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Basics of Social Network Analysis
 
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Basics of Social Network Analysis In this video Dr Nigel Williams explores the basics of Social Network Analysis (SNA): Why and how SNA can be used in Events Management Research. The freeware sound tune 'MFF - Intro - 160bpm' by Kenny Phoenix http://www.last.fm/music/Kenny+Phoenix was downloaded from Flash Kit http://www.flashkit.com/loops/Techno-Dance/Techno/MFF_-_In-Kenny_Ph-10412/index.php The video's content includes: Why Social Network Analysis (SNA)? Enables us to segment data based on user behavior. Understand natural groups that have formed: a. topics b. personal characteristics Understand who are the important people in these groups. Analysing Social Networks: Data Collection Methods: a. Surveys b. Interviews c. Observations Analysis: a. Computational analysis of matrices Relationships: A. is connected to B. SNA Introduction: [from] A. Directed Graph [to] B. e.g. Twitter replies and mentions A. Undirected Graph B. e.g. family relationships What is Social Network Analysis? Research technique that analyses the Social structure that emerges from the combination of relationships among members of a given population (Hampton & Wellman (1999); Paolillo (2001); Wellman (2001)). Social Network Analysis Basics: Node and Edge Node: “actor” or people on which relationships act Edge: relationship connecting nodes; can be directional Social Network Analysis Basics: Cohesive Sub-group Cohesive Sub-group: a. well-connected group, clique, or cluster, e.g. A, B, D, and E Social Network Analysis Basics: Key Metrics Centrality (group or individual measure): a. Number of direct connections that individuals have with others in the group (usually look at incoming connections only). b. Measure at the individual node or group level. Cohesion (group measure): a. Ease with which a network can connect. b. Aggregate measure of shortest path between each node pair at network level reflects average distance. Density (group measure): a. Robustness of the network. b. Number of connections that exist in the group out of 100% possible. Betweenness (individual measure): a. Shortest paths between each node pair that a node is on. b. Measure at the individual node level. Social Network Analysis Basics: Node Roles: Node Roles: Peripheral – below average centrality, e.g. C. Central connector – above average centrality, e.g. D. Broker – above average betweenness, e.g. E. References and Reading Hampton, K. N., and Wellman, B. (1999). Netville Online and Offline Observing and Surveying a Wired Suburb. American Behavioral Scientist, 43(3), pp. 475-492. Smith, M. A. (2014, May). Identifying and shifting social media network patterns with NodeXL. In Collaboration Technologies and Systems (CTS), 2014 International Conference on IEEE, pp. 3-8. Smith, M., Rainie, L., Shneiderman, B., and Himelboim, I. (2014). Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Internet Project.
Views: 33381 Alexandra Ott
Social Networks for Fraud Analytics
 
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Data mining algorithms are focused on finding frequently occurring patterns in historical data. These techniques are useful in many domains, but for fraud detection it is exactly the opposite. Rather than being a pattern repeatedly popping up in a data set, fraud is an uncommon, well-considered, imperceptibly concealed, time-evolving and often carefully organized crime which appears in many types and forms. As traditional techniques often fail to identify fraudulent behavior, social network analysis offers new insights in the propagation of fraud through a network. Indeed, fraud is not something an individual would commit by himself, but is often organized by groups of people loosely connected to each other. The use of networked data in fraud detection becomes increasingly important to uncover fraudulent patterns and to detect in real-time when certain processes show some characteristics of irregular activities. Although analyses focus in the first place on fraud detection, the emphasis should shift towards fraud prevention, i.e. detecting fraud before it is even committed. As fraud is a time-evolving phenomenon, social network algorithms succeed to keep ahead of new types of fraud and to adapt to changing environment and surrounding effects.
Views: 7987 Bart Baesens
Facebook Friend Recommendation using Graph Mining | AI Case Study
 
