Changes In Network Structure Research Articles

Risk spillover mechanism among commercial banks and FinTech institutions throughout public health emergencies

While FinTech has contributed to the improvement of efficiency and cost reduction for commercial banks, it has also brought about risks for them. Especially during the outbreak of major public emergencies, the degree of risk spillover among financial institutions increased significantly. In this paper, we constructed a high-dimensional risk spillover network using the elastic net shrinkage technique to investigate the impact of public health emergencies on the risk spillovers between FinTech institutions and commercial banks. The total spillover index was utilized to access the overall coupling between FinTech institutions and commercial banks. Furthermore, we employed the sectoral spillover index and institutional centrality index to examine the spillover intensity across different sectors and institutions. Additionally, we analyzed the changes in the risk spillover network structure and institutional risk role during emergencies, aiming to uncover the impact mechanism of public health emergencies on risk spillovers. The results reveal that (1) public health emergencies, such as the COVID-19 pandemic, have intensified the industry correlation between the FinTech and banking sectors, and the primary risks of the system have shifted from intraindustry risks to interindustry risks. (2) Public health emergencies have changed the risk transmission roles of FinTechs and banks. FinTechs transitioned from being risk recipients to risk contributors, while banks shifted from being risk contributors to risk recipients. (3) FinTechs play a crucial role in facilitating indirect risk transmission within the system, acting as influential adjacent institutions that bridge the gap between critical institutions. (4) Compared with large commercial banks, small and medium-sized banks are more sensitive to FinTech risks. This study provides supporting evidence for regulatory agencies to enhance risk management in financial innovation during public health emergencies and in the post-pandemic era.

Trade network dynamics in a globalized environment and on the edge of crises

At the edges of crises, the roles of trade networks and supply chains are evident. Global trade networks aggregate all legal trade activities. Thus, researchers believe that network structure changes reflect phenomena such as crises, globalization, deglobalization, or even technological development, including those related to green technologies. Although trade network analysis has been widely researched, researchers have focused mainly on one or a few products or product groups. While the impact of crises and technological changes in the case of individual products has been demonstrated in some studies, less attention has been given to structural pattern changes in trade on these products. To understand how a shock, crisis, or technological change in one product may affect the trade network of another product, the patterns and relationships of node- and network-level indicators across products need to be identified. In this study, the dynamics and interdependencies of trade networks are analyzed using a combination of network indicators, cluster analysis and causality analysis. Variations and implications of the temporal patterns and causal relationships of trade network indicators are revealed for different products and product groups. Researchers have argued that the deterioration of structural indicators exacerbates crises and amplifies their consequences. A causality analysis shows that disruptions in one product’s supply chain quickly affect the trade of other products, altering the structural characteristics of trade networks. The proposed temporal pattern analysis shows that while the 2008 financial crisis did not cause structural changes in the product trade network, indicators improved before, during, and after the crisis. Nevertheless, before the pandemic, there was a noticeable deterioration in almost all structural indicators, signaling more deglobalization. This study contributes to a comprehensive understanding of trade networks, equipping decision-makers with the knowledge to promote the resilience and sustainability of these networks in an evolving global landscape.

Adoption of deep learning-based magnetic resonance image information diagnosis in brain function network analysis of Parkinson’s disease patients with end-of-dose wearing-off

Objectivethis study was to analyze the brain functional network of end-of-dose wearing-off (EODWO) in patients with Parkinson’s disease (PD) using a convolutional neural network (CNN)-based functional magnetic resonance imaging (fMRI) data classification model. Methodsone hundred PD patients were recruited and assigned to control (Ctrl) group (39 cases without EODWO) and experimental (Exp) group (61 cases with EODWO). The data classification model based on a CNN was employed to assist the analysis of the changes in brain functional network structure in the two groups. The CNN-based fMRI data classification model was primarily based on a CNN architecture, with improvements made to the initialization of convolutional kernel parameters. Firstly, a structure based on restricted Boltzmann machine (RBM) was constructed, followed by the initialization of convolutional kernel parameters. Subsequently, the model underwent training. Utilizing the data analysis module within the GRETNA toolbox, extracted feature sets were analyzed, including local measures such as betweenness centrality (BC) and degree centrality (DC), as well as global measures such as global efficiency (Eg) and local efficiency (Eloc). Resultsas sparsity increased, there was a gradual upward trend observed in Eg; however, the values of Eg in both brain functional networks remained relatively stable within the range of 0.2–0.5. The Eg value of the Ctrl group’s whole-brain functional network was 0.17 ± 0.02, while that of the Exp group’s whole-brain functional network was 0.17 ± 0.03, with no significant difference between them (P>0.05). The functional DC value of the superior frontal gyrus in the Exp group (8.71 ± 2.56) was significantly lower than that of the Ctrl group (13.32 ± 3.22), whereas the functional DC value of the anterior cingulate gyrus in the Exp group (19.33 ± 4.78) was significantly higher than that of the Ctrl group (15.21 ± 4.02) (P<0.05). There was no significant correlation observed between the functional DC value and levodopa or dopamine agonist therapy (DDT) in the Exp group, whereas the Ctrl group exhibited a significant positive correlation. Conclusionanalysis conducted via a CNN-based fMRI data classification model revealed a correlation between the occurrence of EODWO in PD patients and functional impairments in the left precuneus. Additionally, the occurrence of EODWO may potentially diminish the plasticity of the central prefrontal dopamine.

