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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: 31659 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: 7298 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
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: 1699 Sumo Logic, Inc.
Social Network Analysis
 
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Views: 1193 Wolfram
Graph Mining and Analysis  Lecture_4
 
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Graph Mining and Analysis Lecture_4 18 December 2015
Data mining in social media
 
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I used screencast-o-matic to record my presentation.
Views: 300 Bryan Russowsky
Talk Data to Me: Let's Analyze Social Media Data with Tableau
 
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Social media data is hot stuff—but it sure can be tricky to understand. In this session, Michelle from Tableau's social media team will share how they analyze social media data from multiple sources. We'll compare methods for collecting data, and discuss tips for ensuring that it answers new questions as they arise. Whether you're new to social media analysis or have already started diving into your data, this session will provide key tips, tricks, and examples to help you achieve your goals.
Views: 9842 Tableau Software
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: 2507 Microsoft Research
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: 70 The Audiopedia
Visual Analysis of Social Networks
 
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An introduction to information visualization, specifically network visualization techniques. Table of Contents: 00:10 - Information Visualization 01:06 - Challenger 02:56 - 04:44 - 05:37 - 05:49 - 06:08 - Main Idea 06:34 - Information Visualization 07:45 - Key Attributes 09:07 - Tasks in Info Vis 09:45 - Tasks in Info Vis 10:07 - How Vis Amplifies Cognition 12:50 - Network Visualization 13:15 - What is interesting about this network? 21:49 - What makes a good visualization? 23:15 - Is this a good visualization? 23:56 - What about this one? 24:56 - And this one? 25:29 - And finally, this one? 26:06 - Node Size and Color 28:33 - Node Size and Color 29:40 - Edge Weight 30:04 - Visualization Issues 31:05 - Example: Senate Voting Records 31:48 - Filtering 32:38 - Example: Senate Voting Records 32:43 - Filtering 32:46 - Examples 32:51 - Visualization Tools 34:19 - In Class Exercise
Views: 6715 jengolbeck
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: 8812 Leonid Zhukov
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: 14087 Bart Baesens
DATA MINING   1 Data Visualization   3 1 1  Graphs and Networks
 
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https://www.coursera.org/learn/datavisualization
Views: 1044 Ryo Eng
Graph Mining and Analysis  Lecture_12
 
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Graph Mining and Analysis Lecture_12 22 December 2015
Web Graph
 
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Web Graph
Views: 2148 Social Networks
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?
Graph Mining and Analysis Hands On Tutorial_1
 
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Graph Mining and Analysis Hands On Tutorial_1 18 December 2015
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
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: 725 PyData
Facebook text analysis on R
 
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For more information, please visit http://web.ics.purdue.edu/~jinsuh/.
Views: 10448 Jinsuh Lee
Enterprise Connectors - Social Media Data Mining
 
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This is a replay of the webinar covering using the CData Enterprise Connectors for FireDAC to connect to Twitter and Facebook to mine social media data. The examples are in Delphi, but they could also easily be adaptable for C++Builder too.
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: 37964 Well Academy
Fraud Detection in Real Time with Graphs
 
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Gorka Sadowski, a CISSP from the akalak cybersecurity consulting firm and Philip Rathle, VP of Product for Neo4j, talk about handling real-time fraud detection with graphs. They discuss retail banking + first-party fraud, automobile insurance fraud and online payment ecommerce fraud.
Views: 15474 Neo4j
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.
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: 3828 Leonid Zhukov
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: 671 Tsotsos Lab
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: 8932 Ben Rodick
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
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
Google Analytics Data Mining with R (includes 3 Real Applications)
 
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R is already a Swiss army knife for data analysis largely due its 6000 libraries but until now it lacked an interface to the Google Analytics API. The release of RGoogleAnalytics library solves this problem. What this means is that digital analysts can now fully use the analytical capabilities of R to fully explore their Google Analytics Data. In this webinar, Andy Granowitz, ‎Developer Advocate (Google Analytics) & Kushan Shah, Contributor & maintainer of RGoogleAnalytics Library will show you how to use R for Google Analytics data mining & generate some great insights. Useful Resources:http://bit.ly/r-googleanalytics-resources
Views: 26909 Tatvic
PageRank Algorithm - Example
 
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Google's page rank algorithm! Find more: http://www.globalsoftwaresupport.com/ Numerical Methods Course: http://bit.ly/2rv5m91 FREE Beginner Java Course: http://bit.ly/2rMkyxN
Views: 37014 Balazs Holczer
Devashish Shankar - Deep Learning for Natural Language Processing
 
