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Neighborhood Conditions for Fractional ID-k-factor-critical Graphs 被引量:1
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作者 Si-zhong ZHOU Zhi-ren SUN 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2018年第3期636-644,共9页
Let G be a graph and k≥2 a positive integer. Let h : E(G)→[0, 1] be a function. If e∑eЭxh(e) = k holds for each x∈V(G), then we call G[Fh ] a fractional k-factor of G with indicator function h where Fh ={e... Let G be a graph and k≥2 a positive integer. Let h : E(G)→[0, 1] be a function. If e∑eЭxh(e) = k holds for each x∈V(G), then we call G[Fh ] a fractional k-factor of G with indicator function h where Fh ={e∈E(G) : h(e) 〉 0}. A graph G is fractional independent-set-deletable k-factor-critical(in short, fractional ID-k-factor-critical), if G-I has a fractional k-factor for every independent set I of G. In this paper, we prove that if n≥9 k-14 and for any subset X?V(G) we have NG(X) = V(G) if |X| ≥ kn/(3k-1); or |NG(X)3 k-1/k| ≥|X|if|X|〈 kn/(3k-1),then G is fractional ID-k-factor-critical. 展开更多
关键词 graph neighborhood fractional k-factor fractional ID-k-factor-critical graph
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2-Connected Factor-critical Graphs G with Exactly |E(G)| + 1 Maximum Matchings
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作者 Ming-hua LI Yan LIU 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2017年第4期1001-1014,共14页
A connected graph G is said to be a factor-critical graph if G - v has a perfect matching for every vertex v of G. In this paper, the 2-connected factor-critical graph G which has exactly |E(G)|+ 1 maximum matchi... A connected graph G is said to be a factor-critical graph if G - v has a perfect matching for every vertex v of G. In this paper, the 2-connected factor-critical graph G which has exactly |E(G)|+ 1 maximum matchings is characterized. 展开更多
关键词 maximum matching factor-critical graph 2-connected graph
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The Characterization of p-factor-critical Graphs 被引量:1
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作者 Shao-hui ZHAI Er-ling WEI Fu-ji ZHANG 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2022年第1期154-158,共5页
A graph G is said to be p-factor-critical if G-u1-u2-···-up has a perfect matching for any u1,u2,···,up∈V(G).The concept of p-factor-critical is a generalization of the concepts of facto... A graph G is said to be p-factor-critical if G-u1-u2-···-up has a perfect matching for any u1,u2,···,up∈V(G).The concept of p-factor-critical is a generalization of the concepts of factor-critical and bicritical for p=1 and p=2,respectively.Heping Zhang and Fuji Zhang[Construction for bicritical graphs and k-extendable bipartite graphs,Discrete Math.,306(2006)1415–1423]gave a concise structure characterization of bicritical graphs.In this paper,we present the characterizations of p-factor-critical graphs and minimal p-factorcritical graphs for p≥2.As an application,we also obtain a class of graphs which are minimal p-factor-critical for p≥1. 展开更多
关键词 perfect matching p-factor-critical graph TRANSVERSAL
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INDEPENDENT-SET-DELETABLE FACTOR-CRITICAL POWER GRAPHS 被引量:6
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作者 原晋江 《Acta Mathematica Scientia》 SCIE CSCD 2006年第4期577-584,共8页
It is said that a graph G is independent-set-deletable factor-critical (in short, ID-factor-critical), if, for everyindependent-set I which has the same parity as |V(G)|, G - I has a perfect matching. A graph G ... It is said that a graph G is independent-set-deletable factor-critical (in short, ID-factor-critical), if, for everyindependent-set I which has the same parity as |V(G)|, G - I has a perfect matching. A graph G is strongly IM-extendable, if for every spanning supergraph H of G, every induced matching of H is included in a perfect matching of H. The κ-th power of G, denoted by G^κ, is the graph with vertex set V(G) in which two vertices are adjacent if and only if they have distance at most k in G. ID-factor-criticality and IM-extendability of power graphs are discussed in this article. The author shows that, if G is a connected graph, then G^3 and T(G) (the total graph of G) are ID-factor-critical, and G^4 (when |V(G)| is even) is strongly IM-extendable; if G is 2-connected, then D^2 is ID-factor-critical. 展开更多
