社交网络1第二版
社交网络分析与舆情应对作业指导书
社交网络分析与舆情应对作业指导书第1章社交网络分析概述 (3)1.1 社交网络的发展历程 (3)1.2 社交网络分析的基本概念 (4)1.3 社交网络分析的应用领域 (4)第2章舆情监控与应对策略 (4)2.1 舆情监控的重要性 (5)2.2 舆情监控的基本流程 (5)2.3 舆情应对策略与技巧 (5)第3章数据收集与预处理 (6)3.1 数据来源及采集方法 (6)3.1.1 社交媒体平台数据 (6)3.1.2 新闻网站数据 (6)3.1.3 论坛与博客数据 (6)3.2 数据预处理技术 (7)3.2.1 数据整合 (7)3.2.2 数据去重 (7)3.2.3 数据标准化 (7)3.2.4 数据归一化 (7)3.3 数据清洗与转换 (7)3.3.1 缺失值处理 (7)3.3.2 异常值处理 (7)3.3.3 数据类型转换 (7)3.3.4 数据降维 (7)3.3.5 数据编码 (7)第4章社交网络结构分析 (7)4.1 社交网络拓扑特性 (7)4.1.1 度分布特性 (8)4.1.2 聚集系数 (8)4.1.3 平均路径长度 (8)4.1.4 网络密度 (8)4.2 社区发觉算法 (8)4.2.1 基于模块度优化的算法 (8)4.2.2 基于图论的算法 (8)4.2.3 基于概率模型的算法 (8)4.2.4 基于深度学习的算法 (8)4.3 网络中心性分析 (8)4.3.1 度中心性 (9)4.3.2 介数中心性 (9)4.3.3 接近中心性 (9)4.3.4 PageRank中心性 (9)4.3.5 结构洞理论 (9)第5章用户行为分析 (9)5.1 用户行为特征提取 (9)5.1.1 用户基本属性特征 (9)5.1.2 用户行为特征 (9)5.1.3 用户兴趣特征 (10)5.2 用户行为模式识别 (10)5.2.1 聚类分析 (10)5.2.2 关联规则分析 (10)5.2.3 时间序列分析 (10)5.3 用户影响力评估 (10)5.3.1 帖子传播力评估 (10)5.3.2 用户互动影响力评估 (10)5.3.3 用户意见领袖识别 (11)第6章舆情传播模型 (11)6.1 经典舆情传播模型 (11)6.1.1 常见舆情传播模型概述 (11)6.1.2 传染病模型 (11)6.1.3 独立级联模型 (11)6.1.4 线性阈值模型 (11)6.2 病毒式营销与舆情扩散 (11)6.2.1 病毒式营销概述 (11)6.2.2 病毒式营销与舆情扩散的关系 (11)6.2.3 病毒式营销策略在舆情应对中的应用 (11)6.3 舆情传播预测与优化 (12)6.3.1 舆情传播预测方法 (12)6.3.2 舆情传播优化策略 (12)6.3.3 舆情应对策略在实际案例中的应用 (12)第7章文本挖掘与情感分析 (12)7.1 文本预处理技术 (12)7.1.1 分词 (12)7.1.2 词性标注 (12)7.1.3 停用词过滤 (12)7.1.4 词干提取与词形还原 (12)7.2 基于机器学习的情感分析方法 (13)7.2.1 支持向量机（SVM） (13)7.2.2 决策树 (13)7.2.3 随机森林 (13)7.2.4 神经网络 (13)7.3 情感极性及强度分析 (13)7.3.1 情感极性分析 (13)7.3.2 情感强度分析 (13)第8章舆情应对策略制定 (13)8.1 舆情应对策略体系构建 (13)8.1.1 策略体系框架 (14)8.1.2 舆情分类与应对策略 (14)8.1.3 舆情应对策略内容 (14)8.2 舆情应对策略评估方法 (14)8.2.1 定性评估方法 (14)8.2.2 定量评估方法 (14)8.2.3 综合评估方法 (14)8.3 舆情应对策略实施与优化 (14)8.3.1 舆情应对策略实施流程 (14)8.3.2 舆情应对策略优化方法 (14)8.3.3 案例分析与启示 (14)8.3.4 持续改进与动态调整 (15)第9章舆情应对案例解析 (15)9.1 网络负面舆情案例分析 (15)9.2 舆情应对成功案例分析 (15)9.3 舆情应对策略改进措施 (16)第10章舆情应对实践与展望 (16)10.1 舆情应对团队建设与管理 (16)10.1.1 团队组织结构设计 (16)10.1.2 团队成员能力要求 (17)10.1.3 团队培训与评估 (17)10.2 舆情应对技术发展动态 (17)10.2.1 舆情监测技术 (17)10.2.2 舆情分析技术 (17)10.2.3 舆情应对策略制定 (17)10.3 未来社交网络分析与舆情应对发展趋势展望 (17)10.3.1 数据驱动与智能化 (17)10.3.2 跨界融合与创新 (17)10.3.3 面向全过程的舆情管理 (17)10.3.4 个性化与定制化服务 (17)10.3.5 法治与道德约束 (18)第1章社交网络分析概述1.1 社交网络的发展历程社交网络作为一种新兴的互联网应用形式，自20世纪90年代以来，互联网技术的飞速发展，已经经历了多个阶段的演变。
社会网络分析与社交关系研究
社会网络分析与社交关系研究社会网络分析（Social Network Analysis，简称SNA）是一种研究人际关系和社交网络的方法，通过对个体之间的联系和互动进行定量和定性的分析，揭示了人际关系的模式和结构。
这一方法在社会学、心理学、管理学等多个领域发挥了重要作用并取得了丰富成果。
社会网络分析首次引入了图论的概念，在网络中将个体表示为节点，联系表示为边。
通过将这些节点和边进行可视化和分析，人们可以揭示出网络中的关键人物、子群体以及信息传播的路径。
这种方法使我们能够更加深刻地理解社会关系的形成和演变。
社交关系研究是社会网络分析的重要组成部分，它聚焦于个体之间的相互作用，包括朋友、家人、同事和其他社交网络中的关系。
社交关系的强弱、多样性和密度等特征对于个体的社会支持和信息获取具有重要影响。
人们通过与其他人建立和维持社交关系，不仅能够获得资源和支持，还能够满足情感和归属感的需求。
社会网络分析和社交关系研究对于解决一系列社会问题具有重要意义。
首先，它们有助于理解信息传播和影响力扩散的模式。
在社交媒体时代，我们每天都面临着大量的信息和观点，而社交网络分析可以帮助我们识别关键意见领袖和信息的传播路径，从而更好地理解和应对信息流动的特点。
其次，社会网络分析和社交关系研究对于社会支持和社会资本的研究非常重要。
社会支持指的是在面临困境和挑战时，人们通过社交网络获得的物质和情感上的帮助。
研究表明，拥有丰富和多样化的社交关系网络可以增强个体的心理健康和适应能力。
社会网络分析可以帮助我们识别出社交网络中的“缺口”和“孤立群体”，从而制定有针对性的干预措施。
此外，社会网络分析还可以应用于犯罪和恐怖主义的研究。
犯罪和恐怖主义往往涉及组织和组织成员之间的联系，而社会网络分析可以帮助我们识别出关键嫌疑人、犯罪集团以及信息的传递路径，从而为相关部门提供指导和决策支持。
最后，社会网络分析和社交关系研究对于组织和团队的管理也具有重要意义。
社交网络分析算法的使用方法
社交网络分析算法的使用方法社交网络已成为人们日常生活中不可或缺的一部分。
通过社交网络，人们可以与朋友、家人、同事和陌生人进行交流和互动。
这些网络提供了丰富的信息和机会，也成为了理解社会关系和人际互动的重要资源。