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Follow me on Facebook facebook.com/himanshu.kaushik.2590 Subscribe to our channel on youtube to get latest updates on Video lectures Our video lectures are helpful for examinations like GATE UGC NET ISRO DRDO BARCH OCES DCES DSSSB NIELIT Placement preparations in Computer Science and IES ESE for mechanical and Electronics. Get access to the most comprehensive video lectures call us on 9821876104/02 Or email us at [email protected] Visit Our websites www.gatelectures.com and www.ugcnetlectures.com For classroom coaching of UGC NET Computer Science or GATE Computer Science please call us on 9821876104 Real World Application of Artificial Intelligence in facebook using Graph mining. Get access to all the video lectures visit our website www.appliedaicourse.com Links of Our Demo lectures playlists Our Courses - https://goo.gl/pCZztL Data Structures - https://goo.gl/HrZE6J Algorithm Design and Analysis - https://goo.gl/hT2JDg Discrete Mathematics - https://goo.gl/QQ8A8D Engineering Mathematics - https://goo.gl/QGzMFv Operating System - https://goo.gl/pzMEb6 Theory of Computation - https://goo.gl/CPBzJZ Compiler Design - https://goo.gl/GhcLJg Quantitative Aptitude - https://goo.gl/dfZ9oD C Programming - https://goo.gl/QRNx54 Computer Networks - https://goo.gl/jYtsCQ Digital Logic - https://goo.gl/3iosMc Database Management System - https://goo.gl/84pCFD Computer Architecture and Organization - https://goo.gl/n9H69F Microprocessor 8085 - https://goo.gl/hz5bvv Artificial Intelligence - https://goo.gl/Y91rk2 Java to Crack OCJP and SCJP Examination - https://goo.gl/QHLKi7 C plus plus Tutorials - https://goo.gl/ex1dLC Linear Programming Problems - https://goo.gl/RnRHXH Computer Graphics - https://goo.gl/KaGsXs UNIX - https://goo.gl/9Le7sX UGC NET November examination video solutions - https://goo.gl/Wos193 NIELIT 2017 Question paper Solutions - https://goo.gl/w9QkaE NIELIT Exam Preparation Videos - https://goo.gl/cXMSyA DSSSB Video Lectures - https://goo.gl/f421JF ISRO 2017 Scientist SC paper Solution - https://goo.gl/bZNssE Computer Graphics - https://goo.gl/uWwtgw Number System Digital logic - https://goo.gl/7Q1vG1 Live Classroom Recordings - https://goo.gl/pB1Hvi Verbal Aptitude - https://goo.gl/oJKwfP Thermodynamics - https://goo.gl/BN5Gd6 Heat and Mass Transfer - https://goo.gl/Lg6DzN Pre and Post GATE Guidance - https://goo.gl/k5Ybnz GATE Preparation Tips by Kishlaya Das GATE AIR 37 - https://goo.gl/jfFWQp #GATE #UGCNET
Views: 1189 DigiiMento Education
Graph Mining for Log Data Presented by David Andrzejewski
 
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This talk discusses a few ways in which machine learning techniques can be combined with human guidance in order to understand what the logs are telling us. Sumo Training: https://www.sumologic.com/learn/training/
Views: 1777 Sumo Logic, Inc.
Jure Leskovec: "Large-scale Graph Representation Learning"
 
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New Deep Learning Techniques 2018 "Large-scale Graph Representation Learning" Jure Leskovec, Stanford University Abstract: Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks. Institute for Pure and Applied Mathematics, UCLA February 7, 2018 For more information: http://www.ipam.ucla.edu/programs/workshops/new-deep-learning-techniques/?tab=overview
Mini Lecture: Social Network Analysis for Fraud Detection
 
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In this mini lecture, Véronique Van Vlasselaer talks about how social networks can be leveraged to uncover fraud. Véronique is working in the DataMiningApps group led by Prof. dr. Bart Baesens at the KU Leuven (University of Leuven), Belgium.
Views: 14254 Bart Baesens
DATA MINING   1 Data Visualization   3 1 1  Graphs and Networks
 
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https://www.coursera.org/learn/datavisualization
Views: 1186 Ryo Eng
Graph Mining and Analysis  Lecture_12
 