关键词 Independent set perfect matching induced matching ID-factor-critical IM-extendable power of a graph
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Remarks on Fractional ID-k-factor-critical Graphs 被引量:1
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作者 Si-zhong ZHOU Lan XU Zu-run XU 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2019年第2期458-464,共7页
Let G be a graph, and k a positive integer. A graph G is fractional independent-set-deletable k-factor-critical(in short, fractional ID-k-factor-critical) if G-I has a fractional k-factor for every independent set I o... Let G be a graph, and k a positive integer. A graph G is fractional independent-set-deletable k-factor-critical(in short, fractional ID-k-factor-critical) if G-I has a fractional k-factor for every independent set I of G. In this paper, we present a sufficient condition for a graph to be fractional ID-k-factor-critical,depending on the minimum degree and the neighborhoods of independent sets. Furthermore, it is shown that this result in this paper is best possible in some sense. 展开更多
关键词 graph NEIGHBORHOOD minimum degree FRACTIONAL K-FACTOR FRACTIONAL ID-k-factor-critical graph
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A Neighborhood Union Condition for Fractional ID-[a, b]-factor-critical Graphs 被引量:3
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作者 Yuan YUAN Rong-Xia HAO 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2018年第4期775-781,共7页
Let a, b, r be nonnegative integers with 1 ≤ a ≤ b and r ≥ 2. Let G be a graph of order n with n 〉(a+2 b)(r(a+b)-2)/b.In this paper, we prove that G is fractional ID-[a, b]-factor-critical if δ(G)≥bn/a... Let a, b, r be nonnegative integers with 1 ≤ a ≤ b and r ≥ 2. Let G be a graph of order n with n 〉(a+2 b)(r(a+b)-2)/b.In this paper, we prove that G is fractional ID-[a, b]-factor-critical if δ(G)≥bn/a+2 b+a(r-1)and |NG(x1) ∪ NG(x2) ∪…∪ NG(xr)| ≥(a+b)n/(a+2 b) for any independent subset {x1,x2,…,xr} in G. It is a generalization of Zhou et al.'s previous result [Discussiones Mathematicae Graph Theory, 36: 409-418(2016)]in which r = 2 is discussed. Furthermore, we show that this result is best possible in some sense. 展开更多
关键词 graph minimum degree NEIGHBORHOOD fractional [a b]-factor fractional ID-[a b]-factor-critical
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Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network
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作者 Pongsakon Promsawat Weerapan Sae-dan +2 位作者 Marisa Kaewsuwan Weerawat Sudsutad Aphirak Aphithana 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期579-607,共29页
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u... The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets. 展开更多
关键词 graph neural networks convolutional neural network deep learning dynamic multi-graph SPATIO-TEMPORAL
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Binding Numbers for Fractional ID-k-factor-critical Graphs
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作者 Si Zhong ZHOU 《Acta Mathematica Sinica,English Series》 SCIE CSCD 2014年第1期181-186,共6页
LetG be a graph,and k≥2 be a positive integer.A graph G is fractional independentset-deletable k-factor-critical(in short,fractional ID-k-factor-critical),if G I has a fractional k-factor for every independent set ... LetG be a graph,and k≥2 be a positive integer.A graph G is fractional independentset-deletable k-factor-critical(in short,fractional ID-k-factor-critical),if G I has a fractional k-factor for every independent set I of G.The binding number bind(G)of a graph G is defined as bind(G)=min|NG(X)||X|:=X V(G),NG(X)=V(G).In this paper,it is proved that a graph G is fractional ID-k-factor-critical if n≥6k 9 and bind(G)〉(3k 1)(n 1)kn 2k+2. 展开更多
关键词 graph binding number independent set fractionalk-factor fractional ID-k-factor-criti-cal
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Graph Transformers研究进展综述 被引量:1
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作者 周诚辰 于千城 +2 位作者 张丽丝 胡智勇 赵明智 《计算机工程与应用》 CSCD 北大核心 2024年第14期37-49,共13页
