Suicidal Self-Injury Assessment Questionnaire and the two subscales (depression and anxiety) of the Brief Symptom Inventory at two intervals. The data were estimated in R 4.2.0 to construct the cross-lagged panel network (CLPN). The CLPN results uncovered the gender differences. For boys, self-hitting and feeling scared emerged as central symptoms. Cutting predicted subsequent feelings of sadness (β=-0.57), tension (β=-0.52) and indifference (β=-0.49), potentially serving as a bridge connecting NSSI to depression and anxiety. For girls, biting themselves and feeling scared were central symptoms. Carving and skin rubbing predicted subsequent feelings of indifference (β=-0.31, -0.21), bridging NSSI to depression and anxiety. In addition, feeling scared emerged as the key bridge symptom connecting depression and anxiety. The findings showed the gender-specific developmental characteristics of the directional relations between NSSI and depression and anxiety at the symptom level. They provided new insights into the comorbidity of NSSI and depression and anxiety, carrying important implications for the screening and intervention of adolescent NSSI.

Kinematic network of joint motion provides insight on gait coordination: An observational study on Parkinson's disease

The analysis of gait kinematics requires to encode and collapse multidimensional information from multiple anatomical elements. In this study, we address this issue by analyzing the joints' coordination during gait, borrowing from the framework of network theory. We recruited twenty-three patients with Parkinson's disease and twenty-three matched controls that were recorded during linear gait using a stereophotogrammetric motion analysis system. The three-dimensional angular velocity of the joints was used to build a kinematic network for each participant, and both global (average whole-body synchronization) and nodal (individual joint synchronization, i.e., nodal strength) were extracted. By comparing the two groups, the results showed lower coordination in patients, both at global and nodal levels (neck, shoulders, elbows, and hips). Furthermore, the nodal strength of the left elbow and right hip in the patients, as well as the average joints' nodal strength were significantly correlated with the clinical motor condition and were predictive of it. Our study highlights the importance of integrating whole-body information in kinematic analyses and the advantages of using network theory. Finally, the identification of altered network properties of specific joints, and their relationship with the motor impairment in the patients, suggests a potential clinical relevance for our approach.

Inter-organizational collaboration after institutional reform in China: A perspective based on the revision of the emergency plan

In an era marked by increasingly complex risks and extreme disasters, nations are progressively leaning towards inter-organizational collaboration as a means to adapt and respond effectively. Guided by the principles of holistic governance and comprehensive emergency management, the establishment of comprehensive coordination institutions has emerged as a vital strategy for enhancing a country’s emergency response capabilities. Nevertheless, the transformation of inter-organizational cooperation networks following institutional reform remains shrouded in ambiguity. This research, employing a blend of quantitative and qualitative analyses within a network theory framework, delves into the shifts within inter-organizational cooperation networks subsequent to institutional reform. By scrutinizing the evolution of emergency plan networks at provincial, municipal, and county levels before and after China’s institutional reform and drawing insights from emergency plan revision personnel, this study unveils a noteworthy trend. Specifically, the inter-organizational collaboration network in China’s emergency management sector has gravitated towards a unified command model, fostering heightened cross-functional cooperation and optimizing network structure. The findings of this study not only shed light on the post-reform dynamics of inter-organizational cooperation networks in China but also offer valuable insights for other countries and regions seeking to develop effective emergency management systems. As the global landscape continues to evolve, understanding and adapting to these changes in network dynamics becomes imperative for bolstering disaster response capabilities worldwide.

How Should Enterprises Quantify and Analyze (Multi-Party) APT Cyber-Risk Exposure in their Industrial IoT Network?

Industrial Internet of Things (IIoT) networks (e.g., a smart grid industrial control system) are increasingly on the rise, especially in smart cities around the globe. They contribute to meeting the day-to-day needs (e.g., power, water, manufacturing, transportation) of the civilian society, alongside making societal businesses more efficient, productive, and profitable. However, it is also well known that IoT devices often operate on poorly configured security settings. This increases the chances of occurrence of (nation-sponsored) stealthy spread-based APT malware attacks in IIoT networks that might go undetected over a considerable period of time. Such attacks usually generate a negative first-party QoS impact with financial consequences for companies owning such IIoT network infrastructures. This impact spans (i.e., aggregates) space (i.e., the entire IIoT network or a sub-network) and time (i.e., duration of business disruption), and is a measure of significant interest to managers running their businesses atop such networks. It is of little use to network resilience boosting managers if they have to wait for a cyber-attack to happen to gauge this impact. Consequently, one of the questions that intrigues us is: can managers estimate this first-party impact prior to APT cyber-attack(s) causing financial damage to companies? In this paper, we propose the first computationally efficient and quantitative network theory framework to (a) characterize this first-party impact apriori as a statistical distribution over multiple attack configurations in a family of malware-driven APT cyber-attacks specifically launched on businesses running atop IIoT networks, (b) accurately compute the statistical moments (e.g., mean) of the resulting impact distribution, and (c) tightly bound the accuracy of worst-case risk estimate of such a distribution - captured through the tail of the distribution, using the Conditional Value at Risk (CVaR) metric. In relation to (a) above, our methodology extends the seminal Factor Analysis of Information Risk (FAIR) cyber-risk quantification methodology that does not explicitly account for network interconnections among system-risk contributing variables. We validate the effectiveness of our theory using trace-driven Monte Carlo simulations based upon test-bed experiments conducted in the FIT IoT-Lab. We further illustrate quantitatively that even if spread-based APT cyber-attacks induce a statistically light-tailed first-party cyber-loss distribution on an IIoT networked enterprise in the worst case, the aggregate multi-party cyber-risk distribution incurred by the same enterprise in supply-chain ecosystems could be heavy-tailed. This will pose significant market scale-up challenges to cyber-security improving commercial cyber (re-)insurance businesses. We subsequently propose managerial action items to mitigate the first-party cyber-risk exposure emanating from any given IIoT driven enterprise.

Longitudinal network relations between symptoms of problematic internet game use and internalizing and externalizing problems among Chinese early adolescents

ObjectiveThere has been growing evidence of comorbidity between problematic internet game use and internalizing and externalizing problems in young people. However, little is known about the directionality and gender differences in these longitudinal relations at the symptoms level in the framework of network theory among youth. This study estimated the longitudinal relations between the symptoms of problematic internet game use, internalizing and externalizing problems, and the gender differences of these relations in Chinese youth using cross-lagged panel network modeling (CLPN). MethodsA sample of 1269 Chinese youth (M age = 10.35 years) participated in this study semi-annually at two time points. CLPN analysis was used to calculate the network model of problematic internet game use and internalizing and externalizing problems to explore bridge symptoms and find transmission pathways between problematic internet game use and internalizing and externalizing problems. ResultsThe CLPN revealed significant gender differences. For boys, depressed mood, which leads to relationships turning sour in order to play online games, bridges the relations between internalizing symptoms and problematic internet game use. For girls, irritability is the central predictive symptom, causing a range of problems related to problematic internet game use, which can, in turn, lead to fights or feelings of worthlessness. However, the effect sizes for the pathways between problematic internet game use and internalizing/externalizing problems were relatively weak, and the comorbidity between their relations should not be over-interpreted. ConclusionsThe current findings provide new evidence for understanding the directional relationship between the central characteristics of problematic internet game use and internalizing and externalizing problems in boys and girls. Gender-specific interventions targeting the central symptoms of internalizing and externalizing problems and problematic internet game use can help mitigate the vicious cycle of comorbidity among adolescents.

SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework.

Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed for phylogenetic inference of transmission histories, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, although common source outbreaks violate this assumption. We propose a maximum likelihood framework, SOPHIE, based on the integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modeled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity, and accurately infers transmissions without case-specificepidemiological data.

Cross-Informant Compatibility of Depression Symptoms in Children: A Network Approach

Utilizing multiple informants to assess children’s depressive symptoms increases diagnostic accuracy, reliability, and validity of inferences. However, previous studies have found low to moderate agreement among informants. We applied network statistics to gain insight into children and their mothers’ differential perceptions of depressive symptoms. The sample included children and mother dyads (n = 185) who applied to psychotherapy services at an outpatient university clinic. Mothers filled out the Child Behavior Checklist, which includes a depression subscale, and children filled out the Children’s Depression Inventory. We computed association networks for thirteen depressive symptoms separately for children and mothers using the graphical LASSO. Sadness had the highest strength centrality in the networks of both children and mothers, but the pattern of connectivity and centrality of other symptoms differed. We discussed our findings within the framework of network theory.