为了深入了解社交网络中的关系和模式，社交网络分析算法应运而生。
社交网络分析算法是一种用于识别、分析和预测社交网络中的关系模式和趋势的方法。
它结合了图论、统计学和数据挖掘技术，适用于各种类型的社交网络，包括在线社交媒体平台、企业内部网络和科学研究网络等。
下面将介绍几种常用的社交网络分析算法及其使用方法。
1. 社区发现算法社区发现算法旨在识别社交网络中的紧密连接的群体或社区。
常用的算法包括Girvan-Newman算法、Louvain算法和谱聚类算法等。
使用这些算法的步骤如下：首先，导入社交网络数据并构建图模型。
每个节点表示一个用户或个体，边表示两个节点之间的关系。
然后，计算节点之间的相似度或连接强度。
这可以通过计算节点间的距离、共同邻居数或其他相似性指标来实现。
接下来，应用社区发现算法来检测网络中的社区。
这些算法基于节点之间的链接模式来确定社区结构。
最后，可视化社区结构，并根据分析结果进行进一步的解释和推断。
2. 影响力传播算法影响力传播算法用于研究在社交网络中如何传播信息、观点或行为。
其中比较有名的算法是独立级联模型（IC模型）和线性阈值模型（LT模型）。
使用这些算法的步骤如下：首先，确定某个节点或群体作为信息源。
然后，为每个节点分配传播概率或阈值。
这些值表示了节点接受信息并传播给邻居的能力。
接下来，使用影响力传播算法模拟信息在社交网络中的传播过程。
这些算法基于节点之间的连接和传播概率来模拟信息在网络中的扩散。
最后，分析信息传播的规律和影响因素，并根据结果确定改进传播策略的方法。
3. 关键节点识别算法关键节点识别算法用于识别对整个社交网络结构和信息传播具有重要影响力的节点。
常用的算法包括介数中心性、度中心性和PageRank算法等。
社交网络分析：操作指南说明书
Social Network Analysis: ‘How to guide’January 2016ContentsTable of ContentsWhat is social network analysis? (3)What can social network analysis do for me? (3)What will I get at the end of it? (3)What are the limitations? (5)What do I need to complete the analysis? (6)Approach (7)Resource A: Possible data collection process (9)Resource B: Possible analytical approach (13)Disclaimer: ‘The views expressed in this report are those of the authors, not necessarily those of the Home Office (nor do they represent Government policy).’This guide is intended to help local areas and police forces use intelligence data to undertake social network analysis of their local gang issues.What is social network analysis?The aim of social network analysis is to understand a community by mapping the relationships that connect them as a network, and then trying to draw out key individuals, groups within the network (‘components’), and/or associations between the individuals.A network is simply a number of points (or ‘nodes’) that are connected by links. Generally in social network analysis, the nodes are people and the links are any social connection between them – for example, friendship, marital/family ties, or financial ties.What can social network analysis do for me?Social network analysis can provide information about the reach of gangs, the impact of gangs, and gang activity. The approach may also allow you to identify those who may be at risk of gang-association and/or being exploited by gangs.Network analysis can be completed ‘qualitatively’ – that is, with diagrams drawn by hand. This guide details a more systematic approach to network analysis. Particular benefits of this include: ∙Practicality: The approach provides an objective, replicable representation of the community which is described in the intelligence data. It does not need thoseundertaking it to have knowledge of a gang or extensive analytical training.∙Wider applications: It also provides a systematic understanding of local gang issues and the relationship with those who may be seen as gang-associated. This has potential applications for producing community impact statements and particular interventions(e.g. gang injunctions).