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In [[network theory]], '''link analysis''' is a [[data-analysis]] technique used to evaluate relationships (connections) between nodes. Relationships may be identified among various types of nodes (objects), including [[organization]]s, [[people]] and [[Financial transaction|transactions]]. Link analysis has been used for investigation of criminal activity ([[fraud detection]], [[counterterrorism]], and [[Intelligence (information gathering)|intelligence]]), [[Computer security|computer security analysis]], [[search engine optimization]], [[market research]], [[medical research]], and art.

In [[network theory]], '''link analysis''' is a [[data-analysis]] technique used to evaluate relationships (connections) between nodes. Relationships may be identified among various types of nodes (objects), including [[organization]]s, [[people]] and [[Financial transaction|transactions]]. Link analysis has been used for investigation of criminal activity ([[fraud detection]], [[counterterrorism]], and [[Intelligence (information gathering)|intelligence]]), [[Computer security|computer security analysis]], [[search engine optimization]], [[market research]], [[medical research]], and art.

In network theory, link analysis is a data-analysis technique used to evaluate relationships (connections) between nodes. Relationships may be identified among various types of nodes (objects), including organizations, people and transactions. Link analysis has been used for investigation of criminal activity (fraud detection, counterterrorism, and intelligence), computer security analysis, search engine optimization, market research, medical research, and art.

In network theory, link analysis is a data-analysis technique used to evaluate relationships (connections) between nodes. Relationships may be identified among various types of nodes (objects), including organizations, people and transactions. Link analysis has been used for investigation of criminal activity (fraud detection, counterterrorism, and intelligence), computer security analysis, search engine optimization, market research, medical research, and art.

在网络理论中，链路分析是一种数据分析技术，用于评估节点之间的关系(连接)。可以识别各种类型的节点(对象)之间的关系，包括组织、人员和事务。链接分析被用于犯罪活动的调查(欺诈侦查、反恐和情报)、计算机安全分析、搜索引擎优化安全、市场调查、医学研究和艺术。

在网络理论中，链路分析是一种用于评估节点之间关系(连接)的数据分析技术。该技术可以鉴别各种类型节点(对象)之间的关系，包括组织、人群和市场交易双方。链路分析已被应用于诸多领域，如打击犯罪活动(如欺诈侦查、反恐和情报)、计算机安全分析、搜索引擎优化、市场调查、医学研究和艺术。

Knowledge discovery is an iterative and interactive process used to identify, analyze and visualize patterns in data. Network analysis, link analysis and social network analysis are all methods of knowledge discovery, each a corresponding subset of the prior method. Most knowledge discovery methods follow these steps (at the highest level):

Knowledge discovery is an iterative and interactive process used to identify, analyze and visualize patterns in data. Network analysis, link analysis and social network analysis are all methods of knowledge discovery, each a corresponding subset of the prior method. Most knowledge discovery methods follow these steps (at the highest level):

知识发现是一个迭代和交互的过程，用于识别、分析和可视化数据中的模式。网络分析、链接分析和社会网络分析都是知识发现的方法，每一种都是先验方法的一个子集。大多数知识发现方法遵循以下步骤(在最高级别) :

知识的发现，是指不断地识别、分析和可视化数据中的内在模式，这是一个持续迭代和交互的过程。网络分析、链路分析和社会网络分析都是知识发现的方法，每一种都是先验方法的一个子集。大多数知识发现方法遵循以下几个步骤(在最高级别) :

  Transformation

  Transformation

# [[Data analysis|Analysis]]

# [[Data analysis|Analysis]]

  Analysis

  Analysis

# [[Data visualization|Visualization]]

# [[Data visualization|Visualization]]

  Visualization

  Visualization

Data gathering and processing requires access to data and has several inherent issues, including information overload and data errors. Once data is collected, it will need to be transformed into a format that can be effectively used by both human and computer analyzers. Manual or computer-generated visualizations tools may be mapped from the data, including network charts. Several algorithms exist to help with analysis of data – Dijkstra’s algorithm, breadth-first search, and depth-first search.

Data gathering and processing requires access to data and has several inherent issues, including information overload and data errors. Once data is collected, it will need to be transformed into a format that can be effectively used by both human and computer analyzers. Manual or computer-generated visualizations tools may be mapped from the data, including network charts. Several algorithms exist to help with analysis of data – Dijkstra’s algorithm, breadth-first search, and depth-first search.

数据收集和处理需要访问数据，并存在一些固有的问题，包括信息超载和数据错误。一旦数据被收集，它将需要转换成一种人和计算机分析程序都能有效使用的格式。手工或计算机生成的可视化工具可以根据数据进行映射，包括网络图。有几种算法可以帮助分析数据-Dijkstra 的算法，广度优先搜索和深度优先搜索。

在得到数据后，需进行数据的收集和处理，但此过程存在一些固有的问题，包括信息超载和数据错误。在数据被收集后，它将转换成一种人和计算机分析程序都能有效使用的格式。之后基于数据，人工或计算机生成的可视化工具可以进行作图，包括网络图。目前有几种算法可以帮助进行数据分析-Dijkstra算法，广度优先搜索和深度优先搜索。