越览（126）——精读期刊论文的1方法基础

摘要：This issue will introduce the method basics of the intensively read replica paper "Crowd intelligence knowledge mining method base

分享兴趣，传播快乐，

增长见闻，留下美好。

亲爱的您，这里是LearingYard学苑！

今天小编为您带来“越览（126）——精读期刊论文

《基于共词网络的群智知识挖掘方法

——在应急决策中应用》的

1方法基础”。

欢迎您的访问！

Share interest, spread happiness,

increase Knowledge, and leave beautiful.

Dear, this is the LearingYard Academy!

Today, the editor brings the

＂Yue Lan (126)：Intensive reading of the journal article

＇Crowd intelligence knowledge mining method

based on co-word Network – application

in emergency decision-making’

1 Method basics".

Welcome to visit!

一、内容摘要（Summary of Content）

本期推文将从思维导图、精读内容、知识补充三个方面介绍精读复刻论文《基于共词网络的群智知识挖掘方法——在应急决策中应用》的1方法基础。

This issue will introduce the method basics of the intensively read replica paper "Crowd intelligence knowledge mining method based on co-word network – application in emergency decision-making" in terms of mind maps, intensively read content, and knowledge supplementation.

二、思维导图（Mind Mapping）

三、精读内容（Intensive reading content）

（一）TF⁃IDF算法（TF-IDF algorithm）

本节首先介绍了TF⁃IDF算法的基础知识：TF-IDF是Salton提出的一种计算文档中特征词权重的经典方法。其核心思想是：一个单词若在少量文档中高频出现，则其代表性更强。TF表示特征词在文档中的出现频率，IDF表示特征词在整个语料库中的稀有程度，计算方式为该词出现在语料库中的文档总数与包含该词的文档数的比值的对数。最终，TF与IDF相乘得到TF-IDF值，用于衡量特征词在文档中的重要性。

This section first introduces the basics of the TF-IDF algorithm: TF-IDF is a classic method proposed by Salton to calculate the weight of feature words in a document. The core idea is that if a word appears frequently in a small number of documents, it is more representative. TF represents the frequency of occurrence of a feature word in a document, and IDF represents the rarity of the feature word in the entire corpus. It is calculated as the logarithm of the ratio of the total number of documents in which the word appears in the corpus to the number of documents containing the word. Finally, TF is multiplied by IDF to obtain the TF-IDF value, which is used to measure the importance of the feature word in the document.

（二）Louvain社区检测算法（Louvain community detection algorithm）

本节首先介绍了Louvain社区检测算法的基础知识：Louvain算法是一种基于模块度优化的快速社区检测算法，广泛应用于网络聚类分析。其目标是最大化整个网络的模块度，从而评估社区划分的质量。模块度衡量社区的紧密程度，计算时考虑节点间的连接权重及其变化量ΔQ。

This section first introduces the basics of the Louvain community detection algorithm: The Louvain algorithm is a fast community detection algorithm based on modularity optimization and is widely used in network clustering analysis. Its goal is to maximize the modularity of the entire network to evaluate the quality of community division. Modularity measures the tightness of the community, and the connection weights between nodes and their changes ΔQ are taken into account during calculation.

Louvain算法主要包括两个阶段：首先，将每个节点视为一个独立社区，逐步调整其归属以最大化模块度；然后，将形成的社区折叠为新节点，重复上述过程，直至模块度变化量低于阈值。

The Louvain algorithm mainly consists of two stages: first, each node is regarded as an independent community, and its affiliation is gradually adjusted to maximize the modularity; then, the formed community is collapsed into a new node, and the above process is repeated until the change in modularity is lower than the threshold.

四、知识补充（Knowledge supplement）

网络聚类分析是一种用于发现和划分网络中的社区或簇的技术，其核心目标是识别网络中内部连接紧密、外部连接稀疏的子结构。这些子结构通常对应于具有某种相似特征的节点群体，例如社交网络中的朋友圈、学术合作网络中的研究小组等。

Network clustering analysis is a technique used to discover and divide communities or clusters in a network. Its core goal is to identify substructures in the network that are tightly connected internally and sparsely connected externally. These substructures usually correspond to groups of nodes with similar characteristics, such as friend circles in social networks, research groups in academic collaboration networks, etc.

在网络科学中，“社区”或“簇”指的是一组节点，它们之间的连接比与其他节点的连接更密集。例如，在社交网络中，一个社区可能代表着兴趣相近的用户群体，而在生物网络中，一个簇可能代表着具有相似功能的基因或蛋白质。

In network science, a "community" or "cluster" refers to a group of nodes that are more densely connected to each other than to other nodes. For example, in a social network, a community may represent a group of users with similar interests, while in a biological network, a cluster may represent genes or proteins with similar functions.

网络聚类分析主要有以下几个目标：

Network cluster analysis has the following main goals:

1. 识别网络中的社区结构：找到网络中相互关联紧密的节点集合。

1. Identify community structures in the network: find sets of nodes in the network that are closely connected to each other.

2. 优化网络结构：理解网络中不同部分的作用，优化社交推荐、信息传播等。

2. Optimize network structure: understand the role of different parts of the network, optimize social recommendations, information dissemination, etc.

3. 提高计算效率：在大规模网络中，聚类可以减少计算复杂度，提高数据处理速度。

3. Improve computational efficiency: In large-scale networks, clustering can reduce computational complexity and increase data processing speed.

4. 发现隐藏的模式：揭示网络中可能存在的隐含关系，如病毒传播路径、社交群体影响力等。

4. Discover hidden patterns: reveal potential implicit relationships in the network, such as viral transmission paths, social group influence, etc.

今天的分享就到这里了。

如果您对文章有独特的想法，

欢迎给我们留言，让我们相约明天。

祝您今天过得开心快乐！

That's all for today's sharing.

If you have a unique idea about the article,

please leave us a message,

and let us meet tomorrow.