This paper proposes a novel approach to representation learning termed "Emergent Topology Learning." The core idea is that neural networks, when appropriately designed, can autonomously discover and represent the underlying topology of complex data distributions. Traditional representation learning often focuses on learning feature representations that capture statistical correlations within the data. However, this approach frequently fails to capture the essential structural relationships, particularly in high-dimensional and non-Euclidean datasets. Our method addresses this limitation by introducing a self-organizing neural network architecture that iteratively refines its internal representation based on topological similarity metrics. Specifically, the network prioritizes connections between nodes that reflect underlying structural similarities, effectively learning the "shape" of the data. This differs significantly from conventional methods, which passively learn from data, and instead employs an active, structure-aware learning process. The resulting network possesses a more robust and interpretable representation, capable of generalizing to unseen data with similar topological characteristics. We formalize this approach with a mathematical framework, detailing the loss function, the iterative refinement process, and the topological similarity metrics utilized. The primary contribution is a demonstrable demonstration of topology learning in a neural network framework, offering a new paradigm for representation learning, particularly in domains like graph data analysis and complex systems modeling.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Existing graph embedding techniques primarily focus on static graph representations, often neglecting the crucial aspect of temporal dynamics inherent in many real-world graphs. This paper introduces a novel approach combining relational graph embeddings with Temporal Graph Neural Networks (TGNNs) to address this limitation. The core idea is to learn embeddings that adaptively reflect changes in the graph's relational structure over time. We propose a framework where a TGNN is trained to process graph data streams, capturing evolving relationships and generating dynamic embeddings. These embeddings are then utilized for downstream tasks requiring understanding of temporal graph patterns. The proposed method offers a significant advancement over static graph embeddings by incorporating temporal information, leading to improved performance in tasks such as node classification, link prediction, and graph representation learning within dynamic environments. The key contributions lie in the integration of TGNNs with relational graph embeddings and the development of a learning strategy that allows for continuous adaptation to temporal changes.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Protein structure prediction remains a grand challenge in computational biology. Traditional methods often struggle to accurately capture the intricate relationships within a protein sequence, leading to suboptimal structural models. This work explores the application of Graph Neural Networks (GNNs) to address this challenge. We hypothesize that by representing protein sequences as graphs, where nodes represent amino acids and edges represent interactions, GNNs can effectively learn and model these complex relationships, ultimately improving the accuracy and efficiency of protein structure prediction. This paper details the framework for utilizing GNNs, focusing on the construction of protein graphs, the design of suitable GNN architectures, and the training process. We demonstrate the potential of this approach and discuss future research directions. The core claim of this work is the utilization of GNNs to enhance protein structure prediction. The core mechanism involves transforming protein sequences into graph structures, leveraging GNNs to learn structural information. This approach represents a novel way to tackle the protein folding problem. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to distributed graph learning utilizing Federated Bayesian Networks (FBNs). The core challenge in training large graph neural networks (GNNs) lies in the substantial computational resources required, often necessitating centralized training environments. Federated Bayesian Networks offer a decentralized solution, enabling learning across multiple clients without direct data sharing. The proposed method involves local training of Bayesian Networks on individual client graph subsets, followed by parameter aggregation by a central server to refine a global Bayesian Network model. This architecture addresses the limitations of traditional GNN training while prioritizing data privacy and mitigating computational demands. The key innovation lies in the synergistic combination of federated learning principles with the probabilistic inference capabilities of Bayesian Networks, resulting in a robust and scalable framework for distributed graph learning. This approach demonstrates the potential for efficient learning from decentralized graph data sources.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to modeling complex systems utilizing multi-scale graph neural networks (MS-GNNs). Complex systems, characterized by interconnected components operating at varying scales, pose significant challenges for traditional modeling techniques. This research proposes a framework that decomposes a complex system into sub-systems defined by their respective scales. Each sub-system is then represented as a graph, and a graph neural network (GNN) is applied to learn patterns and relationships within that scale. A key innovation lies in the multi-scale fusion mechanism, which intelligently integrates information from these GNNs at different scales, ultimately constructing a comprehensive representation of the entire system. The core claim is the design of an MS-GNN capable of capturing the interconnectedness of complex systems across multiple scales, facilitating accurate modeling and prediction. The proposed methodology offers a fundamentally new approach combining the strengths of graph neural networks and multi-scale analysis, paving the way for improved understanding and prediction of complex phenomena.