139 papers indexed here
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This paper introduces Temporal Topology, a novel system for predicting future topology based on event streams. We propose a Temporal Graph Network, a neural network trained to learn geometric patterns from sequences of events, allowing for dynamic analysis and predictive modeling. The system's core claim centers around...
This paper proposes a novel approach to system security analysis leveraging Graph Neural Networks (GNNs). Traditional security assessments often rely on static analysis and may fail to capture the dynamic and interconnected nature of complex systems. We argue that system vulnerabilities can be effectively modeled as gr...
Graph embedding techniques have become increasingly prevalent in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, traditional graph embedding methods often treat nodes and edges independently, neglecting the crucial relational context that governs the s...
This paper proposes a novel approach to cognitive computing centered around the symbolic simulation of human reasoning. The core idea is to build cognitive systems by explicitly modeling the underlying processes of human thought, leveraging constraints, contextual knowledge, and probabilistic inference. We introduce a...
This paper introduces a novel dynamic graph neural network (D-GNN) architecture, termed "Adaptive Dynamic Graph Neural Network," designed to optimize the learning process of GNNs through dynamic adjustment of the graph's structure and parameters. Traditional GNN approaches often rely on fixed graph structures, limiting...
This paper proposes a novel framework for constructing and reasoning with dynamic semantic graph knowledge. The core idea is to build a graph that not only represents entities and their relationships but also incorporates dynamic semantic states (events, emotions, temporal changes) and their interactions. This dynamic...
This paper proposes a novel framework for modeling social networks based on the principles of empathetic computation. Traditional social network models primarily focus on structural aspects and relationships, often neglecting the crucial role of emotions and behaviors in shaping social interactions. This research intro...
This paper proposes a novel probabilistic inference network designed to leverage the correlations inherent within multi-modal data. The core idea is to construct a network architecture that explicitly models and utilizes relationships between different data modalities. This is achieved through the application of Graph...
This paper proposes a novel approach to decentralized learning utilizing Federated Graph Neural Networks (FedGNNs). The core idea is to facilitate collaborative knowledge discovery across a network of devices by employing locally maintained graph representations and periodically synchronized Graph Neural Networks (GNNs...
Neuro-Symbolic Reasoning via Dynamic Graph Construction (NSRC-DG) presents a novel approach to reasoning that integrates the strengths of neural networks and symbolic reasoning. Traditional reasoning systems often struggle with complex, nuanced inferences requiring deep understanding and logical deduction. This work in...
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Temporal Topology Modeling for Signal Processing addresses the limitations of traditional signal processing methods by introducing a novel temporal topology model. This model leverages the combined power of recurrent neural networks and graph theory to effectively capture the evolution of signal patterns over time. The...
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