Topological alterations in white matter anatomical networks in cervical dystonia

BackgroundAccumulating neuroimaging evidence indicates that patients with cervical dystonia (CD) have changes in the cortico-subcortical white matter (WM) bundle. However, whether these patients’ WM structural networks undergo reorganization remains largely unclear. We aimed to investigate topological changes in large-scale WM structural networks in patients with CD compared to healthy controls (HCs), and explore the network changes associated with clinical manifestations.MethodsDiffusion tensor imaging (DTI) was conducted in 30 patients with CD and 30 HCs, and WM network construction was based on the BNA-246 atlas and deterministic tractography. Based on the graph theoretical analysis, global and local topological properties were calculated and compared between patients with CD and HCs. Then, the AAL-90 atlas was used for the reproducibility analyses. In addition, the relationship between abnormal topological properties and clinical characteristics was analyzed.ResultsCompared with HCs, patients with CD showed changes in network segregation and resilience, characterized by increased local efficiency and assortativity, respectively. In addition, a significant decrease of network strength was also found in patients with CD relative to HCs. Validation analyses using the AAL-90 atlas similarly showed increased assortativity and network strength in patients with CD. No significant correlations were found between altered network properties and clinical characteristics in patients with CD.ConclusionOur findings show that reorganization of the large-scale WM structural network exists in patients with CD. However, this reorganization is attributed to dystonia-specific abnormalities or hyperkinetic movements that need further identification.

Perioperative research into memory (PRiMe), part 2: Adult burns intensive care patients show altered structure and function of the default mode network

BackgroundLong-term cognitive impairment (LTCI) is experienced by up to two thirds of patients discharged from burns intensive care units (BICUs), however little is known about its neurobiological basis. This study investigated if patients previously admitted to BICU showed structural and functional MRI changes of the Default Mode Network (DMN). MethodsFifteen patients previously admitted to BICU with a significant burns injury, and 15 matched volunteers, underwent structural and functional MRI scans. Functional connectivity, fractional anisotropy and cortical thickness of the main DMN subdivisions (anterior DMN (aDMN), posterior DMN (pDMN) and right (rTPJ) and left (lTPJ) temporo-parietal junctions) were compared between patients and volunteers, with differences correlated against cognitive performance. ResultsFunctional connectivity between rTPJ and pDMN (t = 2.91, p = 0.011) and between rTPJ and lTPJ (t = 3.18, p = 0.008) was lower in patients compared to volunteers. Functional connectivity between rTPJ and pDMN correlated with cognitive performance (r2 =0.33, p < 0.001). Mean fractional anisotropy of rTPJ (t = 2.70, p = 0.008) and lTPJ (T = 2.39, p = 0.015) was lower in patients but there was no difference in cortical thickness. ConclusionsPatients previously admitted to BICU show structural and functional disruption of the DMN. Since functional changes correlate with cognitive performance, this should direct further research into intensive-care-related cognitive impairment.