∙Targeting responses: The process of mapping a gang may allow action to be more closely tailored to specific individuals –for example, differentiating between ‘core’ gang members and peripheral members. This may increase the effectiveness of work totackle gangs and gang culture.∙Multiple uses: The data collection process can be completed centrally and the overall network analysis provided to local teams. The networks can then be examined /manipulated to answer particular local questions as required. This may be more efficient than producing different analytical products for each local issue.What will I get at the end of it?The technique will generate diagrams that will show the relationships between individuals that are contained in your data, this could include: criminal links, social links, potential feuds, etc. Figure 1 below gives an example – to note the diagrams can include names, pictures and further details of individuals as required.Figure 1: Example output (note: numbers here indicate individuals).It is also possible to produce statistical analysis of the networks which can help you to define a problem, and to explore the roles of particular individuals in the networks (see table 1 for some key statistics). This can be completed automatically by the social network analysis software.Table 1: Key network statistics SizeNumber of nodes - the people in the network Size of the networkNumber of individuals inthe networkNumber of links - socialconnections/relationships between nodes (e.g. friendship, family ties) How ‘busy’ the network in totalNumber of relationshipsbetween individuals in thenetwork (in total)Number of unique links How ‘busy’ the network is, takingout relationships that areduplicatedNumber of relationshipsbetween individuals in thenetwork, with duplicatesremovedCohesivenessNumber of components – distinct groups in the network Whether there may be sub-groups in the networkNumber of discrete groupsin the networkDensity The extent to which nodes areinterconnected – lower densitynetworks have fewer linksbetween nodesThe proportion of all linksthat are actually presentDiameter Size of the network Greatest number of steps between any pair of nodesMean average distance between nodes How ‘close’ (in network terms)the nodes are to each otherAverage number of stepsneeded to go from onenode to any otherCentralityMean degree How central (on average) nodesin the network areAverage number of linksthat pass through thenodesMean betweeness How central (on average) nodesin the network areAverage number of uniquepaths that pass through thenodesWhat are the limitations?The analysis is based on intelligence data, which have the potential to be incomplete, inaccurate or untimely. The results may be most usefully considered in combination with other sources of information, and operational experience.The approach described here does not limit itself to identifying gang members. This means that not all those identified in the analysis will necessarily recognise themselves or be recognised by others as being in a gang.What do I need to complete the analysis?SoftwareTo complete the social network analysis, software packages will be needed to complete the following