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Traditional methods for causal discovery often face challenges when dealing with intricate, high-dimensional datasets, frequently failing to accurately capture complex causal relationships. Furthermore, these methods typically lack the capability to simulate the consequences of interventions, hindering the ability to validate discovered causal structures and estimate their effects. This paper introduces a novel approach that integrates Graph Neural Networks (GNNs) with a dedicated simulation engine. The GNN learns the underlying graph structure from the data, establishing a representation of the causal relationships. Simultaneously, the simulation engine leverages this learned graph to model the impact of interventions – specifically, the addition or removal of edges – ultimately enabling the estimation of causal effects. This synergistic combination provides a robust framework for both discovering causal relationships and validating their accuracy through simulated interventions, addressing the limitations of existing causal discovery techniques. The proposed method offers a pathway to more reliable causal inference in complex systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The optimization of computational graphs is a critical step in deploying machine learning models, particularly deep neural networks. Traditional manual optimization methods are labor-intensive, require deep domain expertise, and are prone to human error. This paper proposes a novel approach leveraging program synthesis techniques to automate the process of computational graph optimization. The core idea is to treat computational graph optimization as a program synthesis problem, where a synthesis engine generates efficient execution plans based on the graph's structure, target hardware specifications, and performance constraints. We introduce a framework that translates performance objectives (e.g., minimizing latency, maximizing throughput) into formal synthesis targets. The system employs a combination of constraint solving, search algorithms, and potentially machine learning to explore the space of possible execution plans and identify the optimal one. This approach promises to significantly reduce the time and effort required for optimization, improve the accuracy of the resulting plans, and facilitate the deployment of complex models on diverse hardware platforms. The key contribution lies in applying program synthesis to a traditionally manual and complex optimization task, opening new avenues for automated and adaptive model deployment.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to dependency discovery within software systems, leveraging topological information derived from program code. The core idea is to automatically uncover complex dependencies between programs by analyzing control flow graphs, data dependencies, and abstract syntax trees (ASTs). A graph neural network (GNN) algorithm is developed to learn program representations and infer dependencies using graph convolutional operations. Furthermore, execution data is utilized to validate and refine the dependency graph. This method overcomes limitations of existing static analysis and monitoring-based tools by effectively utilizing program abstract representations, enabling the discovery of intricate dependencies often missed by traditional approaches. The research contributes to a more complete understanding of software ecosystems and facilitates improved dependency management, version control, and software evolution.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of Temporal Graph Neural Networks (T-GNNs) for predicting the dynamics of complex systems. Traditional Graph Neural Networks (GNNs) operate on static graphs, failing to capture the inherent temporal evolution present in many real-world scenarios. We propose a novel framework utilizing GNNs that explicitly incorporate time-dependent graph structures, leading to enhanced predictive accuracy. The core of our approach lies in a 'time-aware' graph convolution operation, which integrates past node states and temporal relationships within the graph. This allows the model to learn predictive embeddings that evolve alongside the system's dynamics. Through theoretical analysis and conceptual demonstration, we articulate the benefits of this approach and highlight its potential for applications in diverse domains, including financial markets, biological networks, and other dynamic systems. The resulting T-GNN models demonstrate a significant improvement over static GNNs in predictive accuracy, establishing a new paradigm for modeling and forecasting complex system behavior.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Reinforcement learning (RL) has achieved remarkable success in various domains, but its "black box" nature poses a significant challenge for real-world deployment. Understanding the rationale behind an RL agent's decisions is crucial for trust, debugging, and improving performance. This paper proposes a novel approach to explainable AI (XAI) within reinforcement learning by leveraging causal reasoning. We model the environment and the agent's policy using a causal Bayesian network. By performing inference through this network, we trace the causal chain of events leading to a specific action, providing a transparent explanation. This method moves beyond simply observing the agent's behavior to understanding the underlying reasons for its choices. The core of our approach lies in identifying and representing the causal relationships within the RL system, enabling us to dissect the decision-making process and ultimately build more robust and reliable RL agents. The proposed framework offers a significant step toward interpretable RL and addresses a critical limitation of current techniques. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to neural network design termed Adaptive Neuron Topology Optimization. The core concept revolves around dynamically adjusting the connectivity topology of a neural network based on real-time monitoring of neuron activity. Traditional neural network topology is static, often hindering optimal performance. This work proposes a system leveraging reinforcement learning to optimize the network's topology. Specifically, a reinforcement learning algorithm is employed to modify both connection weights and the connections themselves between neurons, creating a self-adapting topology. The system aims to improve learning efficiency and generalization capabilities by allowing the network to evolve its structure based on the data it is processing. The key innovation lies in the dynamic, data-driven adaptation of the network topology, moving away from pre-defined static architectures. The system's effectiveness is demonstrated through a theoretical framework outlining the core mechanisms and potential benefits.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to neural network design termed Adaptive Neuron Topology Optimization. The core concept revolves around dynamically adjusting the connectivity topology of a neural network based on real-time monitoring of neuron activity. Traditional neural network topology is static, often hindering optimal performance. This work proposes a system leveraging reinforcement learning to optimize the network's topology. Specifically, a reinforcement learning algorithm is employed to modify both connection weights and the connections themselves between neurons, creating a self-adapting topology. The system aims to improve learning efficiency and generalization capabilities by allowing the network to evolve its structure based on the data it is processing. The key innovation lies in the dynamic, data-driven adaptation of the network topology, moving away from pre-defined static architectures. The system's effectiveness is demonstrated through a theoretical framework outlining the core mechanisms and potential benefits.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations