Information Processing & Management最新文献_第6页

Supporting group cruise decisions with online collective wisdom: An integrated approach combining review helpfulness analysis and consensus in social networks 利用在线集体智慧支持集体巡航决策：结合评论有用性分析和社交网络共识的综合方法

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-10-28 DOI: 10.1016/j.ipm.2024.103936

Feixia Ji , Jian Wu , Francisco Chiclana , Qi Sun , Changyong Liang , Enrique Herrera-Viedma

Online cruise reviews provide valuable insights for group cruise evaluations, but the vast quantity and varied quality of reviews pose significant challenges. Further complications arise from the intricate social network structures and divergent preferences among decision-makers (DMs), impeding consensus on cruise evaluations. This paper proposes a novel two-stage methodology to address these issues. In the first stage, an inherent helpfulness level–personalized helpfulness level (IHL–PHL) model is devised to evaluate review helpfulness, considering not only inherent review quality but also personalized relevance to the specific DMs’ contexts. Leveraging deep learning techniques like Sentence-BERT and neural networks, the IHL–PHL model identifies high-quality, highly relevant reviews tailored as decision support data for DMs with limited cruise familiarity. The second stage facilitates consensus among DMs within overlapping social trust networks. A binary trust propagation method is developed to optimize trust propagation across overlapping communities by strategically selecting key bridging nodes. Building upon this, a constrained maximum consensus model is proposed to maximize group agreement while limiting preference adjustments based on trust-constrained willingness, thereby preventing inefficient iterations. The proposed model is verified with a dataset of 7481 reviews for four cruise alternatives. Finally, some comparisons, theoretical and practical implications are provided. Overall, this paper offers a comprehensive methodology for real-world group cruise evaluation, using online reviews from platforms like CruiseCritic as a form of collective wisdom to support decision-making.

在线邮轮评论为团体邮轮评估提供了宝贵的见解，但数量庞大、质量参差不齐的评论带来了巨大的挑战。错综复杂的社会网络结构和决策者（DMs）之间不同的偏好阻碍了在邮轮评价上达成共识，从而使问题变得更加复杂。本文提出了一种新颖的两阶段方法来解决这些问题。在第一阶段，设计了一个固有有用性水平-个性化有用性水平（IHL-PHL）模型来评估评论的有用性，不仅考虑固有的评论质量，还考虑与特定 DMs 情境的个性化相关性。IHL-PHL 模型利用 Sentence-BERT 和神经网络等深度学习技术，识别出高质量、高度相关的评论，为对巡航熟悉程度有限的 DM 量身定制决策支持数据。第二阶段是促进重叠社会信任网络中的 DM 达成共识。我们开发了一种二元信任传播方法，通过战略性地选择关键桥梁节点来优化重叠社区间的信任传播。在此基础上，提出了一种受限最大共识模型，以最大化群体协议，同时限制基于信任受限意愿的偏好调整，从而防止低效迭代。针对四种邮轮备选方案的 7481 条评论数据集对所提出的模型进行了验证。最后，本文还提供了一些比较、理论和实践意义。总之，本文利用 CruiseCritic 等平台的在线评论作为支持决策的一种集体智慧形式，为现实世界的团体邮轮评估提供了一种全面的方法。

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引用次数: 0

Category-guided multi-interest collaborative metric learning with representation uniformity constraints 具有表征统一性约束的分类指导多兴趣协作度量学习

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-10-24 DOI: 10.1016/j.ipm.2024.103937