随着图结构数据在各种实际场景中的广泛应用,对其进行有效建模和处理的需求日益增加。Graph Transformers(GTs)作为一类使用Transformers处理图数据的模型,能够有效缓解传统图神经网络(GNN)中存在的过平滑和过挤压等问题,因此可以学习... 随着图结构数据在各种实际场景中的广泛应用,对其进行有效建模和处理的需求日益增加。Graph Transformers(GTs)作为一类使用Transformers处理图数据的模型,能够有效缓解传统图神经网络(GNN)中存在的过平滑和过挤压等问题,因此可以学习到更好的特征表示。根据对近年来GTs相关文献的研究,将现有的模型架构分为两类:第一类通过绝对编码和相对编码向Transformers中加入图的位置和结构信息,以增强Transformers对图结构数据的理解和处理能力;第二类根据不同的方式(串行、交替、并行)将GNN与Transformers进行结合,以充分利用两者的优势。介绍了GTs在信息安全、药物发现和知识图谱等领域的应用,对比总结了不同用途的模型及其优缺点。最后,从可扩展性、复杂图、更好的结合方式等方面分析了GTs未来研究面临的挑战。 展开更多
关键词 graph Transformers(GTs) 图神经网络 图表示学习 异构图
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基于Graph Transformer的半监督异配图表示学习模型
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作者 黎施彬 龚俊 汤圣君 《计算机应用》 CSCD 北大核心 2024年第6期1816-1823,共8页
现有的图卷积网络(GCN)模型基于同配性假设,无法直接应用于异配图的表示学习,且许多异配图表示学习的研究工作受消息传递机制的限制,导致节点特征混淆和特征过度挤压而出现过平滑问题。针对这些问题,提出一种基于Graph Transformer的半... 现有的图卷积网络(GCN)模型基于同配性假设,无法直接应用于异配图的表示学习,且许多异配图表示学习的研究工作受消息传递机制的限制,导致节点特征混淆和特征过度挤压而出现过平滑问题。针对这些问题,提出一种基于Graph Transformer的半监督异配图表示学习模型HPGT(HeteroPhilic Graph Transformer)。首先,使用度连接概率矩阵采样节点的路径邻域,再通过自注意力机制自适应地聚合路径上的节点异配连接模式,编码得到节点的结构信息,用节点的原始属性信息和结构信息构建Transformer层的自注意力模块;其次,将每个节点自身的隐层表示与它的邻域节点的隐层表示分离更新以避免节点通过自注意力模块聚合过量的自身信息,再把每个节点表示与它的邻域表示连接,得到单个Transformer层的输出,另外,将所有的Transformer层的输出跳连到最终的节点隐层表示以防止中间层信息丢失;最后,使用线性层和Softmax层将节点的隐层表示映射到节点的预测标签。实验结果表明,与无结构编码(SE)的模型相比,基于度连接概率的SE能为Transformer层的自注意力模块提供有效的偏差信息,HPGT平均准确率提升0.99%~11.98%;与对比模型相比,在异配数据集(Texas、Cornell、Wisconsin和Actor)上,模型节点分类准确率提升0.21%~1.69%,在同配数据集(Cora、CiteSeer和PubMed)上,节点分类准确率分别达到了0.8379、0.7467和0.8862。以上结果验证了HPGT具有较强的异配图表示学习能力,尤其适用于强异配图节点分类任务。 展开更多
关键词 图卷积网络 异配图 图表示学习 graph Transformer 节点分类
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GraphMLP-Mixer:基于图-多层感知机架构的高效多行为序列推荐方法
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作者 卢晓凯 封军 +2 位作者 韩永强 王皓 陈恩红 《计算机研究与发展》 EI CSCD 北大核心 2024年第8期1917-1929,共13页
在多行为序列推荐领域,图神经网络(GNNs)虽被广泛应用,但存在局限性,如对序列间协同信号建模不足和处理长距离依赖性等问题.针对这些问题,提出了一种新的解决框架GraphMLP-Mixer.该框架首先构造全局物品图来增强模型对序列间协同信号的... 在多行为序列推荐领域,图神经网络(GNNs)虽被广泛应用,但存在局限性,如对序列间协同信号建模不足和处理长距离依赖性等问题.针对这些问题,提出了一种新的解决框架GraphMLP-Mixer.该框架首先构造全局物品图来增强模型对序列间协同信号的建模,然后将感知机-混合器架构与图神经网络结合,得到图-感知机混合器模型对用户兴趣进行充分挖掘.GraphMLP-Mixer具有2个显著优势:一是能够有效捕捉用户行为的全局依赖性,同时减轻信息过压缩问题;二是其时间与空间效率显著提高,其复杂度与用户交互行为的数量成线性关系,优于现有基于GNN多行为序列推荐模型.在3个真实的公开数据集上进行实验,大量的实验结果验证了GraphMLP-Mixer在处理多行为序列推荐问题时的有效性和高效性. 展开更多
关键词 多行为建模 序列推荐 图神经网络 MLP架构 全局物品图
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基于GraphSAGE网络的藏文短文本分类研究
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作者 敬容 杨逸民 +3 位作者 万福成 国旗 于洪志 马宁 《中文信息学报》 CSCD 北大核心 2024年第9期58-65,共8页
文本分类是自然语言处理领域的重要研究方向,由于藏文数据的稀缺性、语言学特征抽取的复杂性、篇章结构的多样性等因素导致藏文文本分类任务进展缓慢。因此,该文以图神经作为基础模型进行改进。首先,在“音节-音节”“音节-文档”建模... 文本分类是自然语言处理领域的重要研究方向,由于藏文数据的稀缺性、语言学特征抽取的复杂性、篇章结构的多样性等因素导致藏文文本分类任务进展缓慢。因此,该文以图神经作为基础模型进行改进。首先,在“音节-音节”“音节-文档”建模的基础上,融合文档特征,采用二元分类模型动态网络构建“文档-文档”边,以充分挖掘短文本的全局特征,增加滑动窗口,减少模型的计算复杂度并寻找最优窗口取值。其次,针对藏文短文本的音节稀疏性,首次引入GraphSAGE作为基础模型,并探究不同聚合方式在藏文短文本分类上的性能差异。最后,为捕获节点间关系的异质性,对邻居节点进行特征加权再平均池化以增强模型的特征提取能力。在TNCC标题文本数据集上,该文模型的分类准确率达到了62.50%,与传统GCN、原始GraphSAGE和预训练语言模型CINO相比,该方法在分类准确率上分别提高了2.56%、1%和2.4%。 展开更多
关键词 图神经网络 藏文文本分类 TNCC数据集
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A STABILITY RESULT FOR TRANSLATINGSPACELIKE GRAPHS IN LORENTZ MANIFOLDS
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作者 高雅 毛井 吴传喜 《Acta Mathematica Scientia》 SCIE CSCD 2024年第2期474-483,共10页
In this paper,we investigate spacelike graphs defined over a domain Ω⊂M^(n) in the Lorentz manifold M^(n)×ℝ with the metric−ds^(2)+σ,where M^(n) is a complete Riemannian n-manifold with the metricσ,Ωhas piece... In this paper,we investigate spacelike graphs defined over a domain Ω⊂M^(n) in the Lorentz manifold M^(n)×ℝ with the metric−ds^(2)+σ,where M^(n) is a complete Riemannian n-manifold with the metricσ,Ωhas piecewise smooth boundary,and ℝ denotes the Euclidean 1-space.We prove an interesting stability result for translating spacelike graphs in M^(n)×ℝ under a conformal transformation. 展开更多
关键词 mean curvature flow spacelike graphs translating spacelike graphs maximal spacelike graphs constant mean curvature Lorentz manifolds
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A Value for Games Defined on Graphs
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作者 Néstor Bravo 《Applied Mathematics》 2024年第5期331-348,共18页
