153 papers indexed here
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Graph representation learning has emerged as a powerful technique for analyzing complex relationships within graph-structured data. However, existing methods often struggle to effectively capture the temporal dynamics inherent in many real-world graphs, where relationships evolve over time. This research introduces a n...
This paper introduces a novel approach to graph theory based on neural rings – a dynamic neural network representation of graph structure. The core innovation lies in employing a neural ring that learns and adjusts its parameters to optimize the graph's inherent properties, leading to enhanced information propagation e...
This paper investigates a novel dynamic approach to adaptive neural network weight adjustment, specifically focusing on the utilization of graph neural networks (GNNs) for dynamic node weighting. We introduce a method that intelligently adjusts node weights during training based on the network's internal state and data...
This paper presents a novel approach to symbolic manipulation learning using self-supervised learning techniques. We leverage Recurrent Neural Networks (RNNs) to learn manipulation rules directly from data, combined with Graph Representation Learning (GRL) to capture the structural complexities inherent in symbolic dom...
This paper presents a novel approach to visual event detection that leverages multi-scale information fusion. The core idea is to enhance detection accuracy and robustness by integrating visual information at different scales, such as low-level textures and high-level semantic cues. We propose a framework utilizing mul...
Predicting drug-target interactions (DTIs) is a crucial task in drug discovery, aiming to identify potential therapeutic candidates and minimize adverse effects. Traditional methods often rely on high-throughput screening or homology modeling, which are time-consuming and expensive. This paper proposes a novel approach...
This paper presents a novel approach to neural symbolic reasoning by integrating distributed neural networks with knowledge graphs. The core claim is that this hybrid architecture can achieve more efficient and reliable symbolic reasoning, ultimately boosting the generalization capabilities of neural reasoning systems....
This paper introduces Neural Symbolic Time Table Reasoning (NSTI), a novel framework for understanding complex time series data by integrating the strengths of neural networks and symbolic reasoning. The core claim is that combining neural networks with symbolic time table inference provides a more interpretable and ro...
Predicting drug-target interactions (DTIs) is a crucial task in drug discovery, aiming to identify potential therapeutic candidates and minimize adverse effects. Traditional methods often rely on high-throughput screening or homology modeling, which are time-consuming and expensive. This paper proposes a novel approach...
Predicting protein structure from its amino acid sequence remains a central challenge in computational biology. Traditional methods often rely on homology modeling and ab initio approaches, which can be computationally expensive and limited in their accuracy. This work explores the potential of graph embeddings to addr...
Current graph neural networks (GNNs) predominantly operate as passive data processors, lacking the capacity for situated reasoning – the ability to understand and respond to the context of a situation. This paper introduces Cognitive Graph Networks (CGNs), a novel framework designed to overcome this limitation. CGNs in...
This paper introduces a novel approach to knowledge graph reasoning by fusing neural networks with symbolic reasoning techniques. Traditional knowledge graph reasoning methods often rely heavily on probabilistic models, leading to limited interpretability and potentially sacrificing accuracy. Our proposed framework add...
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