Long Wang, Tao Lian

Multi-interest collaborative metric learning has recently emerged as an effective approach to modeling the multifaceted interests of a user in recommender systems. However, two issues remain unexplored. (1) There is no explicit guidance for the matching of an item against multiple interest vectors of a user. (2) The desired property of item representations with respect to their categories is overlooked, resulting in that different categories of items are mixed up in the latent space. To overcome these issues, we devise a Category-guided Multi-interest Collaborative Metric Learning model (CMCML) with representation uniformity constraints. CMCML is designed as a novel category-guided Mixture-of-Experts (MoE) architecture, where the gating network leverages the item category to guide the matching of an item against multiple interest vectors of a user, encouraging items with the same category to approach the same interest vector. In addition, we design a user multi-interest uniformity loss and a category-aware item uniformity loss: The former aims to avoid representation degeneration by enlarging the difference among multiple interest vectors of the same user; the latter is tailored to push different categories of items apart in the latent space. Quantitative experiments on Ciao, Epinions and TaFeng demonstrate that our CMCML improves the value of NDCG@20 by 12.41%, 10.89% and 10.39% respectively, compared to other state-of-the-art collaborative metric learning methods. Further qualitative analyses reveal that our CMCML yields a better representation space where items from distinct categories are arranged in different regions with high density.

多兴趣协作度量学习最近已成为推荐系统中模拟用户多方面兴趣的一种有效方法。然而，有两个问题仍有待探索。(1) 没有明确的指南来指导如何将一个项目与用户的多个兴趣向量进行匹配。(2) 忽视了项目表征在其类别方面的理想属性，导致不同类别的项目在潜在空间中混杂在一起。为了克服这些问题，我们设计了一种具有表征统一性约束的类别引导多兴趣协作度量学习模型（CMCML）。CMCML 被设计为一种新颖的类别引导专家混合物（MoE）架构，其中门控网络利用项目类别来引导项目与用户的多个兴趣向量进行匹配，鼓励具有相同类别的项目接近相同的兴趣向量。此外，我们还设计了用户多兴趣一致性损失和类别感知的项目一致性损失：前者的目的是通过扩大同一用户多个兴趣向量之间的差异来避免表征退化；后者则是为了在潜在空间中将不同类别的项目区分开来。在 Ciao、Epinions 和 TaFeng 上进行的定量实验表明，与其他最先进的协作度量学习方法相比，我们的 CMCML 将 NDCG@20 的值分别提高了 12.41%、10.89% 和 10.39%。进一步的定性分析显示，我们的 CMCML 能够产生更好的表示空间，不同类别的项目被高密度地排列在不同的区域。

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引用次数: 0

Graph similarity learning for cross-level interactions 跨级别交互的图相似性学习

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-10-23 DOI: 10.1016/j.ipm.2024.103932

Cuifang Zou, Guangquan Lu, Longqing Du, Xuxia Zeng, Shilong Lin

Graph similarity computation is crucial in fields such as bioinformatics, e.g., identifying compounds with similar biological activities by comparing molecular structural similarities. Traditional methods such as graph edit distance (GED) and maximal common subgraphs suffer from high computational complexity and sensitivity to noise, which limit their practical applications. Existing deep learning methods make it difficult to extract graph features, which affects computational accuracy comprehensively. To address these problems, we propose a new method, CLSim, which improves performance by enhancing feature extraction and improving graph similarity computation. Using the attention mechanism, CLSim first aligns graph pair features to the shared space and aggregates node features into global embeddings. The directionality of the embedding vectors is considered when extracting graph-level features to handle more complex data. In addition, we develop cross-layer feature extraction techniques that combine node-level information with graph-level embeddings to capture detailed node-graph interaction details. Experimental results on three datasets show that CLSim has excellent generalization capabilities and achieves lower error rates compared to the GED approach and the graph neural network baseline. In the worst case, its time complexity remains quadratic. Example query results further validate the effectiveness of the model, providing a more efficient and accurate solutions for graph similarity tasks.