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Graph Mining and Analysis Lecture_12 22 December 2015
Mining the Social Graph: How Digital Publishers are Using Facebook Data to Delight & Enthrall Users
 
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Originally presented at AllFacebook Marketing Conference (a Mediabistro event) in San Francisco, CA on June 5, 2013. Jay Budzik, Chief Technology Officer at Perfect Market, and Jason Jedlinski, VP of Digital Products & Platforms at Tribune Broadcasting, shared how Perfect Social, Perfect Market's social sharing tool, helped KTLA.com nearly double its Facebook referral traffic. Perfect Social is available to premium digital publishers (10M+ PVs per month) for a 60-day free trial: http://goo.gl/dSaJ8
Views: 720 ThePerfectMarket
Pseudo Feature Extraction in Social Network Analysis and Text Mining
 
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This is a webinar I delivered as a part of a webinar series entitled "We are all Social Things" organized by the IS department at King Saud University - Female Section The recording started a bit late, but you will be able to follow.
Views: 743 Ibrahim Almosallam
Data Mining Techniques to Prevent Credit Card Fraud
 
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Includes a brief introduction to credit card fraud, types of credit card fraud, how fraud is detected, applicable data mining techniques, as well as drawbacks.
Views: 10186 Ben Rodick
What is ENTERPRISE SOCIAL GRAPH? What does ENTERPRISE SOCIAL GRAPH mean?
 
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What is ENTERPRISE SOCIAL GRAPH? What does ENTERPRISE SOCIAL GRAPH mean? ENTERPRISE SOCIAL GRAPH meaning - ENTERPRISE SOCIAL GRAPH definition - ENTERPRISE SOCIAL GRAPH explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ An enterprise social graph is a representation of the extended social network of a business, encompassing relationships among its employees, vendors, partners, customers, and the public. With the advent of Web 2.0 and Enterprise 2.0 technologies a company can monitor and act on these relationships in real-time. Given the number of relationships and the volume of associated data, algorithmic approaches are used to focus attention on changes that are deemed relevant. The term was first popularized in a 2010 Forbes article, to describe the multi-relational nature of enterprise-centric networks that are now at least partially observable at scale. The enterprise social graph integrates representations of the various social networks in which the enterprise is embedded into a unified graph representation. Given the online context of many of the relationships, social interactions often comprise direct communication along with interactions around digital artifacts. Therefore, the enterprise social graph codifies not only relationships among individuals but also individual-object interaction patterns. This definition follows Facebook's and Google's concept of a social graph that explicitly includes the objects with which individuals interact in a network. Examples of these relationship patterns can include authorship, sharing or sending information, management or other social hierarchy, bookmarking, and other gestural signals that describe a relationship between two or more nodes. Additional representational challenges arise with the need to capture interaction dynamics and their changing social context over time, and as such, representational choices vary based ultimately on the analytic questions that are of interest. Besides being a specialized type of social graph, the enterprise social graph is related to network science and graph theory. Changes in how people connect, share, accomplish tasks through online social networks, combined with the growth of ambient public information relevant to an enterprise, contribute to the dynamism and increasing complexity of enterprise social graphs. Whereas meetings, phone calls, or email have been the traditional media for these exchanges, increasingly collaboration and conversation occurs via online social media. As Kogut and Zander point out, the more tacit knowledge is, the more difficult and expensive it is to transmit, since the costs of codifying and teaching will rise as tacitness increases. The consumerization of social business software enables simpler and more cost-effective ways making relationships and tacit knowledge both observable and actionable. From an internal enterprise perspective, understanding the enterprise social graph can provide greater awareness of internal dynamics, organizational and information flow inefficiencies, information seeking and expert identification, or exposing opportunities for new valued connections. From an external perspective, it can provide deeper insights into marketplace conditions and customer demand, customer issues and concerns, product development and co-creation, supply-side operational awareness or external causal relationships. Recent developments in big data analysis, combined with graph mining techniques, make it possible to analyze petabytes of structured and unstructured information and feed user-facing applications. In making use of the enterprise social graph, such applications excel at search, routing, and matching operations, particularly where these include personalization, statistical analysis and machine learning. Examples of applications that combine big data mining techniques over the enterprise social graph include business intelligence, personalized activity streams and intelligent filtering, social search, recommendation engines, automated question or message routing, expertise identification, and information context discovery.
Views: 26 The Audiopedia
Graph Mining and Analysis  Lecture_4
 
