Geometric Space Research Articles

High-Throughput Spectroscopy of Geometry-Tunable Arrays of Axial InGaAs Nanowire Heterostructures with Twin-Induced Carrier Confinement.

Predicting the optical properties of large-scale ensembles of luminescent nanowire arrays that host active quantum heterostructures is of paramount interest for on-chip integrated photonic and quantum photonic devices. However, this has remained challenging due to the vast geometrical parameter space and variations at the single object level. Here, we demonstrate high-throughput spectroscopy on 16800 individual InGaAs quantum heterostructures grown by site-selective epitaxy on silicon, with varying geometrical parameters to assess uniformity/yield in luminescence efficiency, and emission energy trends. The luminescence uniformity/yield enhances significantly at prepatterned array mask opening diameters (d0) greater than 50 nm. Additionally, the emission energy exhibits anomalous behavior with respect to d0, which is notably attributed to rotational twinning within the InGaAs region, inducing significant energy shifts due to quantum confinement effects. These findings provide useful insights for mapping and optimizing the interdependencies between geometrical parameters and electronic/optical properties of widely tunable sets of quantum nanowire heterostructures.

Three-dimensional Change Detection and Description in Complex Construction Scenarios

Abstract. In large-scale engineering construction scenarios, construction objects and equipment are numerous; their spatial positions and postures change frequently and are complex and varied. Describing the changes between construction entities and the entities themselves in a three-dimensional (3D) geometric space and functional semantic space is challenging. In response to this challenging, we propose a novel workflow to detect and describe the changes in a construction site. First, we add the constraint of object surface continuity to the octree change detection (OCD), exclude the independently changing cells, and improve the change detection robustness. Second, based on the changes in entity elements that occur in the 3D geometric space, we introduce six-type semantics to characterize the changes that occur. Differing from existing change detection semantic description methods that focus on the attribute semantics of changed entity elements, our method focuses on the semantic description of the change process. Finally, for the variations in different semantic types, we propose specific quantization methods that can better quantify the variations in entity elements in 3D geometric space. For the proposed solution, we tested it using two sets of multi-period time-series point cloud data, which can accurately identify the changing construction entities in space and complete the calculation of bridge construction progress, including the building demolition and construction, and the change in the construction apparatus position.

An Effective Alternative to Current Mathematics

If you don't understand mathematics, ask yourself if I'm right, because others don't understand mathematics either. By effective alternative to current mathematics, we mean working in a more complete mathematical space than the classical 3D+t variety which is inadequate for generating well-defined definitions and hypotheses as well as its limited ability to solve time-dependent partial differential equations. The current classical discrete 3D+t space PDE, in which time is an external controller and not integrated into the 3D geometric space, cannot be integrated digitally. This space is logically incomplete and misleading in the production of definitions and hypotheses as well as in the resolution itself of time- dependent PDEs. It is no wonder that these definitions/assumptions are confusing and result in weak or intractable mathematics, leading to all kinds of misunderstandings, from horrible notations to undisciplined length of theorems containing a considerable amount of black magic and ending with a gray nature of the mathematical result obtained. In this article, we present some of the most inaccurate assumptions and definitions in current classical mathematics that arise from using the 3D+t manifold space to specify initial conditions, boundary conditions, and the source/sink term. Fortunately, these inaccurate assumptions that start with inadequate space for boundary conditions, initial conditions, and source/sink term can be spotted and analyzed via 4D unitary numerical statistical theory called Cairo techniques in the format of transition chains of matrix B to complete what is missing. In other words, we present how to spot some of the worst mathematical conclusions of classical 3D geometry plus t as an external control numerical space, and then show how to correct them via the 4D unit space which is the subject of this article.

MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion.

Temporal knowledge graphs (TKGs) are receiving increased attention due to their time-dependent properties and the evolving nature of knowledge over time. TKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, which can often be mixed together. However, embedding TKGs into Euclidean space, as is typically done with TKG completion (TKGC) models, presents a challenge when dealing with high-dimensional nonlinear data and complex geometric structures. To address this issue, we propose a novel TKGC model called multicurvature adaptive embedding (MADE). MADE models TKGs in multicurvature spaces, including flat Euclidean space (zero curvature), hyperbolic space (negative curvature), and hyperspherical space (positive curvature), to handle multiple geometric structures. We assign different weights to different curvature spaces in a data-driven manner to strengthen the ideal curvature spaces for modeling and weaken the inappropriate ones. Additionally, we introduce the quadruplet distributor (QD) to assist the information interaction in each geometric space. Ultimately, we develop an innovative temporal regularization to enhance the smoothness of timestamp embeddings by strengthening the correlation of neighboring timestamps. Experimental results show that MADE outperforms the existing state-of-the-art TKGC models.