图相似性计算在生物信息学等领域至关重要，例如，通过比较分子结构相似性来识别具有相似生物活性的化合物。图编辑距离（GED）和最大公共子图等传统方法存在计算复杂度高、对噪声敏感等问题，限制了它们的实际应用。现有的深度学习方法难以提取图特征，全面影响了计算精度。针对这些问题，我们提出了一种新方法--CLSim，它通过加强特征提取和改进图相似性计算来提高性能。利用注意力机制，CLSim 首先将图对特征对齐到共享空间，并将节点特征聚合为全局嵌入。在提取图层特征时，会考虑嵌入向量的方向性，以处理更复杂的数据。此外，我们还开发了跨层特征提取技术，将节点级信息与图级嵌入相结合，以捕捉详细的节点-图交互细节。在三个数据集上的实验结果表明，与 GED 方法和图神经网络基线相比，CLSim 具有出色的泛化能力和更低的错误率。在最坏的情况下，其时间复杂度仍为二次方。示例查询结果进一步验证了该模型的有效性，为图形相似性任务提供了更高效、更准确的解决方案。

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引用次数: 0

Heuristic personality recognition based on fusing multiple conversations and utterance-level affection 基于融合多对话和语篇级情感的启发式人格识别

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-10-23 DOI: 10.1016/j.ipm.2024.103931

Haijun He, Bobo Li, Yiyun Xiong, Li Zheng, Kang He, Fei Li, Donghong Ji

Personality Recognition in Conversations (PRC) is a task of significant interest and practical value. Existing studies on the PRC task utilize conversation inadequately and neglect affective information. Considering the way of information processing of these studies is not yet close enough to the concept of personality, we propose the SAH-GCN model for the PRC task in this study. This model initially processes the original conversation input to extract the central speaker feature. Leveraging Contrastive Learning, it continuously adjusts the embedding of each utterance by incorporating affective information to cope with the semantic similarity. Subsequently, the model employs Graph Convolutional Networks to simulate the conversation dynamics, ensuring comprehensive interaction between the central speaker feature and other relevant features. Lastly, it heuristically fuses central speaker features from multiple conversations involving the same speaker into one comprehensive feature, facilitating personality recognition. We conduct experiments using the recently released CPED dataset, which is the personality dataset encompassing affection labels and conversation details. Our results demonstrate that SAH-GCN achieves superior accuracy (+1.88%) compared to prior works on the PRC task. Further analysis verifies the efficacy of our scheme that fuses multiple conversations and incorporates affective information for personality recognition.

对话中的人格识别（PRC）是一项具有重大意义和实用价值的任务。现有的关于人格识别任务的研究对对话的利用不够充分，忽略了情感信息。考虑到这些研究的信息处理方式还不够贴近人格的概念，我们在本研究中提出了针对 PRC 任务的 SAH-GCN 模型。该模型对原始对话输入进行初步处理，以提取说话者的中心特征。利用对比学习（Contrastive Learning）技术，该模型会结合情感信息不断调整每句话的嵌入，以应对语义相似性问题。随后，该模型采用图卷积网络来模拟对话动态，确保中心发言人特征与其他相关特征之间的全面互动。最后，它启发式地将涉及同一发言人的多个对话中的中心发言人特征融合为一个综合特征，从而促进个性识别。我们使用最近发布的 CPED 数据集进行了实验，该数据集是包含感情标签和对话细节的个性数据集。结果表明，在 PRC 任务中，SAH-GCN 的准确率（+1.88%）优于之前的研究。进一步的分析验证了我们融合多个对话和情感信息的人格识别方案的有效性。

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引用次数: 0

LacGCL: Lightweight message masking with linear attention and cross-view interaction graph contrastive learning for recommendation LacGCL：利用线性注意力和跨视图交互图对比学习进行轻量级信息屏蔽，以促进推荐

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-10-23 DOI: 10.1016/j.ipm.2024.103930

Haohe Jia , Peng Hou , Yong Zhou , Hongbin Zhu , Hongfeng Chai

Graph contrastive learning (GCL) has recently attracted significant attention in the field of recommender systems. However, many GCL methods aim to enhance recommendation accuracy by employing dense matrix operations and frequent manipulation of graph structures to generate contrast views, leading to substantial computational resource consumption. While simpler GCL methods have lower computational costs, they fail to fully exploit collaborative filtering information, leading to reduced accuracy. On the other hand, more complex adaptive methods achieve higher accuracy but at the expense of significantly greater computational cost. Consequently, there exists a considerable gap in accuracy between these lightweight models and the more complex GCL methods focused on high accuracy.