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Graph Mining and Analysis Lecture_4 18 December 2015
Text Mining, Web Scraping and Sentiment Analysis with R - Social Media Analysis by R-Tutorials.com
 
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R-Tutorials offers a variety of R courses ranging from beginner to advanced levels. Full courses are hosted on Udemy. Find the links to our webpage (http://www.r-tutorials.com) as well as to our courses (free and paid) below: R Basics beginners FREE course https://www.udemy.com/r-basics/ R Level 1 intermediate course - 60% OFF https://www.udemy.com/r-level1/?couponCode=youtube19 Statistics in R advanced course - 50% OFF https://www.udemy.com/statisticsinr/?couponCode=youtube29 Machine Learning and Statistical Modeling with R Examples - 75% OFF https://www.udemy.com/machine-learning-and-statistical-modeling-with-r/?couponCode=youtube17 Graphs in R advanced course - 70% OFF https://www.udemy.com/graphs-in-r/?couponCode=youtube29 Social Media Analysis advanced course - 56% OFF https://www.udemy.com/r-social-media-mining-scraping-with-twitter/?couponCode=youtube29 Excel 2013 Charts beginners FREE course https://www.udemy.com/excel-charts/ Audacity for Instructors and Podcasters 50% OFF https://www.udemy.com/audacity-mastery-course-for-instructors-and-podcasters/?couponCode=youtube15
Views: 3078 R Tutorials
Social Media Analytics Introduction
 
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Please view the full copyright statement at: http://public.dhe.ibm.com/software/data/sw-library/services/legalnotice.pdf
A Quick Look at Social Network Analysis
 
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You use social networks every day, but how can we understand how they work to affect our decisions, our careers, our health, and our histories? The field of Social Network Analysis is the dynamic and highly adaptable group of techniques that let us quantify and understand the complex structures and flows of relationships, thoughts, and things between people around the world. Look at your own social networks at these links: Check your own personal Facebook social network with Touchgraph: http://www.touchgraph.com/facebook Check your own personal LinkedIn social network with Socilab: http://socilab.com/ Check your own personal Twitter social network with Mentionmapp: http://mentionmapp.com/ Social Network Analysis can enrich the research of faculty and the studies of students—look for workshops run by the Duke Network Analysis Center and classes featuring graph theory, network theory, and social networks. Networks are everywhere—what will you discover with them?
What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning
 
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What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning - SOCIAL MEDIA MINING definition - SOCIAL MEDIA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. The term "mining" is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quanitites of raw ore to find the precious minerals; likewise, social media "mining" requires human data analysts and automated software programs to sift through massive amounts of raw social media data (e.g., on social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, etc.) in order to discern patterns and trends. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs (or, for companies, new products, processes and services). Social media mining uses a range of basic concepts from computer science, data mining, machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis, network science, sociology, ethnography, optimization and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data. In the 2010s, major corporations, as well as governments and not-for-profit organizations engage in social media mining to find out more about key populations of interest, which, depending on the organization carrying out the "mining", may be customers, clients, or citizens. As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Photobucket, or Picasa), news aggregation (Google reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch.tv), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegree.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.
Views: 215 The Audiopedia
Social Network Analysis
 
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An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 3100 Microsoft Research
Andre Panisson: Exploring temporal graph data with Python
 