Temporal geometric mapping defines morphoelastic growth model of Type B aortic dissection evolution

The human aorta undergoes complex morphologic changes that mirror the evolution of disease. Finite element analysis (FEA) enables the prediction of aortic pathologic states, but the absence of a biomechanical understanding hinders the applicability of this computational tool. We incorporate geometric information from computed tomography angiography (CTA) imaging scans into FEA to predict a trajectory of future geometries for four aortic disease patients. Through defining a geometric correspondence between two patient scans separated in time, a patient-specific FEA model can recreate the deformation of the aorta between the two time points, showing that pathologic growth drives morphologic heterogeneity. FEA-derived trajectories in a shape-size geometric feature space, which plots the variance of the shape index versus the inverse square root of aortic surface area (δS vs. AT−1), quantitatively demonstrate an increase in δS. This represents a deviation from physiologic shape changes and parallels the true geometric progression of aortic disease patients.

Is it Time to Demolish Current Mathematics?

Current mathematics in 3D geometric space plus real time t as an external control is incomplete and misleading. The dream of theoretical physicists and mathematicians to demolish all current mathematics and replace it with a single universal numerical statistical law in 4D is now within reach. In this paper, we first focus on the introduction and definition of the proposed unitary 4D space. Next, we introduce and explain what we call the modern Laplacian theorem in 4D unit space. Finally, we explain some unexpected and striking numerical results such as measuring the speed of sound in air at 330 m/s and that of light at 3 E8 m/s. There is an inherent relationship between the speed of sound in air and the diffusivity of sound waves in the sound room, similar to the relationship between the speed of light in a vacuum and the thermal diffusivity of metals when they all live two in a 4D unit. space.

Soft pneumatic actuators for pushing fingers into extension

BackgroundCompliant pneumatic actuators possess many characteristics that are desirable for wearable robotic systems. These actuators can be lightweight, integrated with clothing, and accommodate uncontrolled degrees of freedom. These attributes are especially desirable for hand exoskeletons, where the soft actuator can conform to the highly variable digit shape. In particular, locating the pneumatic actuator on the palmar side of the digit may have benefits for assisting finger extension and resisting unwanted finger flexion, but this configuration requires suppleness to allow digit flexion while retaining sufficient stiffness to assist extension.MethodsTo meet these needs, we designed an actuator consisting of a hollow chamber long enough to span the joints of each digit while sufficiently narrow not to inhibit finger adduction. We explored the geometrical design parameter space for this chamber in terms of shape, dimensions, and wall thickness. After fabricating an elastomer-based prototype for each actuator design, we measured active extension force and passive resistance to bending for each chamber using a mechanical jig. We also created a finite element model for each chamber to enable estimation of the impact of chamber deformation, caused by joint rotation, on airflow through the chamber. Finally, we created a prototype hand exoskeleton with the chamber parameters yielding the best outcomes.ResultsA rectangular cross-sectional area was preferable to a semi-obround shape for the chamber; wall thickness also impacted performance. Extension joint torque reached 0.33 N-m at a low chamber pressure of 48.3 kPa. The finite element model confirmed that airflow for the rectangular chamber remained high despite deformation resulting from joint rotation. The hand exoskeleton created with the rectangular chambers enabled rapid movement, with a cycle time of 1.1 s for voluntary flexion followed by actuated extension.ConclusionsThe developed soft actuators are feasible for use in promoting finger extension from the palmar side of the hand. This placement utilizes pushing rather than pulling for digit extension, which is more comfortable and safer. The small chamber volumes allow rapid filling and evacuation to facilitate relatively high frequency finger movements.

Rational design of Co sub-nano clusters by electronic metal-support interaction for inducing Li2O2 growth

The cathodic electrocatalyst for Li-O2 battery with rich and effective active sites was significant to optimize the formation and decomposition of Li2O2, improve the discharge-charge kinetics. Herein, we develop a novel strategy to controllably prepare sub-nano clusters modified carbon materials. The sub-nano cluster of Co was anchored on the Ketjen Black (KB) substrate and achieved electrocatalytic performance promotion via rational design of the electronic metal-support interaction (EMSI), where the numerous sites in KB with large specific area and geometric space were activated. The experimental and theoretical results indicated that the substrate of the carbon plane could be strongly coupled with the metal atoms and optimized the electronic structure of carbon, leading to effective tuning for Li2O2 growth. The composite exhibited superior performance with high specific capacity of 15311 mAh g−1, low overpotential of 0.88 V and long-term durability of more than 140 cycles with 1000 mAh g−1, strongly related with the optimized Li2O2 growth and decomposition in battery operation. It provides a new strategy for novel electrocatalyst design to tuning the Li2O2 related process.

Exploring Kriging metamodeling to alleviate speed reading dependence in Bridge Weigh-In-Motion predictions

Bridge Weigh-In-Motion (BWIM) systems can be installed and serviced on highway bridges to provide measurements of moving vehicle’s gross weight, axle load, axle separation, and so on, without interrupting traffic. This data can be used to provide information of the heavy traffic over the bridge to management. This article presents the application of Kriging metamodeling to BWIM through the simulation of traffic streams. The Kriging methodology, with its prediction of metamodel variance, allows for the strategic selection of vehicles to train the BWIM metamodel. The proposed approach extends the BWIM concept of influence area from response time histories to geometric input spaces from the strain response wave (RW). The simulated dynamic response of simply supported bridges in the US Interstate Highway System were performed using Finite Element Modeling (FEM) of moving loads, generating RWs captured in different sensor locations. Three independent input spaces based on geometric properties of the RWs are the input to train the Kriging metamodel of a defined training truck group. In this approach, the measurement of the vehicle speed is not needed as an improvement of traditional BWIM methods. The results show that the Kriging prediction can fulfill at least the Type II WIM Systems performance defined in the ASTM’s Standard, using a comparable number of training runs to the number of calibration runs defined in the standard.

GeoExplainer: Interpreting Graph Convolutional Networks with geometric masking

For Graph Convolutional Network (GCN), the inherent geometric topology and relationships within spatio-temporal graphs are critical to factors affect its predictive performance. Understanding the decision-making process of GCN remains a challenging task. Existing perturbation-based explanation methods are primarily tailored for Graph Neural Network (GNN) models and focus on how the information of nodes or subgraphs in static graphs impacts GNN predictions. Due to the geometric properties of spatio-temporal graphs, screw theory serves as crucial mathematical tools for describing the geometric relationships and patterns of motion within the geometric space of spatio-temporal graphs. Therefore,we propose GeoExplainer, which utilizes screw theory to geometric masking for spatio-temporal graphs with strong geometric correlations, generating explanations for GCN. Our method perturbs spatio-temporal graphs using geometric masking, and ultimately generating explanations in the form of geometric masks serve as importance mappings for model prediction results. Firstly, we design a screw motion vector model with geometric constraints to describe the spatio-temporal graph in screw system. Then, we design a geometric screw perturbation to generate smooth geometric masks. Finally, we propose a geometric masking approach to optimize and update the geometric mixture mask. We utilize pointwise mutual information (PMI) to maximize the correlation between the model predictions after geometric perturbation and the original predictions, obtaining the optimal geometric mask as an explanation for GCN. The interpretable experiments of ST-GCN, HD-GCN, GCN and GIN models on four datasets with geometric relationships demonstrate that GeoExplainer outperforms eight competitive interpretation methods in quantitative and visualization analysis.

An efficient angle-based twin random vector functional link classifier

Random vector functional link (RVFL) has always proven to be an excellent classifier in various application areas of machine learning. In this work, inspired by RVFL and its twin variant, i.e., twin RVFL (TRVFL), we propose an efficient angle-based twin random vector functional link (ATRVFL) to address the binary classification problem. ATRVFL is a general classification model in which the first optimization problem can be found by solving a quadratic programming problem (QPP). The second problem can be described as an unconstrained minimization problem (UMP) which is solved by a system of linear equations. ATRVFL finds the solution by solving a QPP and a UMP instead of solving two QPPs. Therefore, its training time is significantly less than TRVFL. To maximize the angle between the normals of the two hyperplanes, the second hyperplane is chosen to be close to its class. The objective of introducing the idea of angle is to maximise the distance between two hyperplanes to improve the ability to discriminate them in geometric spaces. Experimental analyses are performed on a leaf dataset and 25 real-world benchmark datasets (RWDs) collected from the UCI repository of various fields like medical, biological, etc. The results are evaluated based on classification accuracy, the area under the curve, G-mean and F1-score. The results of ATRVFL are compared with Support Vector Machine (SVM), RVFL, Twin-RVFL (TRVFL), RVFL with ε-insensitive Huber loss (ε-HRVFL), ensemble deep learning-based RVFL network (edRVFL) and Intuitionistic fuzzy random vector functional link classifier (IFRVFLC). The classification accuracy of the proposed model on the leaf dataset is 91.093 % and the highest F1-score of 0.768. Overall, based on experimental analysis it is evident that the proposed ATRVFL shows comparative or better performance than SVM, RVFL, TRVFL, ε-HRVFL, IFRVFLC and edRVFL.

The Ego Conquiro as the Paradigm of Modern Imperialism and its Violence Against the Struggle for Epistemic Justice

This article begins with a discussion of the implications of Enrique Dussel’s “I conquer, therefore I am” (ego conquiro) thesis as the paradigm of modern imperialism. According to Dussel, underlying the “myth of modernity” (mito de la modernidad) is an epistemological structure of a substantive ego that naturalizes European colonial expansion. Dussel admits that the “I conquer, therefore, I am” thesis began earlier than Descartes’s account of the cogito, which was theorized after the Spanish colonization of the Americas, but the substantive “I” that constitutes the ego conquiro nonetheless represents a theory of knowledge that served and continues to serve the political domination of bodies that have been historically victimized. Couched within the epistemic structure of Descartes’s cogito is a duality between soul and matter, where the former represents an immortal substance detached from the body, taken to be the recipe for rational truth, while the latter is reduced to a quantifiable object occupying a “zero-point” geometrical space. But the cogito became the epistemological standpoint that allowed for the justification of the many hidden forms of domination around the world, because it masks the social, economic, and geopolitical contexts and their history to the modern subject. This article will investigate the ego conquiro thesis and its link to epistemic violence, namely, epistemic racism and epistemicide, and will argue as a concluding point for a method of resistance that foregrounds philosophical pluralism to disrupt the internalization of the epistemologies of the Global North as the common-sense position(s) of today.

The graded multidimensional geometry of phenotypic variation and progression in neurodegenerative syndromes.

Clinical variants of Alzheimer's disease and frontotemporal lobar degeneration display a spectrum of cognitive-behavioural changes varying between individuals and over time. Understanding the landscape of these graded individual-/group-level longitudinal variations is critical for precise phenotyping; however, this remains challenging to model. Addressing this challenge, we leverage the National Alzheimer's Coordinating Center database to derive a unified geometric framework of graded longitudinal phenotypic variation in Alzheimer's disease and frontotemporal lobar degeneration. We included three time-point, cognitive-behavioural and clinical data from 390 typical, atypical and intermediate Alzheimer's disease and frontotemporal lobar degeneration variants (114 typical Alzheimer's disease; 107 behavioural variant frontotemporal dementia; 42 motor variants of frontotemporal lobar degeneration; and 103 primary progressive aphasia patients). On this data, we applied advanced data-science approaches to derive low-dimensional geometric spaces capturing core features underpinning clinical progression of Alzheimer's disease and frontotemporal lobar degeneration syndromes. To do so, we first used principal component analysis to derive six axes of graded longitudinal phenotypic variation capturing patient-specific movement along and across these axes. Then, we distilled these axes into a visualisable 2D manifold of longitudinal phenotypic variation using Uniform Manifold Approximation and Projection. Both geometries together enabled the assimilation and inter-relation of paradigmatic and mixed cases, capturing dynamic individual trajectories, and linking syndromic variability to neuropathology and key clinical end-points such as survival. Through these low-dimensional geometries, we show that (i) specific syndromes (Alzheimer's disease and primary progressive aphasia) converge over time into a de-differentiated pooled phenotype, while others (frontotemporal dementia variants) diverge to look different from this generic phenotype; (ii) phenotypic diversification is predicted by simultaneous progression along multiple axes, varying in a graded manner between individuals and syndromes; and (iii) movement along specific principal axes predicts survival at 36 months in a syndrome-specific manner and in individual pathological groupings. The resultant mapping of dynamics underlying cognitive-behavioural evolution potentially holds paradigm-changing implications to predicting phenotypic diversification and phenotype-neurobiological mapping in Alzheimer's disease and frontotemporal lobar degeneration.

