Search results “Graph mining techniques social media analysis”
Basics of Social Network Analysis
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
Social Networks for Fraud Analytics
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
DATA MINING   1 Data Visualization   3 1 1  Graphs and Networks
Views: 1961 Ryo Eng
Graph Mining with Deep Learning - Ana Paula Appel (IBM)
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
[Data on the Mind 2017] Social media analysis
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
[Webinar] How to Improve Fraud Detection using Social Network Analytics | #SuccessSeries
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
Graph Mining and Analysis  Lecture_12
Graph Mining and Analysis Lecture_12 22 December 2015
Graph Mining for Log Data Presented by David Andrzejewski
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.
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: 1657 The Audiopedia
Text mining in R and Twitter Sentiment Analytics
- 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
Graph Mining and Analysis Hands On Tutorial_1
Graph Mining and Analysis Hands On Tutorial_1 18 December 2015
Data Mining Techniques to Prevent Credit Card Fraud
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
Analyzing Social Networks with Python
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
Mini Lecture: Social Network Analysis for Fraud Detection
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
Text Mining with Network Analysis for Search Engine Optimization SEO
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
Social Media Analytics using Amazon QuickSight - AWS Online Tech Talks
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
Data mining in social media
I used screencast-o-matic to record my presentation.
Views: 437 Bryan Russowsky
What is Social Network Analysis? by Prof Martin Everett
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
DeepWalk: Turning Graphs Into Features via Network Embeddings
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
@RStudio R Programming Tutorial - 04 Installing TwitteR Package for Social Media Analysis
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.
Algorithms for mining uncertain graph data (KDD 2012)
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.
Jure Leskovec: "Large-scale Graph Representation Learning"
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
Multiplex graph analysis with GraphBLAS
by Gabor Szarnyas At: FOSDEM 2019 https://video.fosdem.org/2019/H.1308/graph_multiplex_analysis_graphblas.webm Introduction Graph analysis workloads present resource-intensive computations that require a large amount of memory and CPU time. Consequently, there an abundance of graph processing tools which build on distributed data processing frameworks, including Spark GraphX, Flink Gelly and Giraph (which runs on Hadoop). According to a recent survey, most of these systems build on the vertex-centric programming model, originally introduced in Google’s Pregel paper. This model defines graph analytical algorithms in terms of vertices communicating with their neighbours through message passing, which allows both easy parallelization (for the systems) and intuitive formalization of the computation (for developers). While these systems indeed exhibit horizontal scalability, they introduce numerous inefficiencies requiring a large amount of resources even for moderately sized graphs. Most practical applications only use graphs up to a few hundred million vertices and edges, which can now be stored comfortably on a single machine. For such graphs, it is worth investigating techniques that allow their evaluation without the additional cost and complexity of operating a distributed cluster. GraphBLAS The GraphBLAS initiative is an effort to design a set of standard building blocks that allow users to formulate graph computations in the language of linear algebra, using operations on sparse adjacency matrices defined on custom semirings. Since its inception, GraphBLAS has been implemented for multiple languages (e.g. C, C++, and Java). Additionally, GraphBLAS is being designed in collaboration with hardware vendors (such as Intel and Nvidia) to define a standardized set of interfaces, which will allow building specialized hardware components for graph processing in the future. Multiplex graph metrics Graph analysis has a significant overlap with network science, a field that aims to uncover the hidden structural properties of graphs and determine the interplay between their vertices. Most works in network science only study homogeneous (monoplex) graphs, and do not distinguish between different types of vertices and edges. We believe this abstraction is wasteful for most real-life networks, which are heterogeneous (multiplex) and emerge by different types of interactions. To illustrate such analyses, we calculated multiplex clustering metrics on the Paradise papers data set to find interesting entities that were engaged in disproportionately high levels of activities with their interconnected neighbours. We found that even on this relatively small data set (2M vertices and 3M edges), naive implementations did not terminate in days. Hence, we adapted techniques from GraphBLAS to optimize the computations to finish in a few minutes. Outline of the talk This talk gives a brief overview of how linear algebra can be used to define graph computations on monoplex graphs, and how we applied it to speedup the calculation of multiplex graph metrics. We present the lessons learnt while experimenting with sparse matrix libraries