tasks:∙Data collection – E.g. Spreadsheet software;∙Data analysis – E.g. Social network analysis software;∙Data visualisation – E.g. Network visualisation software.Some packages may encompass all three. Packages used to create the analysis in this guide were UCINET1 and Node XL2 package for Microsoft Excel3.ResourcesThe time and number of people required to complete the data collection and analysis will depend on the amount of intelligence that needs to be coded and the speed / familiarity of those undertaking the approach. As a rough rule of thumb, coding intelligence data using the method set out in this guidance should take around 10-15 minutes per intelligence log.1 Borgatti, S.P., Everett, M.G. & Freeman, L.C. (2002). Ucinet for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies.2 Social Media Research Foundation, </>3 For UCINET, the collected data was converted into a format that could be read by the software via a Blitzbasic routine (See / ). The networks produced were then visualised in NetDraw (See<https:///site/netdrawsoftware/download>. This was not needed for Node XL, which is able to read in data that is not in matrix form.ApproachStep 1: Define your focusIn completing a network analysis, it is beneficial to set your focus. This will involve considering various elements of the analysis:The gang you will focus on The decision on what gang youwill focus on may be driven byoperational need, or considerationof impact or geographical areaSocial network analysis maybe most reliably applied to arelatively small area, forwhich data is likely to bemore completeThe individuals within the gang you will focus on A gang as a whole may be toolarge to focus on with availableresourcesAre there key individualswithin this gang you want toknow about?The time period you want to look at Looking at a longer time periodmay provide a more detailedpicture, but takes time to doHas the picture changed overtime?The size of catchment you are aiming for The catchment will influence howlong the process takes4Is this wide enough for theissue you are looking toaddress?Step 2: Decide what data you will useSocial network analysis can be applied to any data that highlights relationships between things (e.g. individuals, objects, events, etc.). When looking at gangs, the approach works best with data that can capture non-criminal as well as criminal links, since a lot of useful information is contained in social links. Because of this, intelligence data may be particularly relevant. However, it can be applied to purely criminal data (e.g. arrests).If using police intelligence data, a decision may need to be made about the grading level of the intelligence that will be included in analysis. The decision will depend on the amount of data held and the reliability of the data, and should be made in consultation with intelligence analysts.Step 3: Collect dataResource A provides a process for data collection using police intelligence data. In summary, intelligence logs will need to be searched for the names of individuals, and the logs coded according to set categories. The