Spatiotemporal dynamics analysis and parameter optimization of a network epidemic-like propagation model based on neural network method

In this paper, a reaction-diffusion model is established to study the dynamic behavior of rumor propagation. Firstly, we consider the existence of the positive equilibrium points. Then, we perform a stability analysis to study the conditions for the occurrence of Turing instability. Secondly, we use multiscale analysis to derive the expression of the amplitude equation. In the process of numerical simulation, the reality is considered. It shows that controlling the spread rate of rumor and the number of new Internet users have a great effect on curbing the spread of online rumor. Furthermore, it is proved that the analysis of amplitude equation plays a decisive role in the formation of Turing patterns. We also discuss the phenomenon of Turing patterns when the network structure changes and verify the rationality of the model by Monte Carlo method. Finally, we consider two methods based on statistical principle and convolutional neural network severally to identify the parameters of the reaction-diffusion system with Turing instability by using stable patterns. The statistical principle-based method offers superior accuracy, whereas the convolutional neural network-based approach significantly reduces recognition time and cuts down time costs.

The impact of relationship stickiness and memory on the evolution of individual behavior

This study investigates the dynamics of individual behavior and decision-making processes shaped by changes in personal relationships and preferences, using the prisoner’s dilemma game as a framework. The research focuses on the effects of relationship stickiness and memory on behavior evolution and network structural changes, considering two types of initial relationship networks. The findings indicate that as the number of games increases, there is a general trend of gradual increases in the average relationship stickiness, topological changes, and network heterogeneity. Notably, networks with greater initial heterogeneity, such as scale-free networks, better facilitate individual cooperation. Contrary to previous studies, the stable density of cooperators does not consistently increase with higher temptation gains. This suggests that individuals’ adaptive strategies, which include changing neighbors, can foster cooperation even under greater temptations. Additionally, an increase in the sensitivity coefficient correlates with increases in the final cooperator density, network heterogeneity, and topological changes. As individual memory and temptation gain rise, network heterogeneity measures tend to increase, while measures of topological change generally decrease. A sharp decline in the stable density of cooperators coincides almost synchronously with a rapid increase in network heterogeneity and a decrease in topological changes. This research reveals how relationship stickiness and memory impact individual cooperation, enhancing our understanding of the underlying mechanisms driving cooperative behaviors in complex networks.

How to make you more central? The role of guanxi and interaction

PurposeMany studies have investigated dynamic positions and their importance, but there is less attention paid to how to enter more central positions. Interorganizational relationships are an important factor in network structural change. In Chinese society, firms allocate significant human, financial and material resources towards cultivating guanxi. The purpose of this study is to explore whether and how the three aspects of guanxi, namely renqing, ganqing and xinyong, can make firms more central, and to examine the mediating role of interaction.Design/methodology/approachThe study used a mixed method to collect data from 256 Chinese Cops (complex product systems) firms. And, hypotheses were tested using SPSS 25.0 and AMOS 26.0.FindingsThe results indicate that renqing, ganqing and xinyong have significant positive effects on the increase in centrality, but with varying magnitudes. Additionally, the interaction was found to mediate the relationship between the three aspects of guanxi (renqing, ganqing and xinyong) and the increase in centrality.Originality/valueThe study provides new insights to help firms become more central by combining guanxi (renqing, ganqing and xinyong) with change in centrality, enriching the literature on network dynamics and guanxi-related research. Moreover, the study provides managers with a clear understanding of how to use guanxi to make the firm more central in situations with limited resources.

Spatiotemporal impacts of metro network structure on land use change

Numerous studies have presented compelling findings regarding the favorable influence exerted by the construction of metro systems on the development of land use along the routes. However, the effects of metro network structure on land use change, especially the long-term dynamic effects, remain uncertain. To address this gap, this research utilizes land use coverage data and metro network data from Wuhan, China, spanning the period from 2014 to 2019. A complex network model is employed to analyze changes in the metro network structure, and the GTWR model is employed to investigate the impact of the of metro network structure on land use change. The results indicate that metro construction effectively stimulates land use change along the routes, predominantly leading to the conversion of non-construction land into construction land. Global accessibility, degree centrality, betweenness centrality, and eigenvector centrality, within the metro network structure attributes demonstrate significant positive effects on land use change, and these effects exhibit considerable spatiotemporal non-stationarity. Additionally, built environment factors surrounding metro stations, such as population density, plot ratio, street intersection density, and the quantity of bus stops, also exhibit notably spatiotemporal effects on land use change. These findings indicate that government agencies and urban planners should concentrate on optimizing the distribution of the urban metro network to improve its overall structural efficiency. When planning the construction of the metro network, careful attention should be paid to the built environment near the stations to effectively facilitate land use development in the vicinity.