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PyData NYC 2015 We will see how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications in the academy and industry. We will see a use case where tensor decomposition was used to extract structural and temporal signatures from a time-varying social network collected from wearable proximity sensors. Tensor decompositions have gained a steadily increasing popularity in data mining applications. Data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as multidimensional structures such as tensors. For example, time-varying social networks collected from wearable proximity sensors can be represented as 3-way tensors. By representing this data as tensors, we can use tensor decomposition to extract community structures with their structural and temporal signatures. The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications. Slides available here: http://www.slideshare.net/panisson/exploring-temporal-graph-data-with-python-a-study-on-tensor-decomposition-of-wearable-sensor-data Github repo: https://github.com/panisson/ntf-school
Views: 1454 PyData
Graph Mining and Analysis Hands On Tutorial_1
 
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Graph Mining and Analysis Hands On Tutorial_1 18 December 2015
Data mining in social media
 
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I used screencast-o-matic to record my presentation.
Views: 320 Bryan Russowsky
PageRank Algorithm - Example
 
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Full Numerical Methods Course: https://bit.ly/2wYb2xf
Views: 43215 Balazs Holczer
Social Network Analysis
 
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Views: 1223 Wolfram
Social Media Analytics - Frameworks & Applications
 
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This video is an introduction to Social Media Marketing, application of social media in the overall marketing mix, and measuring the impact of social media marketing. A case study on social media listening is used to illustrate the various concepts. ----------------------------------------------------------------------------------------- Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Big Data, Analytics Cloud Computing, Artificial Intelligence & Machine Learning. PG Program in Business Analytics (PGP-BABI): 12-month program with classroom training on weekends + online learning covering analytics tools and techniques and their application in business. PG Program in Big Data and Machine Learning (PGP-BDML): 12-month program with classroom training on weekends + online learning covering big data analytics tools and techniques, machine learning with hands-on exposure to big data tools such as Hadoop, Python, Spark, Pig etc. PGP-Artificial Intelligence and Machine Learning: a 12-month weekend and classroom program designed to develop competence in AI and ML for future-oriented working professionals. PGP-Data Science & Engineering: 6-month weekend and classroom program allowing participants enables participants in learning conceptual building of techniques and foundations required for analytics roles. PG Program in Cloud Computing: 6-month online program in Cloud Computing & Architecture for technology professionals who want their careers to be cloud-ready. Business Analytics Certificate Program (BACP): 6-month online data analytics certification enabling participants to gain in-depth and hand on knowledge of analytical concepts.
Views: 226 Great Learning
R Lab.1 - Let's Draw a Social Network Graph: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ Let’s try turning some data into a graph for ourselves in R, an open-source statistical program This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Christos Faloutsos - "Mining Large Graphs: Patterns, Anomalies, and Fraud Detection"
 
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Prof. Christos Faloutsos (Carnegie Mellon University) gave a talk on "Mining Large Graphs: Patterns, Anomalies, and Fraud Detection" at the Centre for Innovation in Computing @ Lassonde, York University on February 5, 2016.
Views: 692 Tsotsos Lab
Data Visualization & Interactive Data Exploration with KNIME
 
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This video shows some options for data visualization and interactive data exploration, both within KNIME Analytics Platform and from a web browser through the KNIME WebPortal. Here we show: Sunburst chart, box plot, line plot, stacked plot, scatter plot, network graph and a few more visualization techniques. We also show how to control the plots and charts through a slider object and how to exploit the plot/chart interactivity. A reduced version of the workflow shown in this video, and with a smaller data set, can be downloaded from the KNIME EXAMPLES server under 02_Javascript/09_DataVisualization_AirlineDataset. Visit https://www.knime.com
Views: 12618 KNIMETV
Tom Sawyer CEO: Graph Visualization and Analysis
 