information is inputted onto a spreadsheet which then forms the core dataset for the network analysis. An important aspect of the data collection is being sure not to include individuals twice – for example, due to slight differences in names.Step 4: Analyse your findingsSocial network analysis entails exploring the networks you create to investigate particular questions you want to answer. Therefore, there is no set way of undertaking the analysis.4 The approach detailed in this guide follows a 2-step process.However, some questions you might want to ask are provided in Resource B. Statistical analysis of the networks may help you to answer these questions (see table 1 for a selection of the statistics available).By plotting the network’s centrality scores (degree and betweeness, see table 1), you can also examine the role / characteristics of the nodes in the network relative to the others in that network (e.g. by comparing them against the mean average scores). These can be summarised as follows:GatekeepersHigher Lower ∙ May play an important role in activity, but not much information is held on them ∙ Removal may fragment networksHighly visiblefiguresLower Higher ∙ May have information about many others in the network∙ May be involved in lots of activity in the network, but do not play a unique roleCentralfiguresHigher Higher ∙ Very visible and central role∙ Key figures that may be focused on to fragment networks and to gather informationStep 5: Validate your findingsSocial network analysis can only tell you what the intelligence data shows, and will not give you all the context / details around the data. The intelligence picture may be incomplete or misleading in places and certain gang activities may be more visible than others, thus skewing the picture. For example, drug dealing may be more visible than sexual exploitation but both activities may well be occurring. For this reason it is important to validate findings against operational experience. Examples of the types of questions to ask include:∙Do the findings match what is known?∙Is there anything that seems unusual?∙Can any unusual results be explained by issues with the data e.g. the quality or the content of the intelligence log?Validating the data in this way not only helps to quality assure the findings but can also throw up interesting aspects of the data for further exploration.Resource A: Possible data collection processIdentifying sample1. Identify key nominals to begin the process with – for example, known members of an urbanstreet gang. If a large group, randomly select nominals from this list to pick individuals to focus on.2. Perform a search for intelligence logs concerning one of these nominals (‘nominal A’).3. Omit logs from outside the time period wanted.4. Refine results further by identifying and retaining only intelligence logs known to be for onlythis nominal (important to reduce duplication).Data coding5. For each log, code relevant details from table below on a spreadsheet. Coding templates Aand B provide examples of codes to use but these can be refined according