This paper introduces Dynamic Topology Dependency Neural Networks (TDNN), a novel neural network architecture designed to address the limitations of traditional static neural networks when processing dynamic and complex data streams. The core concept of TDNN revolves around a dynamically adaptable neural network topology governed by a reinforcement learning algorithm and a dependency graph. The network learns to optimize its internal connections and topology in real-time based on the input data's evolution. This allows TDNN to achieve more efficient and robust representations and processing capabilities compared to conventional neural networks. Specifically, the algorithm adjusts both connection weights and the topology (adding, removing, or modifying connections) guided by a dependency graph that reflects the interdependent activation states of neurons. The dependency graph evolves during training, forming a 'neural topology map' that captures the underlying structure of the input data. This dynamic adaptation enables TDNN to effectively handle non-stationary data scenarios.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of topology-based generative algorithms to neural networks, aiming to design novel and complex architectures through a self-organizing learning paradigm. We propose a graph-based approach where the network's structure is explicitly defined by the topology of the input data, offering a shift away from purely random networks. The core mechanism involves learning the network's topology and using this topology to guide the learning process, leading to architectures with enhanced predictability and the potential for generating complex patterns. We present a preliminary design and analysis demonstrating the effectiveness of this approach in generating novel neural network topologies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of Graph Neural Networks (GNNs) to the problem of program abstraction and optimization. Traditional approaches to program abstraction often rely on manually crafted rules or limited pattern matching, which can be brittle and difficult to scale. This work proposes a novel framework that leverages the power of GNNs to learn a high-level, abstract representation of program code from its underlying graph structure. The learned representations are then utilized to perform optimizations such as variable renaming, code simplification, and potentially, the generation of equivalent, more efficient code. We demonstrate the feasibility and potential benefits of this approach, highlighting its ability to capture complex relationships within code and offer a more robust and scalable solution compared to rule-based methods. The core claim is to utilize GNNs to learn program code's abstract representation and optimize the code for improved efficiency and maintainability. The core mechanism involves representing code as a graph structure and applying GNNs for both abstraction and optimization. This approach overcomes the limitations of traditional methods by incorporating deep learning capabilities for a more nuanced understanding of program semantics. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to compiler optimization by leveraging the power of Graph Neural Networks (GNNs). Traditional compiler optimization techniques rely heavily on hand-crafted heuristics and static analysis, often struggling to capture complex code relationships and achieve optimal performance. We introduce a framework where compiler optimization is framed as a learning task within a GNN. Code is represented as a graph, with nodes representing individual code elements (e.g., instructions, variables) and edges representing dependencies between them. The GNN learns to propagate information across this graph, effectively capturing the intricate dependencies and potential optimization opportunities within the code. The learned representations are then utilized to guide optimization decisions, leading to improved optimization performance. This work demonstrates a promising new direction for compiler optimization, offering a more intelligent and potentially more efficient method compared to traditional approaches.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems. However, their inherent complexity often leads to a "black box" problem, where it is difficult to understand the reasoning behind their predictions. This paper introduces a novel approach to explain GNN predictions by learning symbolic representations of the graph structure. We propose a model that maps the GNN's activations to a symbolic representation, enabling the generation of human-readable explanations. This method addresses the interpretability challenge in GNNs, offering a pathway to trust and confidence in their predictions. The core claim is that by leveraging symbolic representation learning, we can transform opaque GNN behavior into understandable insights. The proposed mechanism provides a foundation for building more transparent and reliable GNN-based systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to graph embedding that leverages the temporal dynamics of graph relationships. Traditional graph embedding techniques often treat graphs as static structures, failing to capture the evolving nature of connections between nodes. Our method, Temporal Graph Embedding via Relational Dynamics (TGERD), employs recurrent neural networks (RNNs) to model the sequence of changes in relationships, effectively learning representations of nodes based on their temporal evolution. The core innovation lies in a weighted temporal graph convolution, where recent relationship changes are given higher priority, allowing the model to adapt to shifts in the graph's structure over time. We demonstrate that this approach significantly improves graph embedding quality compared to static embedding methods, particularly in scenarios where relationships are not static. The algorithm is designed for flexibility and can be applied to various graph datasets with evolving connections.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to program error diagnosis and repair leveraging Graph Neural Networks (GNNs). Traditional methods for software defect detection and correction often rely on manual inspection or rule-based systems, which can be time-consuming, expensive, and prone to human error. Our approach transforms program code into a graph structure, representing dependencies between code elements. A GNN is then employed to learn these dependencies and identify patterns associated with errors. This learned representation enables automated diagnosis of errors, and crucially, the generation of potential repair code. We demonstrate the feasibility and effectiveness of this method through a conceptual framework and illustrate the potential benefits of automating this critical aspect of software development. The core claim of this work is the utilization of GNNs to analyze program code dependencies for automated error diagnosis and repair. The core mechanism involves representing code as a graph, utilizing GNNs to learn dependencies and error patterns, and generating repair code.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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.
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 explores the development of an emergent semantic network system designed to accelerate and enhance mathematical proof verification. Traditional methods rely heavily on manual verification, which is inherently slow and susceptible to human error. We propose a novel approach leveraging graph neural networks to automatically construct and analyze semantic networks representing mathematical proofs. The core mechanism centers around identifying and representing relationships between statements, lemmas, theorems, and proofs, enabling faster detection of inconsistencies and hidden connections. This system aims to provide a scalable and automated solution for uncovering mathematical truths and streamlining the verification process. The research investigates the effectiveness of the network in identifying potential errors and inconsistencies, offering a significant advancement in the field of mathematical proof verification.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to graph embedding that explicitly models the temporal dynamics of relationships within a graph. Current graph embedding techniques often treat graphs as static structures, neglecting the crucial aspect of how connections evolve over time. This work addresses this limitation by leveraging recurrent neural networks (RNNs) to process sequences of graph snapshots, learning embeddings that capture the directional, strength, and frequency changes in relationships. The core idea is to represent not just the current state of the graph, but the *process* of its evolution. We propose using weighted temporal convolution to capture nuanced changes in the graph's relational dynamics. The resulting embeddings are expected to be more informative and robust for downstream graph-based tasks compared to static embeddings.
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