Economic component interactions between projects in urban regeneration plans: A network theory framework for plan quality evaluation applied to three French metropolitan cities in Normandy

The evaluation of urban regeneration plan quality is a key issue for scholars and practitioners who are searching for a good urban regeneration plan. While a regeneration plan is intuitively perceived as a bundle of different rationally selected projects, there is no indication of the degree of interrelations and differences among projects in the literature. This study addresses those interactions in the economic development dimension and highlights the implicit economic component networking framework embedded in a plan's “vision.” Using urban economic development theory and the quality plan evaluation method, any project in a plan is classified according to its main urban economic purpose, servicing the “base” approach, fostering “agglomeration” economies, or promoting “clusters” between firms. The presented methodology is upward on a coding process assisted by word counting and downward on a network analysis from which the plan networking pattern is built. The method is applied to three neighboring Norman cities in France, Caen, Le Havre, and Rouen, each of which was involved in its own regeneration programs between 2006 and 2013. The projections of the economic path performances during 2013–2017 stressed the networking approach's accuracy and interest using indicators related to the three urban economic components.

A method to characterize the social cascading damage processes of disasters using media information

Constantly advancing media information is a key data source to characterize the social cascading damage processes following natural hazards. However, media information tends to include a large sample size but low information density. In consideration of these properties, the aim of this study is to develop a new method for media-based information characterizing social cascading damage processes. In developing the method, a network theory framework was constructed to systematically integrate media information and its characterization. The method has two analytical components: a disaster damage network systematically inputting media information and network analysis using the concept of degree centrality. The developed method was applied to the record-breaking 2018 heavy rain disaster in western Japan, employing newspaper articles as media information sources. The study identified the critical disaster events and their relationships. This case study demonstrates that our method will benefit policymakers by providing them with potential fundamental information to support disaster management.

Delimiting the boundaries of sesamoid identities under the network theory framework.

Sesamoid identity has long been the focus of debate, and how they are linked to other elements of the skeleton has often been considered relevant to their definition. A driving hypothesis of our work was that sesamoids’ nature relies deeply on their connections, and thus we propose an explicit network framework to investigate this subject in Leptodactylus latinasus (Anura: Leptodactylidae). Through the dissection of L. latinasus’ skeleton, we modeled its anatomical network where skeletal elements were considered nodes while joints, muscles, tendons, and aponeurosis were considered links. The skeletal elements were categorized into canonical skeletal pieces, embedded sesamoids, and glide sesamoids. We inquired about the general network characterization and we have explored further into sesamoid connectivity behavior. We found that the network is structured in a modular hierarchical organization, with five modules on the first level and two modules on the second one. The modules reflect a functional, rather than a topological proximity clustering of the skeleton. The 25 sesamoid pieces are members of four of the first-level modules. Node parameters (centrality indicators) showed that: (i) sesamoids are, in general terms, peripheral elements of the skeleton, loosely connected to the canonical bone structures; (ii) embedded sesamoids are not significantly distinguishable from canonical skeletal elements; and (iii) glide sesamoids exhibit the lowest centrality values and strongly differ from both canonical skeletal elements and embedded sesamoids. The loose connectivity pattern of sesamoids, especially glides, could be related to their evolvability, which in turn seems to be reflected in their morphological variation and facultative expression. Based on the connectivity differences among skeletal categories found in our study, an open question remains: can embedded and glide sesamoids be defined under the same criteria? This study presents a new approach to the study of sesamoid identity and to the knowledge of their morphological evolution.

"Their reward will be a lovely daughter": The Mobilization of "Hard to Adopt" and the Portrayal of Adoption as a Gift

We analyzed 4300 advertisements of children featured in the Today's Child column, a daily written by Helen Allen in The Toronto Telegram and The Toronto Star 1964–1982, to illustrate the movement of "hard to adopt" children through the network of adoption. Children featured in the column were characterized by adoptable or unadoptable traits and made into objects. Through an actor network theory framework, we understand how the hierarchy of specific characterizations of "hard to adopt" reached the Canadian public.

Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach

BackgroundIdentification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance.MethodsThe protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network.ResultsThe PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest.ConclusionsThe popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.

A patient-centric analysis to identify key influences in allergic rhinitis management

Allergic rhinitis (AR) is increasingly becoming a patient self-managed disease. Just under 70% of patients purchasing pharmacotherapy self-select their treatment with no health-care professional intervention often resulting in poor choices, leading to suboptimal management and increased burden of AR on the individual and the community. However, no decision is made without external, influencing forces. This study aims to determine the key influences driving patients’ decision-making around AR management. To accomplish this aim, we utilised a social network theory framework to map the patient’s AR network and identify the strength of the influences within this network. Adults who reported having AR were interviewed and completed an AR network map and AR severity and quality of life questionnaires. Forty one people with AR completed the study. The AR networks of the participants had a range of 1–11 influences (alters), with an average number of 4 and a median of 5. The larger the impact of AR on their quality of life, the greater the number of alters within their network. The three most commonly identified alters were, general practitioners, pharmacists and the participants’ ‘own experience’. The strength of the influence of health-care professionals (HCPs) was varied. The proportion of HCPs within the AR network increased as the impact of AR on their quality of life increased. By mapping the AR network, this study demonstrated that there are multiple influences behind patient’s decisions regarding AR management but the role of the HCP cannot be dismissed.