to data needs.6. Repeat steps 2-5 for all the other key nominals selected at step 1.7. Once all records pertaining to the key nominals have been identified and coded go to step 2,and repeat for all nominal B’s (i.e. all n amed individuals) with the intelligence logs pertaining to the key nominals. [Note this step can be repeated as many times as is reasonable for any individuals named within subsequent logs. Resource pressures and the value added for each round may be determining factors of how many steps away from the key nominals you want to take].Coding template A: links and attributes1. Link codesa. Intelligence log numberb. Nominal A name / codec. Nominal B name / codeSociald. Unknown relationshipe. Acquaintance / friendf. Businessg. Romantich. Familyi. Financialj. Member of same gangk. Member of different gangl. ‘A’ perpetrated crime against ‘B’m. ‘A’ is victim of crime by ‘B’n. Feud/disputeo. Other (e.g. social services)Criminalp. Legitimate relationshipq. Antisocial behaviourr. Drugss. Firearms accesst. Firearms supplyu. Violent crime without injuryv. Violent crime with injuryw. Crime involving the use of a weaponx. Theft with force or threat of forcey. Theft without force or threat of forcez. Sexual offenceaa. Financial offencebb. Driving-related offencecc. Other criminal activityCausality of criminaldd. To/For/WithOther informationee. Data of contactff. Intelligence gradegg. (1) Link inferred by intel (2) Link inferred by analysthh. Nature of contact2. Attributes codes (if applicable)i. Name / code of nominal Aii. DateCrimeiii. Violence against the person with injuryiv. Violence against the person with injuryv. Threat of violencevi. Homicidevii. Firearms possessionviii. Firearms offencesix. Knife or sharp instrument possessionx. Knife or sharp instrument offencesxi. Robberyxii. Other theftxiii. Burglaryxiv. Fraudxv. Antisocial behaviourxvi. Causing public fear or distressxvii. Vehicle offencesxviii. Arsonxix. Vandalisms and criminal damagexx. Drug possession with intent to supplyxxi. Drug possession without intent to supply xxii. Most serious sexual offencesxxiii. Other sexual offencesxxiv. Other non-notifiable crimeOther informationxxv. Incident occurred as a whole group activity (1=yes, 2=no)xxvi. (1) Victim of crime (2) Perpetrator of crime (3) present at crimexxvii. (1) Suspected (2) ProsecutedCoding template B: Nature of contactCriminal (general)Assaults ✓✓Intimidates ✓✓Kills ✓✓Drives ✓✓Performs monetary task ✓✓Performs weapons storage ✓✓Provides phone use ✓✓Provides protection ✓✓Vandalises ✓✓Sexually offends ✓✓Carries weapon ✓✓Provides weapon ✓✓Steals ✓✓Recruits gang members ✓✓Provides gang members ✓✓Disturbs the peace ✓Displays delinquent / anti-social behaviour ✓Supports other criminal business dealings ✓✓Provides other criminal service ✓✓Provides vehicle for other criminal activity ✓✓Supplies illegal goods ✓✓✓Involved in supply of illegal goods ✓Involved in other criminal task ✓Involved in other criminal activity ✓DrugsAssaults (drug-related) ✓✓Intimidates (drug-related) ✓✓Buys drugs ✓✓Sells drugs ✓✓✓Carries drugs ✓✓Carries