Changes In Network Structure Research Articles

Levodopa responsiveness and white matter alterations in Parkinson's disease: A DTI-based study and brain network analysis: A cross-sectional study.

Patients with Parkinson's disease (PD) present various responsiveness to levodopa, but the cause of such differences in levodopa responsiveness is unclear.Previous studies related the damage of brain white matter (WM) to levodopa responsiveness in PD patients, but no study investigated the relationship between the structural brain network change in PD patients and their levodopa responsiveness. PD patients were recruited and evaluated using the Unified Parkinson's Disease Rating Scale (UPDRS). Each patient received a diffusion tensor imaging (DTI) scan and an acute levodopa challenge test. The improvement rate of UPDRS-III was calculated. PD patients were grouped into irresponsive group (improvement rate<30%) and responsive group (improvement rate ≥ 30%). Tract-based spatial statistics (TBSS), deterministic tracing (DT), region of interest (ROI) analysis, and automatic fiber identification (AFQ) analyses were performed.The structural brain network was also constructed and the topological parameters were calculated. Fifty-four PD patients were included. TBSS identified significant differences in fractional anisotropy (FA) values in the corpus callosum and other regions of the brain. DT and ROI analysis of the corpus callosum found a significant difference in FA between the two groups. Graph theory analysis showed statistical differences in global efficiency, local efficiency, and characteristic path length. PD patients with poor responsiveness to levodopa had WM damage in multiple brain areas, especially the corpus callosum, which might cause disruption of information integration of the structural brain network.

Relationship between cross-linking network structure and phase change performances toward multifunctional epoxy/bio-based wax form-stable phase change materials

High encapsulation rate resulting from the good compatibility between cross-linking network and encapsulated solid–liquid phase change materials (SLPCMs) cannot necessarily result in high enthalpy value for the polymeric PCMs. However, the inner mechanism considering the above fact is not clear due to the lack of suitable model compound systems. In this work, a type of epoxy resin with crystalline alkyl side chains (named D18) and four types of amine curing agents (with similar structure but different polarity and molecular size) were primarily used as the model compound to achieve the encapsulation of beeswax (Bw, a kind of bio-based wax), and then the influence of the cross-linking network structure (using different curing agents) on the phase change performances of the D18/Bw polymeric form-stable PCMs (FSPCMs) was systematically studied. The results suggest that polarity effect and steric hindrance effect are the two factors that affect the phase change performances of the polymeric FSPCMs. Though the encapsulation rate of Bw is the highest (near 70 wt%) in the EPH-Y systems (using 1,3-Cyclohexanebismethylamine as the curing agent), it is the EPM-60 sample (using M−Xylylenediamine as the curing agent and containing 60 wt% Bw) that exhibited the highest enthalpy value of 146.2 J/g. Subsequently, different amount of multi-walled carbon nanotubes (CNT) was introduced to improve the thermal conductivity (TC) and achieve multifunctionalization of the EPM-60 sample. The TC, electromagnetic interference (EMI) shielding performance, and contact angle of the 30 wt% CNT modified EPM-60 sample can reach 1.37 W/mK, 37.4 dB, and 137°, while the enthalpy value can remain 101.1 J/g. This work is very helpful in clarifying the relationship between cross-linking network structure and phase change performances of polymeric FSPCMs, so as to providing theoretical guidance for the cross-linking network structure optimization in preparing other reliable polymeric FSPCMs with high enthalpy value, high reliability, and multifunctionalization.

Synaptic reshaping of plastic neuronal networks by periodic multichannel stimulation with single-pulse and burst stimuli.