To address this issue and achieve high predictive accuracy while maintaining low computational cost, we propose a novel method that incorporates attention-wise graph reconstruction with message masking and cross-view interaction for contrastive learning. The attention-wise graph reconstruction with message masking preserves the structural and semantic information of the graph while mitigating the overfitting problem. Linear attention ensures that the algorithm’s complexity remains low. Furthermore, the cross-view interaction is capable of capturing more high-quality latent features. Our results, validated on four datasets, demonstrate that the proposed method maintains a lightweight computational cost and significantly outperforms the baseline methods in recommendation accuracy.

最近，图形对比学习（GCL）在推荐系统领域引起了极大关注。然而，许多 GCL 方法旨在通过采用密集矩阵运算和频繁操作图结构来生成对比视图，从而提高推荐准确性，这导致了大量的计算资源消耗。虽然较简单的 GCL 方法计算成本较低，但它们无法充分利用协同过滤信息，导致准确性降低。另一方面，更复杂的自适应方法可以获得更高的准确度，但却要以显著增加计算成本为代价。为了解决这个问题，并在保持低计算成本的同时实现高预测准确性，我们提出了一种新方法，该方法结合了带有信息屏蔽和跨视图交互的注意力图重构，用于对比学习。带有信息掩码的注意力导向图重构保留了图的结构和语义信息，同时缓解了过拟合问题。线性注意力确保了算法的低复杂度。此外，跨视图交互能够捕捉到更多高质量的潜在特征。我们在四个数据集上验证的结果表明，所提出的方法保持了较低的计算成本，并且在推荐准确性上明显优于基线方法。

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引用次数: 0

QAIE: LLM-based Quantity Augmentation and Information Enhancement for few-shot Aspect-Based Sentiment Analysis QAIE：基于 LLM 的数量扩增和信息增强技术，适用于基于几个方面的情感分析

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-10-22 DOI: 10.1016/j.ipm.2024.103917

Heng-yang Lu , Tian-ci Liu , Rui Cong , Jun Yang , Qiang Gan , Wei Fang , Xiao-jun Wu

Aspect-based Sentiment Analysis (ABSA) aims to extract fine-grained sentiment information from online reviews. Few-shot ABSA faces challenges with limited labeled data and recent generative models have outperformed traditional classification models. Existing methods use Question Answering (QA) templates with Text-to-Text Transfer Transformer (T5) to extract sentiment elements, introducing a generative sentiment analysis paradigm. However, these models often fail to fully grasp ABSA rules, generating non-standard or incorrect outputs. This issue also arises with large language models (LLMs) due to insufficient labeled data for tuning and learning. Additionally, ABSA datasets often include many short, uninformative reviews, complicating sentiment element extraction in few-shot scenarios. This paper addresses two major challenges in few-shot ABSA: (1) How to let the generative model well understand the ABSA rules under few-shot scenarios. (2) How to enhance the review text with richer information. We propose a Quantity Augmentation and Information Enhancement (QAIE) approach, leveraging LLMs to generate fluent texts and infer implicit information. First, we propose a quantity augmentation module, which leverages the large language model (LLM) to obtain sufficient labeled data for the generative model to learn the ABSA rules better. Then, we introduce an information enhancement module, which brings more informative input to the generative model by enhancing the information in the review. Comprehensive experiments on five ABSA tasks using three widely-used datasets demonstrate that our QAIE model achieves approximately 10% improvement over state-of-the-art models. Specifically, for the most challenging ASQP task, our LLM-based model is compared with the existing state-of-the-art models on datasets Rest15 and Rest16, achieving F1 gains of 9.42% and 6.45% respectively in the

k = 5

few-shot setting.