Fractal rules in brain networks: Signatures of self-organization

We study brain network data of three species, namely, C. elegans, cat and macaque monkey within the framework of network theory and Potts Hamiltonian model, and explore rich fractal nature in it, which could be an important signature of self-organization, and a simple rule to be obeyed in complex patterns of brain networks. Further, this fractal behaviors in topological parameters of brain networks at various network levels could be an indicator of systems level organization in complicated brain functionality. Again, Rich-club formation of leading hubs in brain networks becomes unpredictable as one goes down to different levels of organization. The popularity of these leading hubs in main modules or sub-modules also gets changed at different network levels, with varied attitudes at each level. Moreover, distribution of edges, which involves intra- and inter-modular/sub-modular interactions, inherited from one level of organization to another level follows fractal law. In addition to this, the Hamiltonian function at each network level, which may correspond to the energy cost in network organization at that level, shows fractal nature. Significant motifs, which are building blocks of networks and related to basic functionalities, in brain networks is found to be triangular motif, and its probability distribution at various levels as a function of size of modules or sub-modules follows fractal law.

Some characteristics of supernetworks based on unified hybrid network theory framework

Comparing with single complex networks, supernetworks are more close to the real world in some ways, and have become the newest research hot spot in the network science recently. Some progresses have been made in the research of supernetworks, but the theoretical research method and complex network characteristics of supernetwork models are still needed to further explore. In this paper, we propose three kinds of supernetwork models with three layers based on the unified hybrid network theory framework (UHNTF), and introduce preferential and random linking, respectively, between the upper and lower layers. Then we compared the topological characteristics of the single networks with the supernetwork models. In order to analyze the influence of the interlayer edges on network characteristics, the cross-degree is defined as a new important parameter. Then some interesting new phenomena are found, the results imply this supernetwork model has reference value and application potential.

Understanding cancer complexome using networks, spectral graph theory and multilayer framework

Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

Linking functional traits and network structure to concepts of stability

The objectives of this paper are to discuss stability as an emergent property of ecological systems, as well as promote a common language of network theory that will transfer among socio-ecological systems. Many researchers have understood the relationship between diversity and redundancy of species as network nodes, and even the response/effect trait framework aspect of this (i.e., overlapping traits associated with environmental response and ecosystem-level effects). Many researchers have also made the connections between network architecture and robustness, recognizing species interaction links as important aspects of architecture, but few papers incorporate concepts of diversity and redundancy for both nodes and links into aspects of architecture, and consequently network dynamics and stability. Using a common language framework of network theory and commonalities of network properties across outwardly different network types may reveal the existence of common constraints or determinants on network stability for ecological systems.

Exploring Opportunities for Engaging Public Health Organizations in Transportation Planning

Despite a call for collaboration, there remain challenges to engaging the public health community in the regional transportation planning process. Using an integrated framework of network theory and collaborative planning, we explore collaboration barriers and opportunities between transportation and public health communities. Analysis of primary data collected from a focus group and secondary data from 43 national case studies suggests that major perceived barriers include a lack of formal and informal mechanisms and knowledge management practices that facilitate collaboration. Coordination of policies at multiple levels, leadership, trust, and data sharing are recognized as important tools for collaboration. Implications are discussed.

Bridging, bonding and linking global entrepreneurs: the case of Sri Lanka

The purpose of this study is to understand the dynamics and the nature of successful international business relationships between Sri Lankan small business (SB) entrepreneurs and Sri Lankan emigrants in other countries. Using social capital and social network theory framework, this phenomenological study found the nature and underlying values of business relationships that glue Sri Lankan SB entrepreneurs and their emigrant counterparts. ‘Family-like relationships’ and embedded values lead to developing trusting partnerships that became the sources of augmenting knowledge, resources and internationalization of Sri Lankan SBs. The findings will be useful for international human resource development (IHRD) practitioners to develop training interventions to promote SB internationalization. The implications for IHRD practice and research are discussed. It is important for IHRD scholars to conduct in-depth qualitative research to gain a deeper understanding of hidden values and practices of various SB communities whereas practitioners can initiate actions to develop global SBs.