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graph neural networks

1,904 papers

#graph neural networks Open access Sep 2026

Self-Supervised Learning for Symbolic Manipulation with Recurrent Neural Networks and Graph Representation Learning

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...

Jincheng Zhang · 0 citations
#graph neural networks Open access Sep 2026

基于多尺度信息融合的视觉事件检测

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...

Jincheng Zhang · 0 citations
#graph neural networks Open access Sep 2026

Graph Neural Networks for Predicting Drug-Target Interactions with Structural Similarity

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...

Jincheng Zhang · 0 citations
#graph neural networks Open access Sep 2026

Distributed Neural Symbolic Reasoning for Knowledge Graph Enhancement

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....

Jincheng Zhang · 0 citations
#graph neural networks Open access Sep 2026

Neural Symbolic Time Table Reasoning (NSTI)

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...

Jincheng Zhang · 0 citations
#graph neural networks Open access Sep 2026

Graph Neural Networks for Predicting Drug-Target Interactions with Structural Similarity

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...

Jincheng Zhang · 0 citations
#graph neural networks Open access Sep 2026

Graph Embeddings for Protein Structure Prediction

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...

Jincheng Zhang · 0 citations
#graph neural networks Open access Sep 2026

Cognitive Graph Networks for Situated Reasoning

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...

Jincheng Zhang · 0 citations
#graph neural networks Open access Sep 2026

基于神经符号融合的知识图谱推理

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...

Jincheng Zhang · 0 citations
#graph neural networks Open access Sep 2026

Graph-Based Implicit Modeling

This paper introduces a novel graph-based implicit modeling framework designed to generate and maintain complex, dynamic models for both biological systems and social systems. The core mechanism centers on the integration of graph neural networks and graph embedding techniques, enabling automated learning and represent...

Jincheng Zhang · 0 citations
#graph neural networks Open access Sep 2026

Graph Neural Networks for Anomaly Detection via Topological Distance

Anomaly detection in complex networks – encompassing areas like fraud detection, intrusion detection, and equipment monitoring – presents a significant challenge. Traditional methods often employ simplistic thresholding techniques, which frequently lack robustness and adaptability to varying network characteristics. Th...

Jincheng Zhang · 0 citations
#graph neural networks Open access Sep 2026

Causal Reasoning Graph Neural Networks in Deep Learning

This paper proposes a novel approach to deep learning that integrates causal reasoning with Graph Neural Networks (GNNs). The core idea is to construct a neural network model capable of learning causal relationships and performing inference based on those relationships. Traditional deep learning models often struggle t...

Jincheng Zhang · 0 citations

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Microsoft Research Blog Jul 13, 2026

Verifying Rust cryptography in SymCrypt, from standards to code

Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.

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