基于方面的情感分析（ABSA）旨在从在线评论中提取细粒度的情感信息。由于标注的数据有限，很少有样本的 ABSA 面临着挑战，而最近的生成模型在性能上优于传统的分类模型。现有方法使用带有文本到文本转换器（T5）的问题解答（QA）模板来提取情感元素，从而引入了一种生成式情感分析范式。然而，这些模型往往无法完全掌握 ABSA 规则，从而产生非标准或不正确的输出。由于用于调整和学习的标注数据不足，大型语言模型（LLM）也会出现这个问题。此外，ABSA 数据集通常包含许多短小、无信息量的评论，这就使少量评论场景中的情感元素提取变得更加复杂。本文主要探讨了少量评论 ABSA 的两大挑战：（1）如何让生成模型很好地理解少量评论场景下的 ABSA 规则。(2) 如何用更丰富的信息来增强评论文本。我们提出了一种数量增强和信息增强（QAIE）方法，利用 LLM 生成流畅的文本并推断隐含信息。首先，我们提出了一个数量增强模块，利用大语言模型（LLM）获得足够的标注数据，以便生成模型更好地学习 ABSA 规则。然后，我们引入了信息增强模块，通过增强评论中的信息为生成模型带来更多信息输入。利用三个广泛使用的数据集对五项 ABSA 任务进行的综合实验表明，我们的 QAIE 模型比最先进的模型提高了约 10%。具体来说，在最具挑战性的 ASQP 任务中，我们基于 LLM 的模型与现有数据集 Rest15 和 Rest16 上的先进模型进行了比较，在 k=5 few-shot 设置下，F1 增益分别为 9.42% 和 6.45%。

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引用次数: 0

A hybrid feature fusion deep learning framework for multi-source medical image analysis 用于多源医学图像分析的混合特征融合深度学习框架

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-10-20 DOI: 10.1016/j.ipm.2024.103934

Qiang Cao , Xian Cheng

Despite the widespread adoption of deep learning to enhance image classification, significant obstacles remain. First, multisource data with diverse sizes and formats is a great challenge for most current deep learning models. Second, lacking manual labeled data for model training limits the application of deep learning. Third, the widely used CNN-based methods shows their limitations in extracting global features and yield poor performance for image topology. To address these issues, we propose a Hybrid Feature Fusion Deep Learning (HFFDL) framework for image classification. This framework consists of an automated image segmentation module, a two-stream backbone module, and a classification module. The automatic image segmentation module utilizes the U-Net model and transfer learning to detect region of interest (ROI) in multisource images; the two-stream backbone module integrates the Swin Transformer architecture with the Inception CNN, with the aim of simultaneous extracting local and global features for efficient representation learning. We evaluate the performance of HFFDL framework with two publicly available image datasets: one for identifying COVID-19 through X-ray scans of the chest (30,386 images), and another for multiclass skin cancer screening using dermoscopy images (25,331 images). The HFFDL framework exhibited greater performance in comparison to many cutting-edge models, achieving the AUC score 0.9835 and 0.8789, respectively. Furthermore, a practical application study conducted in a hospital, identifying viable embryos using medical images, revealed the HFFDL framework outperformed embryologists.

尽管深度学习已被广泛应用于增强图像分类，但仍存在重大障碍。首先，对于目前大多数深度学习模型来说，不同大小和格式的多源数据是一个巨大的挑战。其次，缺乏用于模型训练的人工标注数据限制了深度学习的应用。第三，广泛使用的基于 CNN 的方法在提取全局特征方面存在局限性，在图像拓扑方面表现不佳。为了解决这些问题，我们提出了一种用于图像分类的混合特征融合深度学习（HFFDL）框架。该框架由自动图像分割模块、双流骨干模块和分类模块组成。自动图像分割模块利用 U-Net 模型和迁移学习来检测多源图像中的感兴趣区域（ROI）；双流骨干模块集成了 Swin Transformer 架构和 Inception CNN，旨在同时提取局部和全局特征，以实现高效的表征学习。我们用两个公开的图像数据集评估了 HFFDL 框架的性能：一个数据集用于通过胸部 X 光扫描（30,386 幅图像）识别 COVID-19，另一个数据集用于使用皮肤镜图像（25,331 幅图像）进行多类皮肤癌筛查。与许多前沿模型相比，HFFDL 框架表现出更高的性能，AUC 分别达到 0.9835 和 0.8789。此外，一项在医院进行的实际应用研究显示，HFFDL 框架在利用医学图像识别存活胚胎方面的表现优于胚胎学家。