Synaptic dysfunction is associated with several brain disorders, including Alzheimer's disease, Parkinson's disease (PD) and obsessive compulsive disorder (OCD). Utilizing synaptic plasticity, brain stimulation is capable of reshaping synaptic connectivity. This may pave the way for novel therapies that specifically counteract pathological synaptic connectivity. For instance, in PD, novel multichannel coordinated reset stimulation (CRS) was designed to counteract neuronal synchrony and down-regulate pathological synaptic connectivity. CRS was shown to entail long-lasting therapeutic aftereffects in PD patients and related animal models. This is in marked contrast to conventional deep brain stimulation (DBS) therapy, where PD symptoms return shortly after stimulation ceases. In the present paper, we study synaptic reshaping by periodic multichannel stimulation (PMCS) in networks of leaky integrate-and-fire (LIF) neurons with spike-timing-dependent plasticity (STDP). During PMCS, phase-shifted periodic stimulus trains are delivered to segregated neuronal subpopulations. Harnessing STDP, PMCS leads to changes of the synaptic network structure. We found that the PMCS-induced changes of the network structure depend on both the phase lags between stimuli and the shape of individual stimuli. Single-pulse stimuli and burst stimuli with low intraburst frequency down-regulate synapses between neurons receiving stimuli simultaneously. In contrast, burst stimuli with high intraburst frequency up-regulate these synapses. We derive theoretical approximations of the stimulation-induced network structure. This enables us to formulate stimulation strategies for inducing a variety of network structures. Our results provide testable hypotheses for future pre-clinical and clinical studies and suggest that periodic multichannel stimulation may be suitable for reshaping plastic neuronal networks to counteract pathological synaptic connectivity. Furthermore, we provide novel insight on how the stimulus type may affect the long-lasting outcome of conventional DBS. This may strongly impact parameter adjustment procedures for clinical DBS, which, so far, primarily focused on acute effects of stimulation.

Replication package for: Changes in Social Network Structure in Response to Exposure to Formal Credit Markets

Replication package for: Changes in Social Network Structure in Response to Exposure to Formal Credit Markets

Replication package for: Changes in Social Network Structure in Response to Exposure to Formal Credit Markets

Replication package for: Changes in Social Network Structure in Response to Exposure to Formal Credit Markets

A resilient network recovery framework against cascading failures with deep graph learning

Because of the increasing importance and dependencies of infrastructure networks and the potential for massive cascading failures in real-world network systems, maintenance optimization to effectively reduce system performance loss caused by diverse disruptions is of significant interest among researchers and practitioners. In this work, a new recovery framework was developed to rapidly identify important system components for maintenance to improve network resilience against cascading failures. This work provides distinct advantages to determine an optimal maintenance priority by combining real-time network structure importance with other maintenance prioritization based on customer preference. This approach adopts structural graph embedding and deep reinforcement learning to extract real-time network topology information (such as minimum vertex cover) to update the maintenance priority during the recovery process. Based on the case studies on synthetic networks and a US airport network, the proposed recovery framework with real-time network topology awareness shows better performance than other maintenance prioritization strategies regarding resilience enhancement. This work improves the understanding of how the changing network structure influences maintenance effects. It also provides insights of the practical usefulness of advanced deep learning on helping optimal maintenance prioritization to effectively reduce the intensity and extent of cascading failures.

In vitro and in vivo evaluation of surface functionalization of titanium with H2 O2 hydrothermal treatment and FGF-2.

The goals of the study were to investigate the effects on bone bioactivity of a titanium dioxide layer formed by hydrothermal oxidation of a titanium surface with hydrogen peroxide (H2 O2 ) and loading with fibroblast growth factor-2 (FGF-2) in vitro and in vivo. Ti-6Al-4V discs were hydrothermally oxidized with H2 O2 and then loaded with FGF-2. After cytotoxicity testing, Ti-6Al-4V mini-implants were subjected to the same treatment, and their osteogenic potential was evaluated histologically in a rat model. H2 O2 hydrothermal oxidation resulted in a dense porous network structure and hydrophilic changes, which improved retention of FGF-2. Morphologically, the cell density was higher, cell elongation was more pronounced, and the cell adhesion area was significantly higher in FGF-2-loaded cells than in those without FGF-2. In a cell proliferation assay using mouse osteoblast-like cells, absorbance tended to increase over time, especially in the FGF-2 group after 7 and 14 days, and in a bone differentiation assay based on ALP activity, there was a significant increase in the FGF-2 group after 14 days. In the rat model, H2 O2 hydrothermal oxidation and FGF-2 loading both resulted in more laminar bone tissue in the bone marrow around the mini-implant. These results suggest that titanium surface functionalization by H2 O2 hydrothermal oxidation and FGF-2 may promote initial cell adhesion, proliferation, and osteodifferentiation, and enhance bone bioactivity. These effects all contribute to early bonding of an implant with the surrounding bone.