Kinematic Modelling of Humanoid Robot Based on Vectorial Approach

The present work is dedicated to the study of the inverse kinematics solution of a humanoid bipedal robot with 30 degrees of freedom using a newly approach based on vectors that generated from the geometric space of the robot. The solutions of the inverse kinematics problem for all joints articulations are carried out through a simple and linear equations. Finally, simulations of results are performed through harmonization of trajectories of end effectors of the robot's members.

3D Printing for Customized Bone Reconstruction in Spheno-Orbital Meningiomas: A Systematic Literature Review and Institutional Experience.

Background: The treatment of spheno-orbital meningiomas (SOMs) requires extensive bone resections, creating significant defects in a complex geometrical space. Bone reconstruction represents a fundamental step that optimizes long-term aesthetic and functional outcomes. In recent years, 3D printing technology has also been exploited for complex skull base reconstructions, but reports remain scarce. Methods: We retrospectively analyzed four consecutive patients who underwent SOM resection and one-step 3D PEEK customized reconstruction from 2019 to 2023. A systematic review of 3D printing customized implants for SOM was then performed. Results: All patients underwent a frontotemporal craniotomy, removal of SOM, and reconstruction of the superolateral orbital wall and pterional region. The aesthetic outcome was extremely satisfactory in all cases. No orbital implant malposition or infectious complications were documented. Eleven papers were included in the literature review, describing 27 patients. Most (23) patients underwent a single-stage reconstruction; in three cases, the implant was positioned to correct postoperative delayed enophthalmos. Porous titanium was the most used material (16 patients), while PEEK was used in three cases. Prosthesis malposition was described in two (7.4%) patients. Conclusions: Single-step reconstruction with a personalized 3D PEEK prosthesis represents a valid reconstruction technique for the treatment of SOMs with good aesthetic outcomes.

‘안도다다오 건축에서 나타난 기하학적 공간 특성’에 관한 연구

This study is a research paper that derives the geometric design characteristics that appear in Tadao Ando's architecture. The purpose of this study is to theoretically analyze Tadao Ando's architecture and geometry and derive those characteristics through examples of recent domestic buildings since 2010 by Tadao Ando. . The method of this study is to study literature related to theory and analyze cases through architectural collections. Based on Tadao Ando's architectural concept and the meaning of geometry, the characteristics of combined geometry, disconnected geometry, and independent geometry were derived. In addition, a building case analysis was conducted based on geometric spatial characteristics. Tadao Ando's architecture is composed of six methods: juxtaposition, separation, expansion, overlap, transformation, and absorption, and the substantial form of space becomes existential meaning through spatial recognition and becomes essential architecture. Based on this study, we hope to develop research into the essential concepts of geometric space characteristics that appear in architectural space.

Geometry Interaction Embeddings for Interpolation Temporal Knowledge Graph Completion

Knowledge graphs (KGs) have become a cornerstone for structuring vast amounts of information, enabling sophisticated AI applications across domains. The progression to temporal knowledge graphs (TKGs) introduces time as an essential dimension, allowing for a dynamic representation of entity relationships. Despite their potential, TKGs often suffer from incompleteness, necessitating the development of temporal knowledge graph completion (TKGC) techniques. These methods, particularly focusing on interpolation within the known timeframe, aim to infer missing temporal facts and enhance the predictive capabilities of TKGs. The prevalent reliance on Euclidean space modeling in TKGC methods presents challenges in capturing the complex, hierarchical, and time-varying nature of TKGs. To overcome these limitations, we introduced the attention-based geometry interaction embedding (ATGIE) method, a novel approach that leverages the strengths of multiple geometric spaces, i.e., Euclidean, hyperbolic, and hypersphere, to model the intricacies of TKGs more effectively. ATGIE employs an attention mechanism to dynamically weigh the contributions of different geometric spaces, allowing it to adaptively form reliable spatial structures based on interactive geometric information. This multi-space modeling not only captures the diverse relationships within TKGs but also facilitates a nuanced understanding of how entities and their relationships evolve over time. Through extensive experiments, we demonstrate ATGIE’s superiority in TKGC tasks, showcasing its improvement over existing methods, robustness to noise, and sensitivity to temporal dynamics. The results highlight ATGIE’s potential to advance the state-of-the-art in TKGC, offering a promising direction for research and application in the field.