Given a graph g=( V,A ) , we define a space of subgraphs M with the binary operation of union and the unique decomposition property into blocks. This space allows us to discuss a notion of minimal subgraphs (minimal c... Given a graph g=( V,A ) , we define a space of subgraphs M with the binary operation of union and the unique decomposition property into blocks. This space allows us to discuss a notion of minimal subgraphs (minimal coalitions) that are of interest for the game. Additionally, a partition of the game is defined in terms of the gain of each block, and subsequently, a solution to the game is defined based on distributing to each player (node and edge) present in each block a payment proportional to their contribution to the coalition. 展开更多
关键词 graph Theory Values for graphs Cooperation Games Potential Function
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Heterophilic Graph Neural Network Based on Spatial and Frequency Domain Adaptive Embedding Mechanism
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作者 Lanze Zhang Yijun Gu Jingjie Peng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1701-1731,共31页
Graph Neural Networks(GNNs)play a significant role in tasks related to homophilic graphs.Traditional GNNs,based on the assumption of homophily,employ low-pass filters for neighboring nodes to achieve information aggre... Graph Neural Networks(GNNs)play a significant role in tasks related to homophilic graphs.Traditional GNNs,based on the assumption of homophily,employ low-pass filters for neighboring nodes to achieve information aggregation and embedding.However,in heterophilic graphs,nodes from different categories often establish connections,while nodes of the same category are located further apart in the graph topology.This characteristic poses challenges to traditional GNNs,leading to issues of“distant node modeling deficiency”and“failure of the homophily assumption”.In response,this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks(SFA-HGNN),which integrates adaptive embedding mechanisms for both spatial and frequency domains to address the aforementioned issues.Specifically,for the first problem,we propose the“Distant Spatial Embedding Module”,aiming to select and aggregate distant nodes through high-order randomwalk transition probabilities to enhance modeling capabilities.For the second issue,we design the“Proximal Frequency Domain Embedding Module”,constructing adaptive filters to separate high and low-frequency signals of nodes,and introduce frequency-domain guided attention mechanisms to fuse the relevant information,thereby reducing the noise introduced by the failure of the homophily assumption.We deploy the SFA-HGNN on six publicly available heterophilic networks,achieving state-of-the-art results in four of them.Furthermore,we elaborate on the hyperparameter selection mechanism and validate the performance of each module through experimentation,demonstrating a positive correlation between“node structural similarity”,“node attribute vector similarity”,and“node homophily”in heterophilic networks. 展开更多
关键词 Heterophilic graph graph neural network graph representation learning failure of the homophily assumption
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Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems
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作者 Sang-min Lee Namgi Kim 《Computers, Materials & Continua》 SCIE EI 2024年第2期1897-1914,共18页
Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been ... Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets. 展开更多
关键词 Deep learning graph neural network graph convolution network graph convolution network model learning method recommender information systems
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An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework
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作者 Yuchen Zhou Hongtao Huo +5 位作者 Zhiwen Hou Lingbin Bu Yifan Wang Jingyi Mao Xiaojun Lv Fanliang Bu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期537-563,共27页
Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to sca... Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements. 展开更多
关键词 graph neural networks hyperbolic graph convolutional neural networks deep graph convolutional neural networks message passing framework
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GATiT:An Intelligent Diagnosis Model Based on Graph Attention Network Incorporating Text Representation in Knowledge Reasoning