Research on the co-evolution of temporal networks structure and public opinion propagation

Under the new media environment, social platforms, as the carrier of information propagation, have shown a drastic change in their form and structure, endowing public opinion with unique propagation characteristics. In view of this, considering the dynamic changes of online social network (OSN) structure, this article intends to analyse the spreading mechanism of public opinion in temporal networks and improve the applicability of public opinion governance strategies. Combing the changes of OSN topology with the classical susceptible–infected–recovered (SIR) dynamics model, we constructed a co-evolution model of temporal networks structure and public opinion propagation, and the propagation threshold of public opinion was derived with the help of Markov process. Then, the propagation characteristics of public opinion in temporal networks and their co-evolution process under different factors were discussed through simulation experiments. The results show that the propagation of public opinion in temporal networks has faster speed and wider scope compared with that in static networks; netizens’ social activity has a phased impact on the evolution process of public opinion and with its significant heterogeneity, the propagation of public opinion is gradually suppressed; compared with [Formula: see text], the evolution process of public opinion in temporal networks is more sensitive to the state change of public opinion [Formula: see text]. Our research can further enrich the theoretical research system of network science and information science and also provide a certain decision-making reference for the regulators to reasonably govern the propagation of public opinion in social platforms.

Computational Modeling of Structural Synaptic Plasticity in Echo State Networks.

Most existing studies on computational modeling of neural plasticity have focused on synaptic plasticity. However, regulation of the internal weights in the reservoir based on synaptic plasticity often results in unstable learning dynamics. In this article, a structural synaptic plasticity learning rule is proposed to train the weights and add or remove neurons within the reservoir, which is shown to be able to alleviate the instability of the synaptic plasticity, and to contribute to increase the memory capacity of the network as well. Our experimental results also reveal that a few stronger connections may last for a longer period of time in a constantly changing network structure, and are relatively resistant to decay or disruptions in the learning process. These results are consistent with the evidence observed in biological systems. Finally, we show that an echo state network (ESN) using the proposed structural plasticity rule outperforms an ESN using synaptic plasticity and three state-of-the-art ESNs on four benchmark tasks.

Tracing coalition changes in knowledge in and engagement with childhood obesity prevention to improve intervention implementation

BackgroundWhile most coalition research focuses on studying the effects of peer relationship structure, this study examines the coevolution of coalition structure and behavior across three communities in the U.S. with the goal of identifying coalition dynamics that impact a childhood obesity prevention intervention. MethodsOver two years (2018–2020), three communities within the U.S. participated in a childhood obesity prevention intervention at different times. This intervention was guided by the Stakeholder-Driven Community Diffusion theory, which describes an empirically testable mechanism for promoting community change. Measures are part of the Stakeholder-driven Community Diffusion (SDCD) survey with demonstrated reliability, which include knowledge of and engagement with childhood obesity prevention and social networks. Data from three coalition-committees and their respective networks were used to build three different stochastic actor-oriented models. These models were used to examine the coevolution of coalition structure with coalition behavior (defined a priori as knowledge of and engagement with obesity prevention) among coalition-committee members and their nominated alters (Network A) and coalition-committee members only (Network B). ResultsOverall, coalitions decrease in size and their structure becomes less dense over time. Both Network A and B show a consistent preference to form and sustain ties with those who have more ties. In Network B, there was a trend for those who have higher knowledge scores to increase their number of ties over time. The same trend appeared in Network A but varied based on their peers’ knowledge in and engagement with childhood obesity prevention. Across models, engagement with childhood obesity prevention research was not a significant driver of changes in either coalition network structure or knowledge.ConclusionsThe trends in coalition Network A and B’s coevolution models may point to context-specific features (e.g., ties among stakeholders) that can be leveraged for better intervention implementation. To that end, examining tie density, average path length, network diameter, and the dynamics of each behavior outcome (i.e., knowledge in and engagement with childhood obesity prevention) may help tailor whole-of-community interventions. Future research should attend to additional behavioral variables (e.g., group efficacy) that can capture other aspects of coalition development and that influence implementation, and to testing the efficacy of network interventions after trends have been identified.

Hydrogels for localized drug delivery: A special emphasis on dermatologic applications.