in C, Java, and Julia. Our graph analyzer framework is available as open-source. Intended audience: users interested in applying multiplex graph analytical techniques for their problems and developers who strive to implement high-performing graph analytical computations. Speaker biography. Gabor Szarnyas is a researcher working on graph processing techniques. His core research areas are live graph pattern matching, benchmarking graph queries, and analyzing large-scale multiplex networks. His main research project is ingraph, an openCypher-compatible query engine supporting live query evaluation. His research team was the first to publish a formalisation that captures the semantics of a core subset of the openCypher language. Gabor works at the Budapest University of Technology and Economics, teaching system modelling and database theory. He conducted research visits at the University of York, McGill University and the University of Waterloo. He is a member of the openCypher Implementers Group and the LDBC Social Network Benchmark task force. He received 1st prize in the MODELS 2016 ACM Student Research Competition conference and 2nd prize at the SIGMOD 2018 competition. He is a frequent speaker at industrial conferences (FOSDEM, GraphConnect) and meetups (openCypher meetup NYC, Budapest Neo4j meetup). Room: H.1308 (Rolin) Scheduled start: 2019-02-02 14:40:00+01
Views: 142 FOSDEM
Gephi Tutorial - How to use Gephi for Network Analysis
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
Views: 24008 Fullstack Academy
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: 45 The Audiopedia
Facebook text analysis on R
For more information, please visit http://web.ics.purdue.edu/~jinsuh/.
Views: 12942 Jinsuh Lee
Network Analysis. Lecture 17 (part 1). Label propagation on graphs.
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
What is Graph Analytics?
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.
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 89945 edureka!
Data Mining Meets HCI: Making Sense of Large Graphs [1 of 5: Introduction]
* 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
KDD2016 paper 702
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
Visual Analysis of Social Networks
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: 7924 jengolbeck
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
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
FOSDEM 2013 - Mining Social Data
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
Web Graph and Web graph mining and hot topics in web search in Hindi
Web Graph and Web graph mining and hot topics in web search in Hindi
g-Miner: Interactive Visual Group Mining on Multivariate Graphs
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
Kizoa Video Maker: Graph Mining Test
Kizoa Video Maker - http://www.kizoa.com
Views: 80 Milton Pifano
Hidden code: Algorithms in social networks | DW English
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
Data Mining and Network Analysis - Eelke Heemskerk
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?
Cognitive Social Mining Applications in Data Analytics and Forensics
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
Social Network Analysis
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
Introduction to Text Analytics with R: Overview
The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – Overview of the spam dataset used throughout the series – Loading the data and initial data cleaning – Some initial data analysis, feature engineering, and data visualization About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz5_y0 Watch the latest video tutorials here: https://hubs.ly/H0hz61V0 See what our past attendees are saying here: https://hubs.ly/H0hz6-S0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 76014 Data Science Dojo
Jonathan Ronen - Social Networks and Protest Participation: Evidence from 130 Million Twitter Users
Description Data mining social networks for evidence of political participation. A demonstration of python being used to data mine the twitter conversations around the #JeSuisCharlie hashtag, and analyzing it to learn about real world protest behavior. Abstract Pinning down the role of social ties in the decision to protest has been notoriously elusive, largely due to data limitations. The era of social media and its global use by protesters offers an unprecedented opportunity to observe real-time social ties and online behavior, though often without an attendant measure of real-world behavior. We collect data on Twitter activity during the 2015 Charlie Hebdo protest in Paris which, unusually, record real-world protest attendance and high-resolution network structure. We draw on a theory of participation in which protest decisions depend on exposure to others' intentions, and network position determines exposure. Our findings are strong and consistent with this theory, showing that, relative to comparable Twitter users, protesters are significantly more connected to one another via direct, indirect, triadic, and reciprocated ties. These results offer the first large-scale empirical support for the claim that social network structure has consequences for protest participation. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 343 PyData
The Logic of Data Mining in Social Research
This video is a brief introduction for undergraduates to the logic (not the nitty-gritty details) of data mining in social science research. Four orienting tips for getting started and placing data mining in the broader context of social research are included.
Views: 405 James Cook