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Talk Abstract: Data relationships are everywhere, among employees in an organization, devices in a network, genes in a biochemical pathway, and producers and consumers in a supply chain. Graphs provide a fundamental means to help represent relationships inherent in data. Using Tom Sawyer Perspectives, Brendan will demonstrate various advanced graph visualization, layout, and analysis techniques developed over many years of practical experience. He will also discuss some of the software engineering challenges that we face in the era of Big Data. Among the important features Brendan plans to demonstrate are viewing, editing, and interaction methods. Further, he'll show various scalable, incremental and constraint-based layout algorithms and their usage in various industries, and demonstrate problems and solutions in graph analysis. Brendan will also discuss data representation, federated data integration, model management, rules, filters, and synchronized views of data in both desktop and web architectures. Tom Sawyer Software is the leading provider of enterprise software and services that enable organizations to build highly scalable and flexible data visualization and social network analysis applications. These applications are used to discover hidden patterns, complex relationships, and key trends in large and diverse data sets.
Views: 529 SF Data Mining
Network Analysis. Lecture10. Community detection
 
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Community detection algorithms. Overlapping communities. Clique percolation method. Heuristic methods. Label propagation. Fast community unfolding. Random walk based methods. Walktrap. Nibble. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture10.pdf
Views: 9331 Leonid Zhukov
Ben Chamberlain - Real time association mining in large social networks
 
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PyData London 2016 Social media can be used to perceive the relationships between individuals, companies and brands. Understanding the relationships between key entities is of vital importance for decision support in a swathe of industries. We present a real-time method to query and visualise regions of networks that could represent an industries, sports or political parties etc. There is a growing realisation that to combat the waning effectiveness of traditional marketing, social media platform owners need to find new ways to monetise their data. Social media data contains rich information describing how real world entities relate to each other. Understanding the allegiances, communities and structure of key entities is of vital importance for decision support in a swathe of industries that have hitherto relied on expensive, small scale survey data. We present a real-time method to query and visualise regions of networks that are closely related to a set of input vertices. The input vertices can define an industry, political party, sport etc. The key idea is that in large digital social networks measuring similarity via direct connections between nodes is not robust, but that robust similarities between nodes can be attained through the similarity of their neighbourhood graphs. We are able to achieve real-time performance by compressing the neighbourhood graphs using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines to milliseconds on standard laptops. Our method allows analysts to interactively explore strongly associated regions of large networks in real time. Our work has been deployed in Python based software and uses the scipy stack (specifically numpy, pandas, scikit-learn and matplotlib) as well as the python igraph implementation. Slides available here: https://docs.google.com/presentation/d/1-NkcPM3XYn-7jk6233MvvFJiC5Abi3e2nGkF_NSFuFA/edit?usp=sharing Additional information: http://krondo.com/in-which-we-begin-at-the-beginning/
Views: 729 PyData
Network Analysis. Lecture 17 (part 1). Label propagation on graphs.
 
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Node labeling. Label propagation. Iterative classification. Semi-supervised learning. Regularization on graphs Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture17.pdf
Views: 4173 Leonid Zhukov
[Webinar] How to Improve Fraud Detection using Social Network Analytics | #SuccessSeries
 
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In this webinar, you will learn how to: - use social networks for fraud detection - build a social network fraud classifier - evaluate a social network fraud classifier
Views: 700 MorganMcKinley
Why Twitter is blocking the government from using a data-mining tool (CNET Update)
 
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Watch more CNET Upate: http://bit.ly/1M6Q5xn The social network is banning US spy agencies from accessing an analytics service used by news agencies. Meanwhile, Facebook wins a trademark battle in China. Sorry, you won't be able to taste "Face Book" the drink. Subscribe to CNET: http://bit.ly/17qqqCs Watch more CNET videos: http://www.cnet.com/video Follow CNET on Twitter: http://twitter.com/CNET Follow CNET on Facebook: http://www.facebook.com/cnet
Views: 10527 CNET
Benjamin Bengfort |Dynamics in Graph Analysis: Adding Time as a Structure for Visual and Statisti
 
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PyData Carolinas 2016 Network analyses are powerful methods for both visual analytics and machine learning but can suffer as their complexity increases. By embedding time as a structural element rather than a property, we will explore how time series and interactive analysis can be improved on Graph structures. Primarily we will look at decomposition in NLP-extracted concept graphs using NetworkX and Graph Tool. Modeling data as networks of relationships between entities can be a powerful method for both visual analytics and machine learning; people are very good at distinguishing patterns from interconnected structures, and machine learning methods get a performance improvement when applied to graph data structures. However, as these structures become more complex or embed more information over time, both visual and algorithmic methods get messy; visual analyses suffer from the "hairball" effect, and graph algorithms require either more traversal or increased computation at each vertex. A growing area to reduce this complexity and optimize analytics is the use of interactive and subgraph techniques that model how graph structures change over time.
Views: 390 PyData
Data Mining and Network Analysis - Eelke Heemskerk
 