Skin disease treatment is a complex and time-consuming process due to the complex etiology, numerous side effects of conventional therapies, and difficulties in determining primary causes of the disease. Superficial wounds are often easy to treat. However, treatment of severe wounds caused by burn is challenging for clinicians. Optimum therapeutic benefits are based on the site-specific delivery of medicaments at the right time for a prolonged duration. Systemic toxicity and frequent dosing are the major challenges associated with the use of conventional therapeutics. Hydrogels are material of choice for drug delivery because of their high biocompatibility and ability to hold and release therapeutic agents. The number of hydrogels available for use in cosmetology and dermatology continues to grow during the past 1-2 decades. However, new hydrogel materials with high biocompatibility, antibacterial properties, and the ability to stimulate skin regeneration processes are in high demand. These are three-dimensional networks, which absorb a large amount of biological fluids and water. Hydrogels can be used as a biosensor, carrier systems for cells, drug delivery carriers especially for topical applications and in contact lenses. Hydrogels are highly porous carriers containing about 90% water. Stimuli-responsive hydrogels cause a change in network structure that is completely reversible in nature. The present review describes the applications of hydrogels in pharmaceutical formulations with a special emphasis on the treatment of dermatologic conditions such as acne, psoriasis, and mycosis.

Effect of exopolymer gels on the viscoelasticity of mucus-rich saltwater and settling dynamics of particles

Algal blooms enrich seawater with gel-like exopolymers modifying the local structure and physio-chemical properties of water column. These effects are particularly intense during mucilage events occurring seasonally in some seas and coastal regions and in thin layers. However, detailed in situ examination is limited, and the effects of biologically modified seawater on microscale processes remain poorly understood. In this study, we experimentally investigated the impact of exopolymer gels on rheological properties of mucus-rich saltwater and consequences on settling dynamics of fast-sinking particles using aqueous solutions of sodium chloride with an addition of xanthan gum, a model exopolymer forming gels dispersed in water. We studied two types of solutions: colloidal dispersion and dispersion with predominant content of particulate gels. The results showed that dispersions consisting of colloidal exopolymers have stronger inner structure than dispersions with predominant particulate exopolymers, suggesting that viscoelastic properties of seawater vary with the structure of gels. Settling velocity and drag exerted on solid spheres and disks were substantially different for the two types of solutions, with the drag coefficient up to six times higher for colloidal than for particulate solutions of corresponding salinity, indicating that colloidal exopolymers may retard particles falling in marine environment more effectively than particulate gels. The findings highlight possible variation in viscoelasticity of mucus-rich saltwater with exopolymer network structure changes in spatial and temporal scales during algal bloom development and implications to the sedimentation of fast-sinking particles such as faecal pellets, carbonate grains, minerals and microplastics.

Integration of differential expression and network structure for ‘omics data analysis

Differential expression (DE) analysis has been routinely used to identify molecular features that are statistically significantly different between distinct biological groups. In recent years, differential network (DN) analysis has emerged as a powerful approach to uncover molecular network structure changes from one biological condition to the other where the molecular features with larger topological changes are selected as biomarkers. Although a large number of DE and a few DN-based methods are available, they have been usually implemented independently. DE analysis ignores the relationship among molecular features while DN analysis does not account for the expression changes at individual level. Therefore, an integrative analysis approach that accounts for both DE and DN is required to identify disease associated key features. Although, a handful of methods have been proposed, there is no method that optimizes the combination of DE and DN. We propose a novel integrative analysis method, DNrank, to identify disease-associated molecular features that leverages the strengths of both DE and DN by calculating a weight using resampling based cross validation scheme within the algorithm. First, differential expression analysis of individual molecular features is carried out. Second, a differential network structure is constructed using the differential partial correlation analysis. Third, the molecular features are ranked in the order of their significances by integrating their DE measures and DN structure using the modified Google's PageRank algorithm. In the algorithm, the optimum combination of DE and DN analyses is achieved by evaluating the prediction performance of top-ranked features utilizing support vector machine classifier with Monte Carlo cross validation. The proposed method is illustrated using both simulated data and three real data sets. The results show that the proposed method has a better performance in identifying important molecular features with respect to predictive discrimination. Also, as compared to existing feature selection methods, the top-ranked features selected by our method had a higher stability in selection. DNrank allows the researchers to identify the disease-associated features by utilizing both expression and network topology changes between two groups.

Seasonal structure of interactions enhances multidimensional stability of mutualistic networks.