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Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired towards an Organisation, measuring impact of Promotions in Social Media, and (Aspect based) Sentiment Analysis of Reviews. These techniques can not only help gauge the customer’s feedback, but also can help in providing users a better experience. Traditional solutions focused on heavy domain-specific Feature Engineering, and thats exactly where Deep Learning sounds promising! We will depict our foray into Deep Learning with these classes of Applications in mind. Specifically, we will describe how we tamed Deep Convolutional Neural Network, most commonly applied to Computer Vision, to help classify (short) texts, attaining near-state-of-the-art results on several SemEval tasks consistently, and a few tasks of importance to Flipkart. In this talk, we plan to cover the following: Basics of Deep Learning as applied to NLP: Word Embeddings and its compositions a la Recursive Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. New Experimental results on an array of SemEval / Flipkart’s internal tasks: e.g. Tweet Classification and Sentiment Analysis. (As an example we achieved 95% accuracy in binary sentiment classification task on our datasets - up from 85% by statistical models) Share some of the learnings we have had while deploying these in Flipkart! Here is a mindmap explaining the flow of content and key takeawys for the audience: https://atlas.mindmup.com/2015/06/4cbcef50fa6901327cdf06dfaff79cf0/deep_learning_for_natural_language_proce/index.html We have decided to open source the code for this talk as a toolkit. https://github.com/flipkart-incubator/optimus Feel free to use it to train your own classifiers, and contribute!
Views: 11151 HasGeek TV
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: 381 PyData
Visualize your social network
 
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VisualSage for Facebook provides innovative and interactive graph representations of your social network.
Views: 2245 VisualSage
Social Media Social Data and Python: 1 - Getting Started
 
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Social Media, Social Data and Python. Getting started. ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 2775 Sukhvinder Singh
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: 718 ThePerfectMarket
social network analysis
 
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This work consists of developing a platform of sentiments analysis based on a framework that extracts emotions and sentiments expressed by social networks users toward a subject and detects communities and users who influence the obtained results. Nowadays, the tremendous growth of social networks has attracted the attention of many individuals and organizations, because of the social data made available and easily accessible online, hence our Framework is designed to use online available data of the most popular social networks: Facebook and Twitter, as data source via their official APIs. We started with a study on social networks by introducing the concept of semantic web and the analysis of sentiments on concepts level, and then we proceed to the approach of the construction of a graph relied on the social data, as well as to make a comparative study of the communities' detection algorithms and to choose the one that suits our problem. These studies enabled us to define the specifications of our project and the document of analysis and computer design. And by the end we moved to the realization phase of the project.
Views: 98 Hamza Hanafi
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: 10054 KNIMETV
Social Networks in Data Mining - Keynote Bart Baesens
 
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Brooke Fortson interviews Bart Baesens about his keynote address at Analytics 2011. Baesens discusses social networks are being incorporated into analytical models. To learn more about Analytics 2011, visit http://www.sas.com/analyticsseries/us .
Views: 1962 SAS Software
Getting YouTube Data with R | User Network and Sentiment Analysis from Comments
 
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R File: https://drive.google.com/open?id=0B5W8CO0Gb2GGWV9jc2hqa2NTdE0 YouTube data File: https://drive.google.com/open?id=0B5W8CO0Gb2GGN1luZHlUNkxiWUU 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: 3527 Bharatendra Rai
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: 10524 CNET
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
Dense Subgraph Discovery - Part 1
 
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Authors: Aristides Gionis, Charalampos E. Tsourakakis Abstract: Finding dense subgraphs is a fundamental graph-theoretic problem, that lies in the heart of numerous graph-mining applications, ranging from finding communities in social networks, to detecting regulatory motifs in DNA, and to identifying real-time stories in news. The problem of finding dense subgraphs has been studied extensively in theoretical computer science, and recently, due to the relevance of the problem in real-world applications, it has attracted considerable attention in the data-mining community. In this tutorial we aim to provide a comprehensive overview of (i) major algorithmic techniques for finding dense subgraphs in large graphs and (ii) graph mining applications that rely on dense subgraph extraction. We will present fundamental concepts and algorithms that date back to 80's, as well as the latest advances in the area, from theoretical and from practical point-of-view. We will motivate the problem of finding dense subgraphs by discussing how it can be used in real-world applications. We will discuss different density definitions and the complexity of the corresponding optimization problems. We will also present efficient algorithms for different density measures and under different computational models. Specifically, we will focus on scalable streaming, distributed and MapReduce algorithms. Finally we will discuss problem variants, extensions, and will provide pointers for future research directions. ACM DL: http://dl.acm.org/citation.cfm?id=2789987 DOI: http://dx.doi.org/10.1145/2783258.2789987