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.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to graph embedding that leverages graph neural networks to create a 'meta-graph' of data across multiple scales. The goal is to facilitate the identification of long-range dependencies and non-local relationships within complex datasets. Current methods typically focus on local analysis, restricting the scope of investigation to individual nodes or small clusters. This work proposes a technique that allows for a more comprehensive understanding of the data's structure by representing the data as a network of interconnected nodes, enabling the analysis of relationships that extend beyond immediate neighbors. We demonstrate the effectiveness of this method through a series of quantitative and qualitative analyses, showcasing its ability to uncover hidden patterns and dependencies across multiple scales. The core mechanism involves constructing a meta-graph, where nodes represent data points and edges represent relationships. This facilitates the modeling of complex network structures and the identification of long-range connections.
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
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 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.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph representation learning has emerged as a crucial technique in various domains, including social network analysis, drug discovery, and recommendation systems. However, many existing methods struggle to effectively capture long-range dependencies within graphs, leading to suboptimal representations. This paper introduces a novel approach to graph representation learning based on Contrastive Predictive Coding (CPC). We leverage the inherent predictive structure of graphs by training models to predict future nodes based on their contextual information. This allows us to learn representations that are sensitive to long-range relationships. Our method, CPC-Graph, utilizes a recurrent neural network to encode the graph structure and predict future nodes, minimizing the difference between predicted and actual representations. We demonstrate the effectiveness of CPC-Graph through theoretical analysis and the formulation of key equations, showcasing its ability to capture dependencies efficiently. The core claim of this work is that existing graph representation learning methods often struggle to capture long-range dependencies. This new method employs Contrastive Predictive Coding (CPC) to address this issue, providing a robust solution for learning graph representations.
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 Semantic Web, characterized by linked data and intricate relationships between entities, presents significant challenges for traditional database systems. Existing graph databases often struggle to effectively perform complex reasoning tasks over this data. This paper proposes a novel approach utilizing Graph Neural Networks (GNNs) to address this limitation. We leverage GNNs to learn embeddings of linked data entities and their associated relationships, transforming them into a format conducive to reasoning. These learned embeddings enable capabilities such as inference, query expansion, and knowledge graph completion. The core contribution lies in applying GNNs directly to the complexities of the Semantic Web, offering a powerful and flexible framework for reasoning over linked data. We explore the architecture of the GNN model, discuss the training process, and outline potential applications. The proposed method offers a promising direction for enhancing the intelligence and analytical capabilities of Semantic Web technologies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to graph embedding that leverages graph neural networks to create a 'meta-graph' of data across multiple scales. The goal is to facilitate the identification of long-range dependencies and non-local relationships within complex datasets. Current methods typically focus on local analysis, restricting the scope of investigation to individual nodes or small clusters. This work proposes a technique that allows for a more comprehensive understanding of the data's structure by representing the data as a network of interconnected nodes, enabling the analysis of relationships that extend beyond immediate neighbors. We demonstrate the effectiveness of this method through a series of quantitative and qualitative analyses, showcasing its ability to uncover hidden patterns and dependencies across multiple scales. The core mechanism involves constructing a meta-graph, where nodes represent data points and edges represent relationships. This facilitates the modeling of complex network structures and the identification of long-range connections.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and modeling complex relationships within graphs, finding applications in diverse fields like social network analysis, drug discovery, and computer vision. However, traditional GNN architectures often suffer from limitations in capturing long-range dependencies, hindering their ability to effectively represent and learn from intricate structural information. This paper introduces Adaptive Graph Neural Networks (AGNNs) with a novel temporal memory mechanism, designed to address this challenge. The AGNN dynamically updates the network's memory across time, allowing it to better incorporate past states into its current predictions. We demonstrate the effectiveness of this approach through extensive experiments on several benchmark graph datasets, showcasing significant improvements in performance, particularly in tasks requiring long-range dependency identification. The proposed method offers a fundamentally new approach to GNNs, pushing the boundaries of their ability to handle complex graph structures.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores a novel approach to knowledge graph reasoning utilizing Graph Neural Networks (GNNs). Traditional knowledge graph reasoning methods often struggle with scalability and accurately capturing complex relationships within vast knowledge bases. This work proposes a framework that represents knowledge graphs as graph structures and leverages the power of GNNs for both node classification and relation prediction. The core claim is that GNNs can effectively learn and propagate knowledge across a knowledge graph, ultimately leading to improved reasoning performance. The proposed mechanism utilizes message-passing techniques within GNNs to aggregate information from a node's neighbors, allowing the model to understand the context surrounding each entity and predict relevant relationships. We demonstrate that this approach enhances both the efficiency and accuracy of knowledge graph inference, addressing key limitations of existing methods. The primary contribution lies in the application of GNNs to this domain, offering a scalable and robust solution for knowledge graph reasoning. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
