graph neural networks

THE EVOLUTIONARY E-SPORTS CIVILIZATION MODEL (EECM): A Grand Unified Interdisciplinary Theory of Digital-Cognitive Civilization, Cognitive Capital, Artificial Intelligence Augmentation, Neuroeconomic Transformation, Virtual Ontology, and Civilizational Evolution

This paper proposes the Evolutionary E-Sports Civilization Model (EECM), a grand interdisciplinary theoretical framework designed to explain the transformation of gaming, cognition, digital interaction, artificial intelligence, and virtual systems into a new civilizational paradigm. The theory argues that E-sports represents far more than organized competitive gaming; rather, it constitutes a symbolic and structural manifestation of the ongoing transition from industrial civilization toward a digital-cognitive civilization.The framework integrates mathematics, complexity science, neuroscience, psychology, philosophy, sociology, economics, political science, cybernetics, systems theory, information theory, artificial intelligence, and digital ontology into a unified explanatory architecture. It proposes that modern civilization increasingly transforms psychologically meaningful activities into measurable, monetizable, and algorithmically optimized systems of production and social organization.Within this framework, cognition itself becomes a form of capital. Human attention, strategic reasoning, adaptive intelligence, reflex optimization, emotional regulation, collaborative cognition, and digital interaction evolve into economically productive assets. The theory therefore introduces the concept of Cognitive Capital, a post-industrial expansion of classical economic production factors.The EECM framework further argues that artificial intelligence acts as a civilizational amplifier, accelerating the transformation of digital environments into self-organizing socio-economic ecosystems governed increasingly through algorithmic optimization. E-sports is analyzed as an emergent prototype of future virtual civilization structures, where entertainment, labor, economics, governance, identity, and AI converge into unified digital systems.Mathematically, the paper formalizes these transformations using nonlinear dynamical systems, complexity theory, graph theory, information entropy, game theory, network theory, chaos theory, and probabilistic scaling functions. Philosophically, the theory synthesizes concepts from Aristotle, Plato, Nietzsche, Heidegger, Marx, Foucault, Bourdieu, Sartre, Kant, and contemporary philosophy of technology. Psychologically and neuroscientifically, the model incorporates flow theory, predictive processing, dopamine reward systems, cognitive load theory, neural plasticity, and human-machine symbiosis.The framework also explores geopolitical implications, platform sovereignty, AI governance, digital identity formation, algorithmic social structures, metaverse civilization, and the future political economy of virtual systems. Ultimately, the theory proposes that E-sports is not merely a recreational phenomenon but an early-stage manifestation of a broader civilizational transition in which cognition, attention, and digital interaction become the dominant organizational principles of society.

pinn-serving: Derivative-Aware Serving of Physics-Informed Neural Networks

A serving harness and benchmark suite for physics-informed neural networks (PINNs). Introduces derivative-graph baking, which differentiates a tanh MLP symbolically offline and emits the closed-form derivative recursion as a forward-only program, making derivative-valued queries compatible with standard inference backends and quantisation. Includes evidence that output-space accuracy monitoring cannot detect physics degradation at serving time, and a matched comparison against a Crank-Nicolson solver.

Graph Neural Networks for Spatio-Temporal Intrusion Detection

Cross-Database Performance of Speech-Based Graph Neural Networks for Parkinson’s Disease Detection

Graph Neural Networks for Spatio-Temporal Intrusion Detection

Dynamic Network Anomaly Detection via Topological Feature Learning

This paper presents a novel approach to network anomaly detection that leverages topological feature learning to address the limitations of traditional static feature engineering methods. The core idea is to dynamically capture network changes by automatically learning relevant topological features from network graphs. This allows the system to identify anomalies based on shifts in network topology and node behavior. We propose a framework where a graph neural network (GNN) is utilized to learn these dynamic topological features. The learned features are then used in a classification model to detect anomalous nodes or edges. Our approach demonstrates improved accuracy and adaptability compared to traditional methods in dynamic network environments. The key contributions lie in the automated feature extraction process and the utilization of GNNs for capturing temporal dependencies within network structures. The performance is evaluated using synthetic and real-world network datasets.

Temporal Graph Embeddings via Relational Dynamics

This paper introduces a novel framework for generating graph embeddings that explicitly accounts for the temporal dynamics of relationships within a network. Traditional graph embedding techniques often treat graphs as static structures, neglecting the evolving nature of connections and their influence over time. Our approach leverages a recurrent neural network (RNN) coupled with a custom-designed loss function that measures "temporal divergence" – the difference in embeddings of nodes at consecutive time steps – weighted by the strength and type of relationships. This allows the model to learn and represent how relationships change over time, leading to more accurate and robust graph embeddings. We argue that this method represents a significant advancement over existing static embedding techniques and offers the potential to capture emergent network behaviors and predict future network modifications with improved accuracy. The core claim of this work is that incorporating temporal dynamics dramatically enhances graph embedding capabilities. ---

Graph Neural Networks for Modeling Biological Regulatory Networks with Stochasticity

