基于Graph的铁道工程工期估算算法及实现     

涂玉芬 《温州职业技术学院学报》2011,11(4):52-55

为有效帮助铁道工程技术管理人员进行工程工期估算,合理调度和控制各子工程的施工进度,运用Graph结构中的AOE网分析工期估算算法,可实现计算机辅助管理,有利于科学管理铁道工程。  相似文献   

新时期我国软科学研究前沿热点研究——基于软科学文献的知识图谱分析(2008-2018)     

李文强  李海波  迟永峰  陈娜  汝绪伟 《科技管理研究》2020,40(5):252-261

运用科学计量方法对软科学领域的六大核心期刊(2008—2018年)现状和研究热点进行分析。研究发现,目前我国软科学以国家创新驱动发展战略为主要研究方向,围绕区域创新体系及创新链的热点焦点问题和关键环节开展研究,同时,发现各个参与研究者之间合作紧密。研究运用描述性分析的方法,管窥我国软科学领域研究现状,并对未来发展做出展望,为我国软科学繁荣发展提供参考。  相似文献   

隐私保护研究     

胡建  王亚萍 《科技广场》2014,(9)

伴随着数字化网络技术的发展和应用,许多社交网站被创建,使得关于个人的大量信息被收集和发布。为了能够安全地利用社交网站进行信息交流,社交网站在发布数据的同时也要对用户的个人隐私进行必要的保护,本文对社交网络发布的个人信息隐私保护进行了总结,阐述了个人隐私保护模型,指出了社交网络在数据发布时隐私保护存在的待解决的问题以及面临的挑战。  相似文献   

基于ATML标准的测试流程表示方法研究     

王斯侠  高艳华  张永全  周华光 《人天科学研究》2014,(8):18-22

测试信息标准化描述是实现不同ATS平台之间数据交换的关键。针对地面测控设备种类多样及自动测试系统测试任务复杂的情况,对ATML标准进行了深入研究。分析了ATML标准族的背景、目的、模型结构及组件标准,说明了仪器和测试结果组件在实际应用中的信息描述方法,并结合有限自动机的状态转换图理论,提出了测试流程状态转换图模型。实验中设计了一种测试流程信息标准化描述方法,解决了自动测试系统中的数据交换及信息共享问题。实验结果表明ATML在自动测试领域具有良好的发展前景。  相似文献   

A Dual-Pointer guided transition system for end-to-end structured sentiment analysis with global graph reasoning     

《Information processing & management》2022,59(4):102992

Structured sentiment analysis is a newly proposed task, which aims to summarize the overall sentiment and opinion status on given texts, i.e., the opinion expression, the sentiment polarity of the opinion, the holder of the opinion, and the target the opinion towards. In this work, we investigate a transition-based model for end-to-end structured sentiment analysis task. We design a transition architecture which supports the recognition of all the possible opinion quadruples in one shot. Based on the transition backbone, we then propose a Dual-Pointer module for more accurate term boundary detection. Besides, we further introduce a global graph reasoning mechanism, which helps to learn the global-level interactions between the overlapped quadruples. The high-order features are navigated into the transition system to enhance the final predictions. Extensive experimental results on five benchmarks demonstrate both the prominent efficacy and efficiency of our system. Our model outperforms all baselines in terms of all metrics, especially achieving a 10.5% point gain over the current best-performing system only detecting the holder-target-opinion triplets. Further analyses reveal that our framework is also effective in solving the overlapping structure and long-range dependency issues.  相似文献   

Contrastive and attentive graph learning for multi-view clustering     

《Information processing & management》2022,59(4):102967

Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering. Most existing studies generate fusion graphs and constrain multi-view consistency by clustering loss. We argue that local pair-view consistency can achieve fine-modeling of consensus information in multiple views. Towards this end, we propose a novel Contrastive and Attentive Graph Learning framework for multi-view clustering (CAGL). Specifically, we design a contrastive fine-modeling in multi-view graph learning using maximizing the similarity of pair-view to guarantee the consistency of multiple views. Meanwhile, an Att-weighted refined fusion graph module based on attention networks to capture the capacity difference of different views dynamically and further facilitate the mutual reinforcement of single view and fusion view. Besides, our CAGL can learn a specialized representation for clustering via a self-training clustering module. Finally, we develop a joint optimization objective to balance every module and iteratively optimize the proposed CAGL in the framework of graph encoder–decoder. Experimental results on six benchmarks across different modalities and sizes demonstrate that our CAGL outperforms state-of-the-art baselines.  相似文献   