drugs money ✓✓Collects debts (drug-related) ✓✓Cuts/bags/prepares drugs ✓✓Provides drugs materials ✓✓✓Provides location for drugs storage ✓✓Running ✓✓Provides weapon (drugs-related) ✓✓Provides vehicle (drugs-related activity) ✓✓Deals drugs ✓✓✓Delivers drugs ✓✓✓Supplies drugs ✓✓✓Supports drugs business dealings ✓✓Performs other drugs task ✓✓Involved in other drugs activity ✓Resource B: Possible analytical approach1. Understanding a particular issue (e.g. drugs) Which individuals are linked together inthe network? How are they linked?Do results matchwhat I know? Whatseems unusual?Could unusualresults be explainedby an issue with thedata?Who knows thepicture on theground? What dothey think?Who is peripheral to the network and whois central?Who turns up in some networks and notothers? Why is this?2. Disrupting activity Can any hierarchy be seen in the gang (e.g. leaders)?Are there any clear opportunities to fragment the networks (e.g. focusing on ‘gatekeepers’)?Are some networks more / less densely packed (and therefore potentially more / less difficult to disrupt)?3. Identifying vulnerable individuals Who may be vulnerable to increased involvement in gang activity (e.g. who is linked to gang nominals / crime)?Who already looks involved? Could they potentially draw others in?4. Targeting interventions What role do individuals play in the networks?Who is connected to lots of others? Who is uniquely connected to lots of others?Who is a ‘gatekeeper’?If an intervention was delivered to individuals, what impact would it have on the network?ISBN: 978-1-78655-066-8ISSN: 1756-3666© Crown copyright 2016This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: ************************.Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned.。
TheSocialNetwork社交网络
TheSocialNetwork社交网络第一篇：The Social Network 社交网络The Social Network on My EyeA Harvard computer programming genius, Mark Zuckerberg, sat down in front of his computer, typed codes and created the social web site which would soon be in fame as ter Mark was sued by two brothers, the Winklevoss twins who declared he stole their idea about Facebook, and Eduardo Saverin who was the best friend of him.The Social Network tells us th e establishment of Facebook through main characters’ memories.This film was directed by David Fincher and had 8 nominations.And it won 3 awards of them for Best Adapted Screenplay, Best Original Score and Best Editing on 83rd Academy Awards.In film, Mark Zuckerberg is lucky.He has talent and ability to achieve his ambition, founding a company by himself and in harmony with Sean Parker, on the future of Facebook who also a genius, recommended he to meet investors.Sean foreseen Facebook’s potential and did a lot with Mark that good for pared with Eduardo, Sean was more like the CFO of Facebook.Every corn has two sides;while Mark succeeded he lost his friendship with Eduardo, gained “asshole” and was drawn into the following two law suits.Mark issue d new shares of stock, then Eduardo’s share down from 30% to 0.3%.It looked like Mark betrayed Eduardo;but in fact, Eduardo didn’t realize