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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: 41214 Alexandra Ott

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Views: 7296 Joey Anthony

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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: 9439 Bart Baesens

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https://www.coursera.org/learn/datavisualization
Views: 1961 Ryo Eng

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Talk Slides: https://drive.google.com/open?id=1nm3jU2sjLxoatWTenffraN3a6xt0QEE8 Deep learning is widely use in several cases with a good match and accuracy, as for example images classifications. But when to come to social networks there is a lot of problems involved, for example how do we represent a network in a neural network without lost node correspondence? Which is the best encode for graphs or is it task dependent? Here I will review the state of art and present the success and fails in the area and which are the perspective. Ana Paula is a Research Staff Member in IBM Research - Brazil, currently work with large amount of data to do Science WITH Data and Science OF Data at IBM Research Brazil. My technical interesting are in data mining and machine learning area specially in graph mining techniques for health and finance data. I am engage in STEAM initiatives to help girls and women to go to math/computer/science are. She is also passion for innovation and thus I become a master inventor at IBM.
Views: 525 PAPIs.io

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Abstract: People love to talk on the web. How can we listen to what they're telling us, and why would we want to? This workshop will discuss some methods of collecting social media data to construct larger---and in some cases, more naturalistic---datasets than laboratory-based experiments yield. We'll cover methods for building datasets through Python-accessible Twitter APIs, and structuring both the search query and the experimental question to obtain data that is appropriate in both content and amount. We'll also discuss connections between data and metadata, with a focus on geolocation, as well as ways to collect online conversations and interactions. Instructor: Gabriel Doyle (Stanford University) --- Before running this tutorial, you'll need to sign up with the Twitter API. Follow the instructions here: https://github.com/Data-on-the-Mind/2017-summer-workshop/blob/master/doyle-twitter/README.md --- Part of the Data on the Mind 2017 summer workshop: http://www.dataonthemind.org/2017-workshop Funded by the Estes Fund: http://www.psychonomic.org/page/estesfund Organized in collaboration with Data on the Mind: http://www.dataonthemind.org Videography by DeNoise Studios: http://www.denoise.com Workshop hashtag: #dataonthemind

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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: 745 MorganMcKinley

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Graph Mining and Analysis Lecture_12 22 December 2015

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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: 2214 Sumo Logic, Inc.

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Views: 1657 The Audiopedia

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- Learn how to Analyse sentiments on anything being said on Twitter - Get your own Twitter developer app key and pull tweets - Understand what is sentiment analytics and text mining - Create impressive word clouds - Map sentiments on any topic and break them into bar graphs
Views: 26249 Equiskill Insights LLP

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Graph Mining and Analysis Hands On Tutorial_1 18 December 2015

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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: 14042 Ben Rodick

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Maksim Tsvetovat, Alex Kouznetsov, Jacqueline Kazil Social Network data is not just Twitter and Facebook - networks permeate our world - yet we often don't know what to do with them. In this tutorial, we will introduce both theory and practice of Soc
Views: 1294 Next Day Video

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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: 15039 Bart Baesens

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How to identify gaps in the current discourse on a specific subject and how to discover what people are looking for but are not able to find. We use text network analysis tool http://infranodus.com to perform this task and demonstrate how you can do the same in 5 minutes using #textmining. #infranodus #seo #google
Views: 1410 Nodus Labs

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Amazon QuickSight is a fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this webinar we will take an in-depth look at the capabilities that Amazon QuickSight has to offer including, connecting to data sources, data preparation, data visualization, and collaboration. Learning Objectives: - Connect to AWS and non-AWS data sources - Prepare data by joining tables, using SQL queries, adding calculated fields, changing field names and data types, and other techniques - Create charts and graphs with various chart types and filtering capabilities
Views: 1186 AWS Online Tech Talks

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I used screencast-o-matic to record my presentation.
Views: 437 Bryan Russowsky

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The focus of social network analysis is on the network of relations. A social network consists of a set of actors (also called nodes or vertices) together with a set of edges (also called arcs) that link pairs of actors. Since edges can share actors (e.g., the A.B edge shares an actor with the B.C edge) this creates a connected web that we think of as a network. For more methods resources see: http://www.methods.manchester.ac.uk
Views: 32461 methodsMcr

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Dr. Steven Skiena, Stony Brook University Michael Hunger, Neo4j Random walk algorithms help better model real-world scenarios, and when applied to graphs, can significantly improve machine learning. Learn how the Deepwalk supervised learning algorithm transfers deep learning techniques from natural language processing to network analysis, and explore the motivations behind graph-enhanced machine learning. #MachineLearning #DeepWalk #NLP
Views: 3469 Neo4j

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We'll install the TwitteR package, get the consumer key and consumer secret token required for searching tweets and getting data. We'll make sure to satisfy all depended packages of TwitteR. And do initial statistics analysis. This is less of a R Statistics Programming Language "tutorial" and more of a learning-by-sharing video. :) Help us caption & translate this video! http://amara.org/v/RHNb/
Views: 11676 Hendy I.