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引用次数: 0

Triple Sparse Denoising Discriminantive Least Squares Regression for image classification 用于图像分类的三重稀疏去噪判别最小二乘回归技术

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-10-19 DOI: 10.1016/j.ipm.2024.103922

Jinjin Zhang, Qimeng Fan, Dingan Wang, Pu Huang, Zhangjing Yang

Discriminantive Least Squares Regression (DLSR) is an algorithm that employs

ɛ

-draggings techniques to enhance intra-class similarity. However, it overlooks that an increase in intra-class closeness may simultaneously lead to a decrease in the distance between similar but different classes. To address this issue, we propose a new approach called Triple Sparse Denoising Discriminantive Least Squares Regression (TSDDLSR), which combines three sparsity constraints: sparsity constraints between classes to amplify the growth of the distance between similar classes; sparsity constraints on relaxation matrices to capture more local structure; sparsity constraints on noise matrices to minimize the effect of outliers. In addition, we position the matrix decomposition step in the label space strategically with the objective of enhancing denoising capabilities, safeguarding it from potential degradation, and preserving its underlying manifold structure. Our experiments evaluate the classification performance of the method under face recognition tasks (AR, CMU PIE, Extended Yale B, Georgia Tech, FERET datasets), biometric recognition tasks (PolyU Palmprint dataset), and object recognition tasks (COIL-20, ImageNet datasets). Meanwhile, the results show that TSDDLSR significantly improves classification performance compared to existing methods.

判别最小二乘法回归（DLSR）是一种利用ɛ拖曳技术来提高类内相似度的算法。然而，它忽略了类内相似度的增加可能会同时导致相似但不同类之间距离的减小。为了解决这个问题，我们提出了一种名为三重稀疏去噪最小二乘回归（TSDDLSR）的新方法，它结合了三种稀疏性约束：类间稀疏性约束，以放大相似类间距离的增长；松弛矩阵稀疏性约束，以捕捉更多局部结构；噪声矩阵稀疏性约束，以最小化异常值的影响。此外，我们将矩阵分解步骤战略性地置于标签空间中，目的是增强去噪能力，防止潜在的退化，并保留其底层流形结构。我们的实验评估了该方法在人脸识别任务（AR、CMU PIE、Extended Yale B、Georgia Tech、FERET 数据集）、生物识别任务（PolyU Palmprint 数据集）和物体识别任务（COIL-20、ImageNet 数据集）下的分类性能。同时，研究结果表明，与现有方法相比，TSDDLSR 能显著提高分类性能。

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引用次数: 0

Quantifying the degree of scientific innovation breakthrough: Considering knowledge trajectory change and impact 量化科学创新突破的程度：考虑知识轨迹的变化和影响

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-10-19 DOI: 10.1016/j.ipm.2024.103933

Lin Runhui , Li Yalin , Ji Ze , Xie Qiqi , Chen Xiaoyu

Scientific breakthroughs have the potential to reshape the trajectory of knowledge flow and significantly impact later research. The aim of this study is to introduce the Degree of Innovation Breakthrough (DIB) metric to more accurately quantify the extent of scientific breakthroughs. The DIB metric takes into account changes in the trajectory of knowledge flow, as well as the deep and width of impact, and it modifies the traditional assumption of equal citation contributions by assigning weighted citation counts. The effectiveness of the DIB metric is assessed using ROC curves and AUC metrics, demonstrating its ability to differentiate between high and low scientific breakthroughs with high sensitivity and minimal false positives. Based on ROC curves, this study proposes a method to calculate the threshold for high scientific breakthrough, reducing subjectivity. The effectiveness of the proposed method is demonstrated through a dataset consisting of 1108 award-winning computer science papers and 9832 matched control papers, showing that the DIB metric surpasses single-dimensional metrics. The study also performs a granular analysis of the innovation breakthrough degree of non-award-winning papers, categorizing them into four types based on originality and impact through 2D histogram visualization, and suggests tailored management strategies. Through the adoption of this refined classification strategy, the management of innovation practices can be optimized, ultimately fostering the enhancement of innovative research outcomes. The quantitative tools introduced in this paper offer guidance for researchers in the fields of science intelligence mining and science trend prediction.