Community ecologists have made great advances in understanding how natural communities can be both diverse and stable by studying communities as interaction networks. However, focus has been on interaction networks aggregated over time, neglecting the consequences of the seasonal organization of interactions (hereafter 'seasonal structure') for community stability. Here, we extended previous theoretical findings on the topic in two ways: (i) by integrating empirical seasonal structure of 11 plant–hummingbird communities into dynamic models, and (ii) by tackling multiple facets of network stability together. We show that, in a competition context, seasonal structure enhances community stability by allowing diverse and resilient communities while preserving their robustness to species extinctions. The positive effects of empirical seasonal structure on network stability vanished when using randomized seasonal structures, suggesting that eco-evolutionary dynamics produce stabilizing seasonal structures. We also show that the effects of seasonal structure on community stability are mainly mediated by changes in network structure and productivity, suggesting that the seasonal structure of a community is an important and yet neglected aspect in the diversity–stability and diversity–productivity debates.

Dynamic Multiscale Information Spillover among Crude Oil Time Series.

This study investigated information spillovers across crude oil time series at different time scales, using a network combined with a wavelet transform. It can detect the oil price, which plays an important role in the dynamic process of spillovers, and it can also analyze the dynamic feature of systematic risk based on entropy at different scales. The results indicate that the network structure changes with time, and the important roles of an oil price can be identified. WTI and Brent act as important spillover transmitters, and other prices are important spillover receivers at a scale. With the increase in time scale, both the number of neighbors and the importance of spillovers of Brent and WTI as spillover transmitters show downward trends. The importance for spillovers of China–Shengli and Dubai as spillover receivers shows a downward trend. This paper provides new evidence for explaining WTI and Brent as global benchmark oil prices. In addition, systematic risk is time-varying, and it is smaller at short-term scale than at long-term scale. The trend of systematic risk is also discussed when typical oil-related events occur. This paper provides a new perspective for exploring dynamic spillovers and systematic risk that offers important implications for policymakers and market investors.

Adaptive structural changes in the motor cortex and white matter in Parkinson’s disease

Parkinson’s disease (PD) is a movement disorder characterized by the early loss of nigrostriatal dopaminergic pathways producing significant network changes impacting motor coordination. Recently three motor stages of PD have been proposed (a silent period when nigrostriatal loss begins, a prodromal motor period with subtle focal manifestations, and clinical PD) with evidence that motor cortex abnormalities occur to produce clinical PD[8]. We directly assess structural changes in the primary motor cortex and corticospinal tract using parallel analyses of longitudinal clinical and cross-sectional pathological cohorts thought to represent different stages of PD. 18F-FP-CIT positron emission tomography and subtle motor features identified patients with idiopathic rapid-eye-movement sleep behaviour disorder (n = 8) that developed prodromal motor signs of PD. Longitudinal diffusion tensor imaging before and after the development of prodromal motor PD showed higher fractional anisotropy in motor cortex and corticospinal tract compared to controls, indicating adaptive structural changes in motor networks in concert with nigrostriatal dopamine loss. Histological analyses of the white matter underlying the motor cortex showed progressive disorientation of axons with segmental replacement of neurofilaments with α-synuclein, enlargement of myelinating oligodendrocytes and increased density of their precursors. There was no loss of neurons in the motor cortex in early or late pathologically confirmed motor PD compared to controls, although there were early cortical increases in neuronal neurofilament light chain and myelin proteins in association with α-synuclein accumulation. Our results collectively provide evidence of a direct impact of PD on primary motor cortex and its output pathways that begins in the prodromal motor stage of PD with structural changes confirmed in early PD. These adaptive structural changes become considerable as the disease advances potentially contributing to motor PD.

The impact of social capital and transaction efficacy on salesperson performance

In business‐to‐business (B2B) markets, salespeople often act as market‐makers, connecting customers with suppliers while collaborating with other salespeople to form a complex network of relationships. The authors propose that in such a relationship network, the salesperson's social capital, or “who they know,” and transaction efficacy, or “what they do,” have direct and interactive effects on performance. To test the proposed model, the authors use a transaction‐level dataset from a large brokerage firm in the residential construction materials industry. The dataset spans 3 years, enabling authors to observe changes in network structure and model the interplay between social capital and transaction efficacy and their impact on performance. They find that while a salesperson's social capital has a direct effect on performance, these effects are contingent on the salesperson's transaction efficacy. With these results, the authors establish that the joint consideration of the salesperson's network position and transaction efficacy are important determinants of salesperson performance.