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Algorithms for mining uncertain graph data KDD 2012 Jianzhong Li With the rapid development of advanced data acquisition techniques such as high-throughput biological experiments and wireless sensor networks, large amount of graph-structured data, graph data for short, have been collected in a wide range of applications. Discovering knowledge from graph data has witnessed a number of applications and received a lot of research attentions. Recently, it is observed that uncertainties are inherent in the structures of some graph data. For example, protein-protein interaction (PPI) data can be represented as a graph, where vertices represent proteins, and edges represent PPI's. Due to the limits of PPI detection methods, it is uncertain that a detected PPI exist in practice. Other examples of uncertain graph data include topologies of wireless sensor networks, social networks and so on. Managing and mining such large-scale uncertain graph data is of both theoretical and practical significance. Many solid works have been conducted on uncertain graph mining from the aspects of models, semantics, methodology and algorithms in last few years. A number of research papers on managing and mining uncertain graph data have been published in the database and data mining conferences such as VLDB, ICDE, KDD, CIKM and EDBT. This talk focuses on the data model, semantics, computational complexity and algorithms of uncertain graph mining. In the talk, some typical research work in the field of uncertain graph mining will also be introduced, including frequent subgraph pattern mining, dense subgraph detection, reliable subgraph discovery, and clustering on uncertain graph data.

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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

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Views: 1063 Dazzling Diamonds

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Views: 142 FOSDEM

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Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com Gephi is an open-source network analysis software package written in Java that allows us to visualize all kinds of graphs and networks. In this Gephi tutorial, we walk through how Network Analysis can be used to visually represent large data sets in a way that enables the viewer to get a lot of value from the data just by looking briefly at the graph. Watch this video to learn: - What Network Analysis involves - How to use Gephi to visually represent and analyze data sets - Different examples using Gephi

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Views: 7650 Audimation Services

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Views: 45 The Audiopedia

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Views: 12942 Jinsuh Lee

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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: 5588 Leonid Zhukov

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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: 3116 Cray Inc.

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Views: 89945 edureka!

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* Motivation for combining data mining and human-computer interaction for mining large graphs. * Overview of thesis work: (1) Use "attention routing" techniques to find good starting points of analysis; (2) Mixed-initiative methods to combine human and machine to explore large graphs; (3) scale up computation and interaction by leveraging parallel computation, staging of operations, and approximation. * Thesis statement --------------------- Polo Chau's Thesis Defense Ph.D. in Machine Learning Carnegie Mellon University July 30, 2012
Views: 6717 Duen Horng Chau

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Title: Diversified Temporal Subgraph Pattern Mining Authors: Yi Yang, Fudan University Da Yan, The Chinese University of Hong Kong Huanhuan Wu, The Chinese University of Hong Kong James Cheng*, The Chinese University of Hong Kong Shuigeng Zhou, Fudan University John C.S. Lui, The Chinese University of Hong Kong Abstract: Many graphs in real-world applications, such as telecommunications networks, social-interaction graphs and co-authorship graphs, contain temporal information. However, existing graph mining algorithms fail to exploit these temporal information and the resulting subgraph patterns do not contain any temporal attribute. In this paper, we study the problem of mining a set of diversified temporal subgraph patterns from a temporal graph, where each subgraph is associated with the time interval that the pattern spans. This problem motivates important applications such as finding social trends in social networks, or detecting temporal hotspots in telecommunications networks. We propose a divide-and-conquer algorithm along with effective pruning techniques, and our approach runs 2 to 3 orders of magnitude faster than a baseline algorithm and obtains high-quality temporal subgraph patterns in real temporal graphs. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 272 KDD2016 video

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Views: 7924 jengolbeck

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Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. To learn more about Quantopian, visit http://www.quantopian.com. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 2067 Quantopian

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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: 189 Leonhard Euler

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Web Graph and Web graph mining and hot topics in web search in Hindi

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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

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Kizoa Video Maker - http://www.kizoa.com
Views: 80 Milton Pifano

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Every user of social networks sees content custom-tailored for him or her. But who decides what will be interesting to whom? DW explains how the algorithms on social networks do their job. See more videos in our playlist: https://www.youtube.com/playlist?list=PLT6yxVwBEbi1Buc7NDdT0pAll0DAWroe1
Views: 1474 DW News

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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?

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Views: 12468 Spark Summit

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Cognitive Social Mining Applications in Data Analytics and Forensics Anandakumar Haldorai (Sri Eshwar College of Engineering, India) and Arulmurugan Ramu (Presidency University, India) Release Date: December, 2018 Copyright: © 2019 Pages: 250 ISBN13: 978-1-5225-7522-1 ISBN10: 1-5225-7523-5 EISBN13: 978-1-5225-7523-8 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-7522-1&redirectifunowned=true ___________ Description: Recently, there has been a rapid increase in interest regarding social network analysis in the data mining community. Cognitive radios are expected to play a major role in meeting this exploding traffic demand on social networks due to their ability to sense the environment, analyze outdoor parameters, and then make decisions for dynamic time, frequency, space, resource allocation, and management to improve the utilization of mining the social data. Cognitive Social Mining Applications in Data Analytics and Forensics is an essential reference source that reviews cognitive radio concepts and examines their applications to social mining using a machine learning approach so that an adaptive and intelligent mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this book is ideally designed for social network analysts, researchers, academicians, and industry professionals. ___________ Topics Covered: • Cloud Computing • Cognitive Computing • Data Mining • Healthcare • Indexing • Machine Learning Techniques • Medical Document Clustering • Real-Time Ubiquitous Social Mining Services • Security • Social Mining • Social Network Analysis • Social Platforms
Views: 44 IGI Global

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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: 5342 Microsoft Research

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