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Project website: http://corpnet.uva.nl In todays world, the control over big business is strongly intertwined. Corporations share directors, share owners, and often even own each other. But we know astonishingly little about the properties and generating mechanisms of this global networks of corporate control. In the CORPNET research group, a team of social and computer scientists employ cutting edge data analytics and large scale network analysis techniques to study the network of corporate control among over 100 million firms worldwide. How can we use these methods developed in physics and computer science to study social relations and understand who holds the power in contemporary capitalism?
FOSDEM 2013 - Mining Social Data
 
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Slides: http://www.slideshare.net/malk_zameth/mining-social-data-16288490 Hands-on section showing mining techniques for the social web (as a graph): Use them to visualize human interactions at a higher level, be it on public social networks (like facebook) or Enterprise private social networks (like yammer). while the examples will be social, the techniques exposed are usable in any graph datastore, the exact techniques I shall focus on will be: * Extraction * Finding frequent patterns * (Un)Supervised pattern learning * Constructing decision trees * Entity resolution Everything will be illustrated in code, All code will be open-source and pushed to github. Romeu MOURA (R&D Architect at Linagora) Architect in Linagora's cloud R&D projects, Romeu spends unrelenting verbiage trying to convince people to thrust public clouds, that the mining of big data can be ethical and improve everyone's life and that our current communication tools are artifacts of a bygone era.
Views: 187 Leonhard Euler
Web Graph and Web graph mining and hot topics in web search in Hindi
 
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Web Graph and Web graph mining and hot topics in web search in Hindi
R - Sentiment Analysis and Wordcloud with R from Twitter Data | Example using Apple Tweets
 
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Provides sentiment analysis and steps for making word clouds with r using tweets about apple obtained from Twitter. Link to R and csv files: https://goo.gl/B5g7G3 https://goo.gl/W9jKcc https://goo.gl/khBpF2 Topics include: - reading data obtained from Twitter in a csv format - cleaning tweets for further analysis - creating term document matrix - making wordcloud, lettercloud, and barplots - sentiment analysis of apple tweets before and after quarterly earnings report R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 11577 Bharatendra Rai
What is Graph Analytics?
 
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Cray graph analytics expert Dr. James Maltby discusses graph analytics, how it’s used, and why companies should consider migrating towards a graph analytics platform.
Views: 2378 Cray Inc.
Data Mining Open Flights Social Networking Presentation INFS770
 
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This is a presentation created for my Final Assignment in my social networking class. It contains a social networking presentation that I analyzed in gephi.
Views: 212 Tom Austin
BigDataX: Graph model of social networks
 
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Big Data Fundamentals is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn how big data is driving organisational change and essential analytical tools and techniques including data mining and PageRank algorithms. Enrol now! http://bit.ly/2rg1TuF
g-Miner: Interactive Visual Group Mining on Multivariate Graphs
 
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g-Miner: Interactive Visual Group Mining on Multivariate Graphs Nan Cao, Yu-Ru Lin, Liangyue Li, Hanghang Tong Abstract: With the rapid growth of rich network data available through various sources such as social media and digital archives,there is a growing interest in more powerful network visual analysis tools and methods. The rich information about the network nodes and links can be represented as multivariate graphs, in which the nodes are accompanied with attributes to represent the properties of individual nodes. An important task often encountered in multivariate network analysis is to uncover link structure with groups, e.g., to understand why a person fits a specific job or certain role in a social group well.The task usually involves complex considerations including specific requirement of node attributes and link structure, and hence a fully automatic solution is typically not satisfactory.In this work, we identify the design challenges for min-ing groups with complex criteria and present an interactive system, “g-Miner,” that enables visual mining of groups on multivariate graph data. We demonstrate the effectiveness of our system through case study and in-depth expert inter-views. This work contributes to understanding the design of systems for leveraging users’ knowledge progressively with algorithmic capacity for tackling massive heterogeneous information. ACM DL: http://dl.acm.org/citation.cfm?id=2702446 DOI: http://dx.doi.org/10.1145/2702123.2702446
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 40819 Well Academy
social media analytics software - Semalt
 