what Facebook means to him, the division of opinion between ardo did nothing help for Mark and almost killed Facebook except the money he invested.Mark also defended Eduardo about the chicken things.Although the unexpected things landed on Mark, he known what he was doing,for him Facebook went first.He didn’t want anyone ruined Facebook.The Social Network is based on a book called The Accidental Billionaires: The Foundation of Facebook A Tale of Sex, Money, Genius and Betray(2009)by Ben Mezrich.So in reality, Facebook is not entirely founded like the film is acting.The real set-up process may only they know.第二篇：社交网络<<社交网络>>观后感今天看了<<社交网络>>这部电影,使我了解了社交网络的一些东西，也知道了facebook的创始人成长的经历。
社交网络_基础部分
节点度（Degree）
节点的度是指和该节点相关联的边的条数，
又称关联度。反映社交网络中用户的活跃
程度。
对于有向图，节点的入度 是指进入该节点
的边的条数；节点的出度是指从该节点出
发的边的条数。
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量化网络特征的指标
路径（Path）
从节点i出发，经过一条或更多条边，到达 节点j，称这些边按顺序相连形成了一条i 与j之间的路径。包含边数最少或权值加和 最小的路径，称为最短路径（Shortest path）。
7
网络表示
V0 V1 V2 V3 V0 V1 V2 V3
图（Graph）
由节点和边组成。根据边的定义分为有向 图和无向图
邻接矩阵（Adjacency matrix）
把所有边按照下角标的顺序排列
8
量化网络特征的指标
1
3
1
1
密度（Density）
网络中实际存在的边的数目与可能存在的 边的数目的比值，刻画了网络的紧密程度
（3）结束：当S1包含所有节点时，停止；否则重复(2)
12
Prim算法示例
13
量化网络特征的指标
最短路径树 （Shortest PathTree）
概述：从图中选择边子集，使得从根节点到其他节点的权值加和最小 实例：从快递派送中心发货到N个驿站，路线的总和最小问题 迪杰斯特拉（Dijkstra）算法： （1）初始化：选择一个根节点i构成集合S1，其余节点构成S2 （2）从 S2中选择距离根节点i最近的节点k加入到S1
4
问题：只要一个事情有多人参与，自然就会 产生社交网络？
维基百科的互动随着词条编辑的完 成就停止了，临时性的互动不能构 成社交网络
人际关系社交网络分析
人际关系社交网络分析人际关系是我们生活中不可或缺的一部分，而社交网络则是构成人际关系的重要桥梁。
从古至今，人们都通过社交网络来建立和维持人际关系。
本文将对人际关系和社交网络进行分析。
一、人际关系的意义人际关系是人类社会存在的必然结果，它不仅在个人的生活中起着重要的作用，而且对整个社会的发展也具有深远的影响。
人际关系可以带来情感上的支持、资源的共享以及信息的传递，使个体能够融入社会并得到支持。
二、社交网络的构成社交网络由一系列的人际关系构成，这些关系可以分为亲属关系、友情关系和职业关系等。
亲属关系是最早形成的关系，它是通过血缘和婚姻等方式建立起来的。
友情关系则是在学校、社区等环境中形成的，它常常由共同的兴趣和爱好所连接。
而职业关系则是在工作场所中建立的关系，它主要通过工作任务和组织结构来连接。
三、社交网络的重要性社交网络对于人们的生活和发展起着重要的作用。
通过社交网络，人们可以借助他人的力量解决问题、获得资源和支持。
同时，社交网络也可以为个人提供机会，促进个人的自我实现和成就。
四、社交网络的弊端尽管社交网络有许多好处，但也存在一些弊端。
一方面，社交网络可能会导致信息传播的不准确和失真，因为信息的传递过程中可能被篡改或夸大。
另一方面，社交网络的使用也存在成瘾的风险，如果过度沉迷于社交网络的使用，会对个人的生活和工作产生负面影响。
五、构建健康的社交网络为了构建健康的社交网络，我们可以采取以下措施。
首先，要注重面对面的沟通，尤其是与亲友之间的交流，建立真实的人际关系。
其次，要慎重选择网络平台和社群，确保信息的可信度和质量。
最后，要保持适度和平衡地使用社交网络，避免过度依赖和沉迷。
综上所述，人际关系和社交网络是密不可分的。
社交网络既是人际关系的表现形式，也是人际关系的重要支持系统。
通过深入分析和理解人际关系和社交网络的意义，我们可以更好地构建和维护健康的人际关系，并更好地利用社交网络的力量。
社交网络中的网络结构分析方法探讨
社交网络中的网络结构分析方法探讨社交网络是当今互联网时代的重要组成部分，人们通过社交媒体平台进行信息传播、社交互动和人际关系建立。
随着社交网络的快速发展，人们对于网络结构分析方法的研究也越来越深入。
本文将探讨社交网络中的网络结构分析方法。
首先，社交网络的网络结构可以分为两个层面进行分析：节点级别和整体网络级别。
在节点级别上，可以研究节点的位置、度中心性、接近中心性等指标来了解节点的重要程度和影响力。
例如，度中心性指标可以衡量一个节点与其他节点之间的连接程度，节点的度中心性越高，说明该节点在网络中的影响力越大。
此外，社交网络中还存在着一些特殊类型的节点，如核心节点、关键节点和桥接节点。
核心节点指的是在网络中连接其他节点的节点，其影响力较大；关键节点则是关系网络的决定节点，当其被移除后，整个网络的稳定性会受到较大的影响；桥接节点则是连接不同社区的节点，对于数据传递和信息流动起到重要作用。
研究这些特殊节点所扮演的角色，有助于深入理解社交网络中的网络结构和信息传播机制。
在整体网络级别上，可以采用复杂网络分析方法来研究社交网络的拓扑结构和演化过程。
例如，可以构建网络的邻接矩阵和关联矩阵，通过节点之间的连接关系和信息传递路径来研究网络结构。
社交网络往往呈现出小世界网络和无标度网络的特征，即两节点之间的平均路径长度较短，同时少数节点具有较高的度数。
这些特征使得信息在社交网络中的传播更加高效和迅速。
此外，还可以采用社群发现方法来研究社交网络中的网络结构。
社群是指在一个网络中具有一定紧密联系的节点群体，研究社群的形成与演化过程有助于理解社交网络的组织结构和功能。
常见的社群发现方法包括谱聚类、模块度最大化等。
这些方法通过对节点之间的相似性进行度量和分析，将网络划分为具有内部紧密联系、而与其他社群联系较为松散的子网络，有助于揭示社交网络中的群体结构和信息流动机制。
除了以上方法，还可以结合时空信息对社交网络进行更深入的分析。