Social network analysis software Social network analysis software (SNA software) is software which facilitates quantitative or qualitative analysis of social networks, by describing features of a network either through numerical or visual representation. Overview[edit] Some SNA software can perform predictive analysis.[5] This includes using network phenomena such as a tie to predict individual level outcomes (often called peer influence or contagion modeling), using individual-level phenomena to predict network outcomes such as the formation of a tie/edge (often called homophily models[6]) or particular type of triad, or using network phenomena to predict other network phenomena, such as using a triad formation at time 0 to predict tie formation at time 1. Network analysis software generally consists of either packages based on graphical user interfaces (GUIs), or packages built for scripting/programming languages. GUI packages are easier to learn, while scripting tools are more powerful and extensible. See also[edit]
GUESS: The Graph Exploration System What is Maltego - Paterva Wiki From Paterva Wiki What is Maltego? With the continued growth of your organization, the people and hardware deployed to ensure that it remains in working order is essential, yet the threat picture of your “environment” is not always clear or complete. In fact, most often it’s not what we know that is harmful - it’s what we don’t know that causes the most damage. Maltego is a unique platform developed to deliver a clear threat picture to the environment that an organization owns and operates. The unique perspective that Maltego offers to both network and resource based entities is the aggregation of information posted all over the internet - whether it’s the current configuration of a router poised on the edge of your network or the current whereabouts of your Vice President on his international visits, Maltego can locate, aggregate and visualize this information. Maltego offers the user with unprecedented information. What does Maltego do? What can Maltego do for me?
CCNA CRAM Sheet Passing the Cisco Certified Network Associate (CCNA) certification exam will bring instant credibility to your resume and enable you earn more as a network administrator. This is partly due to how difficult the CCNA exam (640-802) is to pass and the incredible amount of information that is covered on the Cisco CCNA exam. Due to the amount of information that the curriculum covers, Cisco allows you to break the exam up into two individual exams designated Interconnecting Cisco Network Devices 1 and 2 (ICND1 and ICND2). I am sure you are well aware that most Cisco CCNA Study Guides are 800 pages or more! So we have taken the time to condense all that information down into a beautiful, full color, laminated, three page fold out double sided Cram Sheet. All major topics covered on the CCNA exam are broken down on this Cram Sheet into color coded areas. Benefits of this Cisco CCNA Cram Sheet are: CCNA Cram Sheet Front CCNA Cram Sheet Back
a free app that helps you remember 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. Is often a connector or hub 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. Eigenvalue Eigenvalue measures how close an entity is to other highly close entities within a network. Hub and Authority Going Further
GGobi data visualization system. Gapminder: Unveiling the beauty of statistics for a fact based world view. How To Configure SSH Keys Authentication With PuTTY And Linux Se This tutorial explains how you can replace password-based SSH authentication with key-based authentication which is more secure because only the people that own the key can log in. In this example, we're using PuTTY as our SSH client on a Windows system. 1. Get the zip file with all PuTTY binaries 2. Open PuTTYgen.exe, press Generate button, move mouse. 3. For this guide let's assume you regular login name is autotimesheet (replace it with one that you use regularly). As root, on the shell, type: adduser autotimesheet --disabled-password You will be asked to fill in some details such as the user's real name (empty string is fine). Now type: su autotimesheet cd /home/autotimesheet mkdir .ssh chmod 700 .ssh cd .ssh Then in that folder, create and edit a file called authorized_keys2. In there, cut/paste your public ssh key, on ONE LINE (That is very important!!!) Do not add the email@example.com at the end of the line. Type: 4. 5.
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. Product Highlights Link Analysis Turn rows and columns into visual data.