A novel method for solving the multi-commodity flow problem on evolving networks

The minimum cost multi-commodity flow (MMCF) problem on evolving networks is the latest challenge to solving the min-cost network flow problem of multi-commodity flowing from multi-source and multi-sink on an evolving network. Previous researches primarily focus on the solution of MMCF problem within static networks. However, researchers ignored the MMCF problem within networks where the structure dynamically changes over time. Evolving networks have a dynamically changing and unpredictable network structure. The existing methods must recalculate when the network structure changes, leading to high computational complexity. This manuscript proposes a dynamic graph computing model for adaptive dynamic path selection and optimization. First, the model selects the path with the least cost for each commodity, assuming the route can transport a specified number of such commodities to find the optimal path faster. Second, we design path selection and flow update strategies for each node in the graph to adapt to changes in the graph structure. At the same time, we can prevent multiple commodity conflicts from exceeding the marginal capacity limit. Finally, the optimal path of each commodity is dynamically pruned based on the original optical path to obtain the tree structure and determine the absolute path to meet the specified transportation quantity of each commodity. Performance analysis results show that our method can efficiently cope with the dynamic changes of the network structure, which provides ideas for solving the multi-commodity network flow problem in dynamic networks.

Abnormal longitudinal changes of structural covariance networks of cortical thickness in mild traumatic brain injury with posttraumatic headache

BackgroundIt is widely acknowledged that mild traumatic brain injury (MTBI) leads to either functionally or anatomically abnormal brain regions. Structural covariance networks (SCNs) that depict coordinated regional maturation patterns are commonly employed for investigating brain structural abnormalities. However, the dynamic nature of SCNs in individuals with MTBI who suffer from posttraumatic headache (PTH) and their potential as biomarkers have hitherto not been investigated. MethodsThis study included 36 MTBI patients with PTH and 34 well-matched healthy controls (HCs). All participants underwent magnetic resonance imaging scans and were assessed with clinical measures during the acute and subacute phases. Structural covariance matrices of cortical thickness were generated for each group, and global as well as nodal network measures of SCNs were computed. ResultsMTBI patients with PTH demonstrated reduced headache impact and improved cognitive function from the acute to subacute phase. In terms of global network metrics, MTBI patients exhibited an abnormal normalized clustering coefficient compared to HCs during the acute phase, although no significant difference in the normalized clustering coefficient was observed between the groups during the subacute phase. Regarding nodal network metrics, MTBI patients displayed alterations in various brain regions from the acute to subacute phase, primarily concentrated in the prefrontal cortex (PFC). ConclusionsThese findings indicate that the cortical thickness topography in the PFC determines the typical structural-covariance topology of the brain and may serve as an important biomarker for MTBI patients with PTH.

Network analysis on psychopathological symptoms, psychological measures, quality of life and COVID-19 related factors in Chinese psychiatric patients in Hong Kong

BackgroundPsychiatric patients are susceptible to adverse mental health impacts during COVID-19, but complex interplays between psychopathology and pandemic-related variables remain elusive. This study aimed to investigate concomitant associations between psychopathological symptoms, psychological measures and COVID-19 related variables in Chinese psychiatric patients during the peak of fifth pandemic wave in Hong Kong.MethodsWe employed network analysis to investigate inter-relationships among psychopathological symptoms (including depression, anxiety, post-traumatic stress disorder-like [PTSD-like] symptoms, insomnia, psychotic symptoms), cognitive complaints, health-related quality of life, loneliness, resilience and selected pandemic-related factors in 415 psychiatric outpatients between 28 March and 8 April, 2022. Network comparisons between genders, diagnosis (common mental disorders [CMD] vs. severe mental disorders [SMD]), and history of contracting COVID-19 at fifth wave were performed as exploratory analyses.ResultsOur results showed that anxiety represented the most central node in the network, as indicated by its highest node strength and expected influence, followed by depression and quality of life. Three comparatively strong connections between COVID-19 and psychopathological variables were observed including: fear of contagion and PTSD-like symptoms, COVID-19 stressor burden and PTSD-like symptoms, and COVID-19 stressor burden and insomnia. Network comparison tests revealed significant network structural difference between participants with history of contracting COVID-19 and those without, but showed no significant difference between genders as well as between CMD and SMD patients.ConclusionsOur findings suggest the pivotal role of anxiety in psychopathology network of psychiatric patients amidst COVID-19. Pandemic-related variables are critically associated with trauma/stress and insomnia symptoms. Future research is required to elucidate potential network structural changes between pandemic and post-COVID periods.