MTGCN: A multi-task approach for node classification and link prediction in graph data     

《Information processing & management》2022,59(3):102902

Both node classification and link prediction are popular topics of supervised learning on the graph data, but previous works seldom integrate them together to capture their complementary information. In this paper, we propose a Multi-Task and Multi-Graph Convolutional Network (MTGCN) to jointly conduct node classification and link prediction in a unified framework. Specifically, MTGCN consists of multiple multi-task learning so that each multi-task learning learns the complementary information between node classification and link prediction. In particular, each multi-task learning uses different inputs to output representations of the graph data. Moreover, the parameters of one multi-task learning initialize the parameters of the other multi-task learning, so that the useful information in the former multi-task learning can be propagated to the other multi-task learning. As a result, the information is augmented to guarantee the quality of representations by exploring the complex constructure inherent in the graph data. Experimental results on six datasets show that our MTGCN outperforms the comparison methods in terms of both node classification and link prediction.  相似文献   

Scientific community detection via bipartite scholar/journal graph co-clustering     

Chiara Carusi  Giuseppe Bianchi 《Journal of Informetrics》2019,13(1):354-386

This paper stems from the observation that researchers in different fields tend to publish in different journals. Such a relationship between researchers and journals is quantitatively exploited to identify scientific community clusters, by casting the community detection problem into a co-clustering problem on bipartite graphs. Such an approach has the potential of leading not only to the fine- grained detection of scholar communities based on the similarity of their research activity, but also to the clustering of scientific journals based on which are the most representative of each community. The proposed methodology is purely data-driven and completely unsupervised, and does not rely on any semantics (e.g. keywords or a-priori subjective categories). Moreover, unlike “flat” data structures (e.g. collaboration graphs or citation graphs) our bipartite graph approach blends in a joint structure both the researcher's attitude and interests (i.e., freedom to select the venue where to publish) as well as the community's recognition (i.e., acceptance of the publication on a target journal); as such may perhaps inspire further scientometric evaluation strategies. Our proposed approach is applied to the Italian research system, for two broad areas (ICT and Microbiology&Genetics), and reveals some questionable aspects and community overlaps in the current Italian scientific sectors classification.  相似文献   

基于K-means和Label Propagation的半监督网页分类     

贺松林  张晖 《人天科学研究》2011,10(2):49-51

提出基于图的半监督学习算法,即类别传播算,结合K均值算法改进,用于网页分类。该K均值类别传播方法使用欧式距离的建立带权∈NN图。在这个图中,图节点表示已标记或未标记的网页,边上的权重表示节点的相似度,已标记节点的类别沿着边向邻居节点传播,从而将网页分类问题形式化为类别在图上的传播。结合K均值方法,提高了计算速度以及图方法的归纳能力,经UCI数据集测试,结果表明,此算法比类别传播算法有更好的性能,能够有效地用于半监督网页分类。  相似文献   

融合专题知识和科技文献的科研知识图谱构建     

李娇  孙坦  黄永文  鲜国建  罗婷婷  赵瑞雪 《数字图书馆论坛》2021,(1):2-9

本文为解决领域科技文献与专题价值的割裂问题提出深度融合科技文献、科研活动等科研对象与领域专题数据资源的图谱构建方法。通过主题词关联设计,构建包含期刊论文、期刊、科研机构、科研人员及专题实体类型的科研本体,选取机器学习专题构建科研知识图谱,并基于图数据库Neo4J进行图谱管理与查询验证。该专题科研知识图谱可以支持单实体/属性、多实体事实性问题的复杂图谱查询,有效揭示专题、科技文献的关联关系,具有较强的应用价值,可以为面向文献数据的智能知识服务提供新的思路和方向。  相似文献