Google+ Statistics Widget Code. Google+ Widget HTML Code
As part of the Google+ project tracking tools, the statistics widget is the way in which we will all show our popularity to website visitors. The all new Google+ Statistics Widget is a section of code which displays the basics about your Google+ profile. Before you zoom off to get your own Google+ website widget code, please +1 this page! Instruction to get your Plus 1 Widget: First visit the social statistics website. Then in the top right hand corner of the web-page, you will see ‘Track your Google+ statistics’. At the top of the page is a link to get your widget code, you’ll it reads: ‘Check more detailed historical statistics”. Once you click through the link you’ll get to the page Personal Statistics for: [Your Profile Name]. The widget displays your profile name, number of Google+ followers, number of profiles you are following and a link to your profile.
Social Network Analysis (SNA) Software with Sentinel Visualizer
Degree Centrality Degree centrality is simply the number of direct relationships that an entity has. An entity with high degree centrality: Is generally an active player in the network. In our example network diagram above, Alice has the highest degree centrality, which means that she is quite active in the network. Betweenness Centrality Betweenness centrality identifies an entity's position within a network in terms of its ability to make connections to other pairs or groups in a network. Holds a favored or powerful position in the network. In the example above, Rafael has the highest betweenness because he is between Alice and Aldo, who are between other entities. Closeness Closeness centrality measures how quickly an entity can access more entities in a network. Has quick access to other entities in a network. As with the betweenness example, Rafael has the highest closeness centrality because he can reach more entities through shorter paths. Eigenvalue Hub and Authority Going Further
社會網路分析
出自 MBA智库百科( 社會網路分析(Social Network Analysis) 社會網路分析方法是由社會學家根據數學方法﹑圖論等發展起來的定量分析方法，近年來，該方法在職業流動、城市化對個體幸福的影響、世界政治和經濟體系、國際貿易等領域廣泛應用，併發揮了重要作用。社會網路分析是社會學領域比較成熟的分析方法，社會學家們利用它可以比較得心應手地來解釋一些社會學問題。 網路指的是各種關聯，而社會網路(Social Network)即可簡單地稱為社會關係所構成的結構。 從社會網路的角度出發，人在社會環境中的相互作用可以表達為基於關係的一種模式或規則，而基於這種關係的有規律模式反映了社會結構，這種結構的量化分析是社會網路分析的出發點。 網路指的是各種關聯，而社會網路(social network)即可簡單地稱為社會關係所構成的結構。 行動者(actor)：這裡的行動者不但指具體的個人，還可指一個群體、公司或其他集體性的社會單位。 關係紐帶(relational tie)：行動者之間相互的關聯即稱關係紐帶。 二人組(dyad)：由兩個行動者所構成的關係。 三人組(triad)：由三個行動者所構成的關係。 子群(subgroup)：指行動者之間的任何形式關係的子集。 群體(group)：其關係得到測量的所有行動者的集合。 社會網路分析是對社會網路的關係結構及其屬性加以分析的一套規範和方法。 從這個意義上說，社會網路分析不僅是對關係或結構加以分析的一套技術，還是一種理論方法——結構分析思想。 韋爾曼指出，作為一種研究社會結構的基本方法，社會網路分析具有如下基本原理： 1．關係紐帶經常是不對稱地相互作用著的，在內容和強度上都有所不同。 2．關係紐帶間接或直接地把網路成員連接在一起；故必須在更大的網路結構背景中對其加以分析。 3．社會紐帶結構產生了非隨機的網路，因而形成了網路群(network clusters)、網路界限和交叉關聯。 4．交叉關聯把網路群以及個體聯繫在一起。 5．不對稱的紐帶和複雜網路使稀缺資源的分配不平等。 6．網路產生了以獲取稀缺資源為目的的合作和競爭行為。 這種結構分析的方法論意義是：社會科學研究的對象應是社會結構，而不是個體。 傳統上對社會現象的研究存在著個體主義方法論與整體主義方法論的對立。 通常，社會學家們是在如下幾個層次上使用社會結構概念的：
Entrevista henry jenkins
+1 reporting in Google Webmaster Tools and Google Analytics
Webmaster level: All It’s been a busy week for us here at the Googleplex. First we released +1 buttons to Google search sites globally, then we announced the beginning of the Google+ project. The +1 button and the Google+ project are both about making it easier to connect with the people you trust online. For the +1 button, that means bringing advice from trusted friends and contacts right into Google search, letting the users who love your web content recommend it at the moment of decision. But when you’re managing a website, it's usually not real until you can measure it. First, +1 metrics in Google Webmaster Tools can show you how the +1 button affects the traffic coming to your pages: The Search Impact report gives you an idea of how +1‘s affect your organic search traffic. Finally, you can also see how users share your content using other buttons besides +1 by using Social Plugin Analytics in Google Analytics. Social reporting is just getting started.
Data Visualization, Link Analysis, Social Network Analysis (SNA) Software: Sentinel Visualizer Product Information
Empowering the demanding needs of intelligence analysts, law enforcement, investigators, researchers, and information workers, Sentinel Visualizer is the next generation data visualization and analysis solution for your big data. With cutting edge features and best-of-breed usability, Sentinel Visualizer provides you with insight into patterns and trends hidden in your data. Its database driven data visualization platform lets you quickly see multi-level links among entities and model different relationship types. Advanced drawing and redrawing features generate optimized views to highlight the most important entities. Social Network Analysis (SNA) metrics reveal the most interesting suspects in complex webs. With advanced filtering, squelching, weighted relationship types, shortest path analysis, timelines, and integrated geospatial features, Sentinel Visualizer helps you maximize the value of your data. Product Highlights Link Analysis Turn rows and columns into visual data.
UCINET Software
Customer Support For customer support (e.g., ordering info, billing etc) contact roberta@analytictech.com. For tech support join the users group or contact support@analytictech.com. We prefer you try the users group first since the answer to your question may benefit others. In addition, you should check out the support page, which includes useful things like tutorial exercises. Fixes, improvements, upgrades UCINET is constantly being upgraded to fix problems, provide new capabilities, or generally make things better. Download and/or Purchase The program can be downloaded and used for free for 90 days. Requirements and Specifications Windows operating system Vista or later.
Dimensions of a Learning Network
Track Google Plus Referrals in Google Analytics! » SEO News PageTraffic Buzz
With the ever increasing Google +'s membership figures wouldn't it be great to be able to track the referrals in Google+ using Google Analytics? This could enable you to get a clear picture of the source of the traffic to you. As successfully demonstrated by Andrew Shotland, he “adapted (aka “ripped off”) this great post from Sebastien Page” who in turn gives “full credit” to Ruben Yau. Following are the steps adapted: “Go to your Google Analytics account and click on Filter Manager > Add Filter, and enter the following information: 1) Filter Name: Full Referrer (or whatever you want to call it) Filter Type: Custom Filter Advanced Field A -> Extract A: Referral (.*) Field B -> Extract B: leave blank Output To -> Constructor: User Defined $A1 Field A Required: Yes Field B Required: No Override Output Field: Yes Case Sensitive: No It should look like this:” 2) Now, when you go on the dashboard, in the left-hand navigation, click on the 'Visitors'. It is further suggested: Get FREE Updates
