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
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