Structural Connectivity Changes in Brain Network After Spinal Cord Injury (P10-4.001)

Structural Connectivity Changes in Brain Network After Spinal Cord Injury (P10-4.001)

Gelling properties of acid-induced tofu (soybean curd): Effects of acid type and nano-fish bone.

The effects of different types of acid coagulants and nano fish bone (NFB) additives on the characteristics of tofu were investigated using texture analyzers, SEM, FT-IR, and other techniques. The breaking force and penetration distance, in descending order, were found in the tofu induced by glucono-d-lactone (GDL) (180.27g and 0.75cm), citric acid (152.90g and 0.74cm), lactic acid (123.33g and 0.73cm), and acetic acid (69.84g and 0.58cm), respectively. The syneresis of these tofu samples was in the reverse order (35.00, 35.66, 39.66, and 44.50%). Lightness and whiteness were not significantly different among the different samples. Regardless of the acid type, the soluble calcium content in the soybean milk was significantly increased after adding NFB. As a result, the breaking force and penetration distance of all tofu samples increased significantly, but the syneresis decreased. Compared with tofu coagulated by other acids, GDL tofu formed a more uniform and dense gel network maintained by the highest intermolecular forces (especially hydrophobic interactions). Regarding the secondary structure, the lowest percentage of α-helix (22.72%) and, correspondingly, the highest β-sheet (48.32%) and random coil (18.81%) were noticed in the GDL tofu. The effects of NFB on the tofu characteristics can be explained by the changes in the gel network, intermolecular forces, and secondary structure, which were in line with the acid type. The characteristics of acid-induced tofu can be most synergistically improved by coagulation with GDL and NFB.

Investigating change in network structure of eating disorder symptoms after delivery of a smartphone app-based intervention.

Eating disorder (ED) research has embraced a network perspective of psychopathology, which proposes that psychiatric disorders can be conceptualized as a complex system of interacting symptoms. However, existing intervention studies using the network perspective have failed to find that symptom reductions coincide with reductions in strength of associations among these symptoms. We propose that this may reflect failure of alignment between network theory and study design and analysis. We offer hypotheses for specific symptom associations expected to be disrupted by an app-based intervention, and test sensitivity of a range of statistical metrics for identifying this intervention-induced disruption. Data were analyzed from individuals with recurrent binge eating who participated in a randomized controlled trial of a cognitive-behavioral smartphone application. Participants were categorized into one of three groups: waitlist (n = 155), intervention responder (n = 49), and intervention non-responder (n = 77). Several statistical tests (bivariate associations, network-derived strength statistics, network invariance tests) were compared in ability to identify change in network structure. Hypothesized disruption to specific symptom associations was observed through change in bivariate correlations from baseline to post-intervention among the responder group but were not evident from symptom and whole-of-network based network analysis statistics. Effects were masked when the intervention group was assessed together, ignoring heterogeneity in treatment responsiveness. Findings are consistent with our contention that study design and analytic approach influence the ability to test network theory predictions with fidelity. We conclude by offering key recommendations for future network theory-driven interventional studies.

An Application of Innovative Algorithm of Integrated Social Network Analysis with Statistical Lstm Chain Network Analysis (Slstm-Cna) for Entrepreneurial Team Member Selection

Selecting the right members for an entrepreneurial team is a critical step in ensuring the success and viability of a startup venture. Each team member brings unique skills, experiences, and perspectives that contribute to the overall vision and execution of the business idea. The factors such as cultural fit, adaptability, and a shared commitment to the venture's mission and values can foster cohesion and synergy within the team. This paper presents an innovative algorithm for the selection of entrepreneurial team members, Leveraging insights from social network dynamics and LSTM-based predictive modeling, the algorithm aims to identify individuals with the highest potential for contributing to the success of a startup venture. By analyzing social network structures, the algorithm identifies key influencers and connectors within professional networks, thereby facilitating the identification of candidates who possess valuable connections and collaborative capacities. Furthermore, SLSTM-CNA enables the algorithm to forecast future network trends and dynamics, aiding in the selection of team members who can adapt and thrive in evolving entrepreneurial ecosystems. simulated entrepreneurial ecosystem, the algorithm identified individuals with the highest potential for contributing to startup success based on various criteria, including network centrality, connectivity, and predictive network dynamics. For instance, individuals with a centrality score exceeding 0.7 demonstrated a 25% higher likelihood of facilitating valuable connections critical for business growth. Additionally, candidates identified through SLSTM-CNA exhibited a 30% increase in adaptability to changing network structures compared to those selected through conventional methods

Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation

Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-mask image inpainting or style transfer. On the other hand, GAN-based diverse image generative methods require retraining/fine-tuning the network or designing complex noise injection functions, which is computationally expensive, task-specific, or struggle to generate high-quality results. Given that many deterministic conditional image generative models have been able to produce high-quality yet fixed results, we raise an intriguing question: is it possible for pre-trained deterministic conditional image generative models to generate diverse results without changing network structures or parameters? To answer this question, we re-examine the conditional image generation tasks from the perspective of adversarial attack and propose a simple and efficient plug-in projected gradient descent (PGD) like method for diverse and controllable image generation. The key idea is attacking the pre-trained deterministic generative models by adding a micro perturbation to the input condition. In this way, diverse results can be generated without any adjustment of network structures or fine-tuning of the pre-trained models. In addition, we can also control the diverse results to be generated by specifying the attack direction according to a reference text or image. Our work opens the door to applying adversarial attack to low-level vision tasks, and experiments on various conditional image generation tasks demonstrate the effectiveness and superiority of the proposed method.

A novel higher-order Deffuant–Weisbuch networks model incorporating the Susceptible Infected Recovered framework

The literature on opinion dynamics lacks consideration of the changing states of nodes during the process of opinion interaction, as observed in the literature on social networks. To address this gap, this paper proposes an adapted three-dimensional Deffuant–Weisbuch model based on the Susceptible Infected Recovered (SIR) model, which explores the impact of node state changes on opinion interaction. We incorporate the SIR model to describe changes in node state and improve the trust threshold by taking into account nodes’ emotional factor, cognitive ability, and state of the arts. The model is also modified using the opinion natural evolution coefficient and accounts for dynamic changes in social network structure. We validate our model with different network types and structures, and verify our results using the Louvain algorithm and index of centrality. Our model reveals that under different network types and structures, multiple opinion cliques are formed, and the opinions of ordinary nodes tend to converge around those of opinion leaders, indicating the guiding role of opinion leaders in shaping public opinion.

Effect of penetrant-polymer interactions and shape on the motion of molecular penetrants in dense polymer networks.

The diffusion of dilute molecular penetrants within polymers plays a crucial role in the advancement of material engineering for applications such as coatings and membrane separations. The potential of highly cross-linked polymer networks in these applications stems from their capacity to adjust the size and shape selectivity through subtle changes in network structures. In this paper, we use molecular dynamics simulation to understand the role of penetrant shape (aspect ratios) and its interaction with polymer networks on its diffusivity. We characterize both local penetrant hopping and the long-time diffusive motion for penetrants and consider different aspect ratios and penetrant-network interaction strengths at a variety of cross-link densities and temperatures. The shape affects the coupling of penetrant motion to the cross-link density- and temperature-dependent structural relaxation of networks and also affects the way a penetrant experiences the confinement from the network meshes. The attractive interaction between the penetrant and network primarily affects the former since only the system of dilute limit is of present interest. These results offer fundamental insights into the intricate interplay between penetrant characteristics and polymer network properties and also suggest future directions for manipulating polymer design to enhance the separation efficiency.

Hydrated Electron Dynamics and Stimulated Raman Scattering in Water Induced by Ultrashort Laser Pulses.

For this study, we employed intense 400 nm, 100 fs pulses linearly propagated through a 50 cm water medium, initially self-stretching the excitation pulses to 2.50 ps. Subsequently, the self-stretched 2.50 ps pulses were focused into deionized water, and we conducted transient absorption experiments to measure and investigate the dynamics of hydrated electrons in water. The excess electrons generated were injected into the hydrogen bond network of the water cluster, leading to the observation of saturated hydrated electrons. Additionally, we observed the emergence of the forward stimulated Raman scattering (SRS) of water molecules. We report the experimental observation of a weak forward SRS emission at 463 nm (corresponding to 3400 cm-1), indicative of the ordinary OH stretching vibration in the liquid phase. Moreover, we observed an intense forward SRS emission at 460 nm in water, corresponding to two anomalous Raman shifts at 3260 cm-1 and 3355 cm-1. These anomalous Raman shifts resulted from changes in the hydrogen bond network structure. We determine that the formation of not fully hydrated and saturated hydrated electrons plays a crucial role in producing this phenomenon.