科学突破有可能重塑知识流动的轨迹，并对后来的研究产生重大影响。本研究旨在引入 "创新突破度"（DIB）指标，以更准确地量化科学突破的程度。DIB 指标考虑了知识流动轨迹的变化以及影响的深度和广度，并通过分配加权引文次数修改了传统的等量引文贡献假设。我们使用 ROC 曲线和 AUC 指标对 DIB 指标的有效性进行了评估，结果表明该指标能够以较高的灵敏度和最小的误报率区分高科学突破和低科学突破。基于 ROC 曲线，本研究提出了一种计算高科学突破阈值的方法，减少了主观性。通过一个由 1108 篇获奖计算机科学论文和 9832 篇匹配对照论文组成的数据集，证明了所提方法的有效性，表明 DIB 指标超越了单维指标。研究还对非获奖论文的创新突破程度进行了细化分析，通过二维直方图可视化将非获奖论文根据原创性和影响力分为四种类型，并提出了有针对性的管理策略。通过采用这种精细化分类策略，可以优化创新实践管理，最终促进创新研究成果的提升。本文介绍的定量工具为科学情报挖掘和科学趋势预测领域的研究人员提供了指导。

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引用次数: 0

Examining communication network behaviors, structure and dynamics in an organizational hierarchy: A social network analysis approach 研究组织层级中的通信网络行为、结构和动态：社会网络分析方法

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-10-19 DOI: 10.1016/j.ipm.2024.103927

Tao Wen , Yu-wang Chen , Tahir Abbas Syed , Darminder Ghataoura

Effectively understanding and enhancing communication flows among employees within an organizational hierarchy is crucial for optimizing operational and decision-making efficiency. To fill this significant gap in research, we propose a systematic and comprehensive social network analysis approach, coupled with a newly formulated communication vector and matrix, to examine communication behaviors and dynamics in an organizational hierarchy. We use the Enron email dataset, consisting of 619,499 emails, as an illustrative example to bridge the micro-macro divide of organizational communication research. A series of centrality measures are employed to evaluate the influential ability of individual employees, revealing descending influential ability and changing behaviors according to hierarchy. We also uncover that employees tend to communicate within the same functional teams through the identification of community structure and the proposed communication matrix. Furthermore, the emergent dynamics of organizational communication during a crisis are examined through a time-segmented dataset, showcasing the progressive absence of the legal team, the responsibility of top management, and the presence of hierarchy. By considering both individual and organizational perspectives, our work provides a systematic and data-driven approach to understanding how the organizational communication network emerges dynamically from individual communication behaviors within the hierarchy, which has the potential to enhance operational and decision-making efficiency within organizations.

有效了解和加强组织层级中员工之间的沟通对于优化运营和决策效率至关重要。为了填补这一重大研究空白，我们提出了一种系统而全面的社会网络分析方法，并结合新制定的沟通向量和矩阵，来研究组织层级中的沟通行为和动态。我们以由 619,499 封电子邮件组成的安然电子邮件数据集为例，说明如何弥合组织沟通研究的微观-宏观鸿沟。我们采用了一系列中心度量来评估单个员工的影响能力，揭示了不同层级的员工影响能力和行为变化。我们还通过确定社区结构和提出沟通矩阵，发现员工倾向于在同一职能团队内进行沟通。此外，我们还通过分时数据集研究了危机期间组织沟通的突发动态，展示了法律团队的逐步缺失、高层管理者的责任以及等级制度的存在。通过考虑个人和组织两个视角，我们的工作提供了一种系统化和数据驱动的方法，用于理解组织沟通网络是如何从个人在层级中的沟通行为中动态产生的，这有可能提高组织内的运营和决策效率。

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引用次数: 0