Enforcing Multilevel Security Policies in Unstable Networks

Multilevel security (MLS) systems control access to data by formalizing permissible and impermissible information flows between data sources and destinations (e.g., database servers and clients) fixed with distinct security labels. In computer networks, MLS systems have been used to prevent unauthorized data disclosure in shared-infrastructure settings where network hosts and devices may fall within different trust domains (e.g., in multi-tenant cloud networks or wireless mesh networks). However, current MLS systems assume static network behavior—thus preventing the network from being practically usable in the presence of dynamic network events that frequent unstable network environments, including sudden changes in traffic patterns, link failures, and topology changes as a result of device movement or intermittent device connectivity. In this paper, we introduce <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLS-Enforcer</monospace> , a software-defined networking (SDN) controller application that can efficiently deploy network-level MLS policies while retaining the ability to securely relabel network nodes under changing topology state and network traffic demands. We model network adaptivity as an integer linear programming problem that reflects a given security policy. We then introduce heuristic relabeling algorithms that achieve near-optimal performance and are more tractable and efficient for larger networks. We validate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLS-Enforcer</monospace> on several network topologies and traffic loads, demonstrating that it can relabel the network to route 90%+ of flows under normal conditions and quickly converge (on the order of seconds for the heuristic algorithms) under changing needs—from small network structure changes to catastrophic failures. This shows that formally secured networks can feasibly be deployed in diverse, changing, and unpredictable environments.

Single Phase to Ground Fault Location of Distribution Network Based on Combined-GAT

Background: At present, small current grounding system has been widely used in distribution network of China. Affected by the complex topology of the distribution network and other factors, single-phase grounding fault has become the most prone type of electrical short-circuit fault in China. Objective: Considering that the traditional fault selection and location methods are difficult to mine the effective information of fault quantity, a new method is proposed in this paper to achieve accurate fault location on the basis of ensuring timeliness. Methods: In this paper, the physical topology of the distribution network is regarded as a graph, the overhead lines and cables of the main equipment are regarded as the nodes in the graph, and the problem of fault node location is corresponding to the task of graph attention classification. Considering the average degree and homogeneity of the given network topology, an improved graph attention network is built to realize fault node location. Results: This paper verifies the effectiveness of the proposed model for fault location through simulation in PSCADA. In addition, the applicability of the proposed model in the case of changes in the distribution network structure is verified. It verifies that the proposed method achieves high positioning accuracy.

Analyzing annual changes in network structures of a social media application-based information-sharing system in a Japanese community

BackgroundUnderstanding the evolution of social network services (SNSs) can provide insights into the functions of interprofessional information-sharing systems. Using social network analysis, we aimed to analyze annual changes in the network structure of SNS-based information sharing among healthcare professionals over a 3-year period in Japan.MethodsWe analyzed data on SNS-based information sharing networks with online message boards for healthcare professionals for 2018, 2019, and 2020 in a Japanese community.These networks were created for each patient so that healthcare professionals could post and view messages on the web platform. In the social network analysis (SNA), healthcare professionals registered with a patient group were represented as nodes, and message posting and viewing relationships were represented as links. We investigated the structural characteristics of the networks using several measures for SNA, including reciprocity, assortativity and betweenness centrality, which reflect interrelational links, the prevalence of similar nodes with neighbors, and the mediating roles of other nodes, respectively. Next, to compare year-to-year trends in networks of patients overall, and between receiving nursing care levels 1–3 (lighter care requirement) and levels 4–5 (heavier care requirement), we described the annual structural differences and analyzed each measure for SNA using the Steel–Dwass test.ResultsAmong 844, 940, and 1063 groups in each year, groups for analysis in care levels 1–3/4–5 were identified as 106/135, 79/89, and 57/57, respectively. The overall annual assessment showed a trend toward increased diameter and decreased density, but the differences were not significant. For those requiring care levels 1–3, assortativity decreased significantly, while for those requiring care levels 4–5, reciprocity decreased and betweenness centrality increased significantly. No significant differences were found in the other items.DiscussionThis study revealed that the network of patients with a lighter care requirement had more connections consisting of nodes with different links, whereas the network of patients with a heavier care requirement had more fixed intermediary roles and weaker interrelationships among healthcare professionals. Clarifying interprofessional collaborative mechanisms underlying development patterns among healthcare professionals can contribute to future clinical quality improvement.