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Visit us - https://semalt.com/?ref=y #software, #media, #analytics, #social, #media_album, #prachifam_software, #sonar_software, #estiakkhanjhuman_software, #kisharilindja_media, #electronic_social, #wewantanewrail_social, #martianmanhunter_social, #beschreibungleser_social, #originsgodmodeglitch_social social media analytics software social media analytics software free nielsen social media analytics software avaya social media analytics software social media analytics software georefrence social media visual analytics software social media crm analytics software hubspot social media analytics software social media analytics architectural software ibm social media analytics software best social media analytics software adobe social media analytics software social media analytics software 2015 buffer social media analytics software wiki social media analytics software social media analytics freeware software crm social media analytics software sysomos social media analytics software nuvi social media analytics software slideshare social media analytics software social media analytics free software free social media analytics software salesforce social media analytics software google social media analytics software kpi social media analytics software comparison social media analytics software radian6 social media analytics software personal social media analytics software introduction social media analytics software music social media analytics software social media analytics dashboard software social media website analytics software analytics social media tracking software arabic social media analytics software analytics social media social media analytics social analytics software media analytics software analytics social media dashboard analytics ibm social media analytics architectural software ibm social media analytics saas software human centered social media analytics software big data social media analytics software open source social media analytics software social media analytics software open source features of social media analytics software google analytics social media traffic software types of social media analytics software guide to social media analytics software graph-based social media analytics software business using social media analytics software ibm social media analytics installation software technology behind social media analytics software purpose of social media analytics software sas institute social media analytics software google analytics social media tracking software book on social media analytics software data mining social media analytics software white label social media analytics software history of social media analytics software problems with social media analytics software sas social media analytics architectural software real time social media analytics software why use social media analytics software free social media analytics dashboard software ibm social media analytics cognos software ibm social media analytics documentation software google analytics social media metrics software social media analytics social media marketing google social media analytics emc social media analytics kpi social media analytics analytics on social media analytics social media free nuvi social media analytics new social media analytics social media analytics bucharest social media analytics coursera social media analytics definitions social media analytics evolution social media analytics ppta social media analytics questions social media analytics techniques social media free analytics introduction social media analytics raven social media analytics analytics social media dashboards sma social media analytics social media analytics argentina social media analytics buchholz social media analytics challenges social media analytics colleges social media analytics jobs social media analytics solutions social media analytics tutorial social media platform analytics tracking social media analytics business analytics social media retail social media analytics social media analytics gartner social media analytics summit social media analytics wiki social media data analytics unica social media analytics healthcare social media analytics roi social media analytics social media analytics buche social media analytics nulled social media analytics singapore top social media analytics understanding social media analytics hp social media analytics informs social media analytics
Views: 0 Manju Nath
Mining Social Media Data for Understanding Students’ Learning Experiences
 
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Abstract—Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences—opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi-label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from about 35,000 tweets streamed at the geo-location of Purdue University. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences.
Getting YouTube Data with R | User Network and Sentiment Analysis from Comments
 
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Note: Package "SocialMediaLab" is now renamed as "vosonSML" R File: https://goo.gl/4gpVdp YouTube data File: https://goo.gl/2p8V9L Includes, - Obtaining Google developer API key - Collecting data using YouTube video IDs - Saving and reading YouTube data file - Creating user network - Histogram of node degree - YouTube user network diagram - Sentiment analysis of YouTube user comments - Obtaining sentiment scores - Sentiment visualization R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 4540 Bharatendra Rai