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作者 Yu Song Pengcheng Wu +2 位作者 Dongming Dai Mingyu Gui Kunli Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第9期4767-4790,共24页
The growing prevalence of knowledge reasoning using knowledge graphs(KGs)has substantially improved the accuracy and efficiency of intelligent medical diagnosis.However,current models primarily integrate electronic me... The growing prevalence of knowledge reasoning using knowledge graphs(KGs)has substantially improved the accuracy and efficiency of intelligent medical diagnosis.However,current models primarily integrate electronic medical records(EMRs)and KGs into the knowledge reasoning process,ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the text.To better integrate EMR text information,we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning(GATiT),which comprises text representation,subgraph construction,knowledge reasoning,and diagnostic classification.In the text representation process,GATiT uses a pre-trained model to obtain text representations of the EMRs and additionally enhances embeddings by including chief complaint information and numerical information in the input.In the subgraph construction process,GATiT constructs text subgraphs and disease subgraphs from the KG,utilizing EMR text and the disease to be diagnosed.To differentiate the varying importance of nodes within the subgraphs features such as node categories,relevance scores,and other relevant factors are introduced into the text subgraph.Themessage-passing strategy and attention weight calculation of the graph attention network are adjusted to learn these features in the knowledge reasoning process.Finally,in the diagnostic classification process,the interactive attention-based fusion method integrates the results of knowledge reasoning with text representations to produce the final diagnosis results.Experimental results on multi-label and single-label EMR datasets demonstrate the model’s superiority over several state-of-theart methods. 展开更多
关键词 Intelligent diagnosis knowledge graph graph attention network knowledge reasoning
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RSscore:Reaction superiority learned from reaction mapping hypergraph
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作者 Chenyang Xu Lijuan Guo +4 位作者 Kang Zhou Hai Yu Chaoliang Wei Fengqi Fan Lei Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第10期203-215,共13页
The selection of chemical reactions is directly related to the quality of synthesis pathways,so a reasonable reaction evaluation metric plays a crucial role in the design and planning of synthesis pathways.Since react... The selection of chemical reactions is directly related to the quality of synthesis pathways,so a reasonable reaction evaluation metric plays a crucial role in the design and planning of synthesis pathways.Since reaction conditions also need to be considered in synthesis pathway design,a reaction metric that combines reaction time,temperature,and yield is required for chemical reactions of different reaction agents.In this study,a chemical reaction graph descriptor which includes the atom-atom mapping relationship is proposed to effectively describe reactions.Then,through pre-training using graph contrastive learning and fine-tuning through supervised learning,we establish a model for generating the probability of reaction superiority(RSscore).Finally,to validate the effectiveness of the current evaluation index,RSscore is applied in two applications,namely reaction evaluation and synthesis routes analysis,which proves that the RSscore provides an important agents-considered evaluation criterion for computer-aided synthesis planning(CASP). 展开更多
关键词 Computer-aided synthesis planning Neural networks Reaction evaluation indicator Reaction graph graph contractive learning
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A Generalization of Torsion Graph for Modules
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作者 Mohammad Jarrar 《Applied Mathematics》 2024年第7期469-476,共8页
Let R be a commutative ring with identity and M an R-module. In this paper, we relate a graph to M, say Γ(M), provided tsshat when M=R, Γ(M)is exactly the classic zero-divisor graph.
关键词 Commutative Ring graph Anihilator
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