Molecular Deconvolution of Bone Marrow Adipose Tissue Interactions with Malignant Hematopoiesis: Potential for New Therapy Development.

Along with a strong impact on skeletal integrity, bone marrow adipose tissue (BMAT) is an important modulator of the adult hematopoietic system. This review will summarize the current knowledge on the causal relationship between bone marrow (BM) adipogenesis and the development and progression of hematologic malignancies. BM adipocytes (BMAds) support a number of processes promoting oncogenesis, including the evolution of clonal hematopoiesis, malignant cell survival, proliferation, angiogenesis, and chemoresistance. In addition, leukemic cells manipulate surrounding BMAds by promoting lipolysis and release of free fatty acids, which are then utilized by leukemic cells via β-oxidation. Therefore, limiting BM adipogenesis, blocking BMAd-derived adipokines, or lipid metabolism obstruction have been considered as potential treatment options for hematological malignancies. Leukemic stem cells rely heavily on BMAds within the structural BM microenvironment for necessary signals which foster disease progression. Further development of 3D constructs resembling BMAT at different skeletal regions are critical to better understand these relationships in geometric space and may provide essential insight into the development of hematologic malignancies within the BM niche. In turn, these mechanisms provide promising potential as novel approaches to targeting the microenvironment with new therapeutic strategies.

Space, Sound, and Acousmatic Music. The Heart of the Research

The acoustic and musical relationship with architectural space has a long history: ancient Greeks, the Romanesque Middle Ages, and the Renaissance, for example, utilized it in various ways. Electroacoustic composition on a support (acousmatic music), with its deliberate choice of “nothing to see,” and the acousmonium as an instrument for spatialized performance, serves as a laboratory for researching space as a musical element both during composition and as the principal agent of performance. Four categories of space emerge from this particular practice of interpreting and understanding the acousmatic repertoire: ambiophonic space immerses the listener in a sonic ‘bath’, source space localizes sounds, and geometric space structures a work in planes and volumes. These three categories most often pertain to multiphonic pieces. The fourth, illusion space, is consciously or unconsciously addressed in works in a stereophonic format, which create the illusion of depth of field through two loudspeakers. A few examples, diagrams, and explanations demonstrate how various spatialization systems are designed, particularly the acousmonium as designed by François Bayle in 1974. The performance of an acousmatic work tends to connect various spatial figures that reinforce the composition’s writing, highlight existing figures, or create new ones. Stereophonic works offer the performer greater freedom of choice. Sixteen figures are listed, along with their musical function. Depending on the character of each piece, a different spatial approach can emphasize one aspect of the composition over another: icons, movement, unmixing of polyphony, phrasing and variations, subjectivity, and matter. Thus, we can observe the significant role of the ‘spatializer’ and the necessity of their active presence in concerts. We witness the emergence of a new musical profession with numerous other applications. The spatial writing of multiphonic works also employs these figures in the studio. Some software is dedicated to this function, but multichannel control is essential in the studio. Lastly, figuralism, by playing with spatial figures, appears to be a key approach to giving meaning to and justifying space as an element that enhances the expressiveness of musical works. Annette Vande Gorne’s opera Yawar Fiesta is an exploration of this topic. The spatial projection of music for acousmatic listening – sound in space – opens up the future space to a fifth dimension of expressive music: the Space of Sound.

Fundamental Dynamics of Popularity-Similarity Trajectories in Real Networks.

Real networks are complex dynamical systems, evolving over time with the addition and deletion of nodes and links. Currently, there exists no principled mathematical theory for their dynamics-a grand-challenge open problem. Here, we show that the popularity and similarity trajectories of nodes in hyperbolic embeddings of different real networks manifest universal self-similar properties with typical Hurst exponents H≪0.5. This means that the trajectories are predictable, displaying antipersistent or "mean-reverting" behavior, and they can be adequately captured by a fractional Brownian motion process. The observed behavior can be qualitatively reproduced in synthetic networks that possess a latent geometric space, but not in networks that lack such space, suggesting that the observed subdiffusive dynamics are inherently linked to the hidden geometry of real networks. These results set the foundations for rigorous mathematical machinery for describing and predicting real network dynamics.