Biological regulatory networks (BRNs) govern cellular processes with inherent stochasticity, reflecting the probabilistic nature of gene expression and protein interactions. Traditional Graph Neural Networks (GNNs) often treat these networks as deterministic, leading to inaccurate representations and predictions. This work proposes a novel framework for modeling BRNs that explicitly incorporates stochasticity. We introduce probabilistic layers within a GNN architecture to represent the uncertainty in network dynamics, drawing inspiration from stochastic differential equations (SDEs). These layers allow the GNN to learn and propagate probabilistic information, capturing the random fluctuations observed in biological systems. Our approach provides a more realistic and robust model of BRNs, offering potential improvements in predicting network behavior and identifying key regulatory nodes. The core claim is to develop GNN architectures that can explicitly model the stochastic nature of biological regulatory networks, accounting for the inherent randomness in gene expression and protein interactions. The core mechanism involves introducing probabilistic layers within the GNN to represent the uncertainty in the network's dynamics, incorporating elements of stochastic differential equations. This addresses a critical gap in current GNN applications to biology, acknowledging the inherent noise in biological systems.

##拓扑优化算法的基于图神经网络的融合

This paper investigates the integration of topology optimization algorithms with graph neural networks (GNNs) to develop a novel framework for efficient and robust optimization of complex topological structures. Traditional topology optimization methods often struggle with intricate designs, necessitating manual configuration. We propose a system that leverages GNNs to dynamically represent and analyze the topology of the problem, accelerating the optimization process. The core mechanism involves constructing a multi-layered graph representing the topological structure, enabling the network to effectively capture and exploit relationships between nodes and edges. The integration of these two powerful tools promises to significantly improve the performance of topology optimization across a range of applications. This work explores the benefits of this combined approach, demonstrating its effectiveness through simulations and preliminary results.

Based on Neural and Symbolic Approaches to Program Generation and Explanation

This paper explores a novel approach to program generation and explanation by integrating the strengths of neural networks and neuro-symbolic reasoning. Current program generation techniques often fall short due to a lack of explicit logical reasoning and interpretability. We propose a framework that combines a neural network encoder to translate problem representations into neural network states and a neuro-symbolic decoder to generate program code based on these states, leveraging knowledge graphs and logical rules. Furthermore, the neuro-symbolic decoder facilitates code explanation, providing a traceable execution path. The core claim is that this hybrid approach surpasses the limitations of single-technique methods, offering a more intelligent and explainable solution for automated program generation and understanding. The methodology outlines a system architecture, detailing the components and their interactions, and highlights the key innovations within the system.

Differential nodal topology in resting-state networks as a potential imaging marker for adolescent bipolar and depressive disorders

Overlapping clinical features and the absence of objective diagnostic markers frequently lead to the misdiagnosis of adolescent bipolar disorder (BD) as major depressive disorder (MDD). However, direct comparisons of functional brain network topology between adolescents with MDD and BD remain limited, particularly regarding their associations with clinical symptom dimensions. A total of 55 adolescents with MDD, 35 with BD, and 44 healthy controls (HC) were recruited. We hypothesized that adolescents with MDD and BD would exhibit distinct patterns of functional brain network organization associated with specific clinical symptoms. Graph-theoretical analyses were used to identify disorder-specific topological alterations, and support vector machine (SVM) models were constructed using significantly altered nodal metrics as classification features, with model performance evaluated using a nested cross-validation framework. Associations between altered nodal metrics and clinical measures were also examined. Compared with MDD patients, adolescents with BD exhibited higher nodal metrics in specific nodes within the default mode network (DMN) and prefrontal regions. Relative to HC, MDD patients showed reduced nodal connectivity and efficiency in visual cortical regions. Correlation analyses revealed that the clustering coefficients of the right dorsolateral superior frontal gyrus and right orbital superior frontal gyrus were positively associated with attention/vigilance performance, whereas the clustering coefficient of the right cuneus was associated with depressive and anxiety symptoms ( p < 0.05). The linear-kernel SVM achieved a mean classification accuracy of 78.5%, a balanced accuracy of 74.0%, and an AUC of 0.739 in distinguishing BD from MDD. Adolescents with MDD and BD exhibited distinct patterns of nodal functional brain network organization, particularly within the default mode, visual, and prefrontal systems. Altered network topology was associated with cognitive and affective symptom dimensions. SVM analyses further suggested that these topological features contain information relevant to differentiating adolescent MDD from BD. These findings provide further insight into the neural mechanisms underlying adolescent affective disorders. Not applicable.

Topological Data Learning: A Deep Learning Model for Analyzing Complex Systems

This paper introduces a novel deep learning model designed for analyzing complex systems by directly processing topological data. The core concept revolves around representing complex data as graphs and leveraging Graph Neural Networks (GNNs) for deep learning. Traditional deep learning methods often struggle with non-Euclidean data, presenting a significant limitation when dealing with systems where relationships are more important than coordinates. This research aims to bridge this gap by providing a framework capable of learning from topological features, ultimately leading to improved insights and predictive capabilities. The model utilizes persistent homology to extract topological features and then feeds them into a GNN for further processing. We demonstrate the potential of this approach through theoretical analysis and discuss its implications for various applications. This work contributes to the growing field of topological data analysis (TDA) by integrating it with the power of deep learning.