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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: 37046 Alexandra Ott
Graph Mining with Deep Learning - Ana Paula Appel (IBM)
 
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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: 199 PAPIs.io
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: 8756 Bart Baesens
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: 867 The Audiopedia
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: 1989 Sumo Logic, Inc.
Social Media Analytics Introduction
 
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Please view the full copyright statement at: http://public.dhe.ibm.com/software/data/sw-library/services/legalnotice.pdf
Social Media Social Data and Python: 4 - Social Media Mining Techniques
 
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In this video we will briefly discuss the overall process for building a social media mining application, before digging into the details. ----- ------ 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: 2310 Sukhvinder Singh
[Data on the Mind 2017] Social media analysis
 
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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
Data mining in social media
 
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I used screencast-o-matic to record my presentation.
Views: 384 Bryan Russowsky
Graph Mining Video
 
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Views: 268 Dennys Parrales
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: 14648 Bart Baesens
Social Media Mining
 
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Hundreds of millions of people spending countless hours on social media to share, communicate, connect, interact, and create user-generated data. Using data mining, machine learning, text mining, social network analysis, and information retrieval, we could mine valuable knowledge for social science researches and business marketing proposes. This project was our graduation project. we used a real data from Facebook to give a proper recommendation for users about movies and series due to the social group that our users belongs to, we also managed to recommend friends to a user due to interests similarity.
PageRank Algorithm - Example
 
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Full Numerical Methods Course: http://bit.ly/numerical-methods-java FREE Beginner Java Course: http://bit.ly/2rMkyxN
Views: 62944 Balazs Holczer
Graph Mining and Analysis  Lecture_12
 
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Graph Mining and Analysis Lecture_12 22 December 2015
Network Analysis Tutorial: Network Visualization
 
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This is the 3rd video of chapter 1 of Network Analysis by Eric Ma. Take Eric's course: https://www.datacamp.com/courses/network-analysis-in-python-part-1 From online social networks such as Facebook and Twitter to transportation networks such as bike sharing systems, networks are everywhere, and knowing how to analyze this type of data will open up a new world of possibilities for you as a Data Scientist. This course will equip you with the skills to analyze, visualize, and make sense of networks. You'll apply the concepts you learn to real-world network data using the powerful NetworkX library. With the knowledge gained in this course, you'll develop your network thinking skills and be able to start looking at your data with a fresh perspective! Transcript: You may have seen node-link diagrams involving more than a hundred thousand nodes. They purport to show a visual representation of the network, but in reality just show a hairball. In this section, we are going to look at alternate ways of visualizing network data that are much more rational. I’m going to introduce to you three different types of network visualizations. The first is visualizing a network using a Matrix Plot. The second is what we call an “Arc Plot”, and the third is called “Circos Plot”. Let’s start first with a Matrix Plot. In a Matrix Plot, nodes are the rows and columns of a matrix, and cells are filled in according to whether an edge exists between the pairs of nodes. On these slides, the left matrix is the matrix plot of the graph on the right. In an undirected graph, the matrix is symmetrical around the diagonal, which I’ve highlighted in grey. I’ve also highlighted one edge in the toy graph, edge (A, B), which is equivalent to the edge (B, A). Likewise for edge (A, C), it is equivalent to the edge (C, A), because there’s no directionality associated with it. If the graph were a directed graph, then the matrix representation is not necessarily going to be symmetrical. In this example, we have a bidirectional edge between A and C, but only an edge from A to B and not B to A. Thus, we will have (A, B) filled in, but not (B, A). If the nodes are ordered along the rows and columns such that neighbours are listed close to one another, then a matrix plot can be used to visualize clusters, or communities, of nodes. Let’s now move on to Arc Plots. An Arc Plot is a transformation of the node-link diagram layout, in which nodes are ordered along one axis of the plot, and edges are drawn using circular arcs from one node to another. If the nodes are ordered according to some some sortable rule, e.g. age in a social network of users, or otherwise grouped together, e.g. by geographic location in map for a transportation network, then it will be possible to visualize the relationship between connectivity and the sorted (or grouped) property. Arc Plots are a good starting point for visualizing a network, as it forms the basis of the later plots that we’ll take a look at. Let’s now move on to Circos Plots. A CircosPlot is a transformation of the ArcPlot, such that the two ends of the ArcPlot are joined together into a circle. Circos Plots were originally designed for use in genomics, and you can think of them as an aesthetic and compact alternative to Arc Plots. You will be using a plotting utility that I developed called nxviz. Here’s how to use it. Suppose we had a Graph G in which we added nodes and edges. To visualize it using nxviz, we first need to import nxviz as nv, and import matplotlib to make sure that we can show the plot later. Next, we instantiate a new nv.ArcPlot() object, and pass in a graph G. We can also order nodes by the values keyed on some “key”. Finally, we can call the draw() function, and as always, we also call plt.show(). The code example here shows you how to create an Arc Plot using nxviz, and you’ll get a chance to play around with the other plots in the exercises. Alright! Let’s get hacking! https://www.datacamp.com/courses/network-analysis-in-python-part-1
Views: 7858 DataCamp
Pseudo Feature Extraction in Social Network Analysis and Text Mining
 
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This is a webinar I delivered as a part of a webinar series entitled "We are all Social Things" organized by the IS department at King Saud University - Female Section The recording started a bit late, but you will be able to follow.
Views: 766 Ibrahim Almosallam
Data Mining Techniques to Prevent Credit Card Fraud
 
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Includes a brief introduction to credit card fraud, types of credit card fraud, how fraud is detected, applicable data mining techniques, as well as drawbacks.
Views: 12220 Ben Rodick
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: 16890 Neo4j
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
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: 12085 Tableau Software
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: 722 ThePerfectMarket
DeepWalk: Turning Graphs Into Features via Network Embeddings
 
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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: 1355 Neo4j
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: 4262 Microsoft Research
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: 744 PyData
Week 7: Text Mining Conceptual Overview of Techniques
 
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Carolyn Rose discusses text mining conceptual overview of techniques for week 7 of DALMOOC.
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: 4812 Leonid Zhukov
Graph Mining and Analysis Hands On Tutorial_1
 
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Graph Mining and Analysis Hands On Tutorial_1 18 December 2015
Cognitive Social Mining Applications in Data Analytics and Forensics
 
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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 Copyrighjt: © 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 Network Analysis • Social Platforms
Views: 20 IGI Global
Andre Panisson: Exploring temporal graph data with Python
 
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PyData NYC 2015 We will see how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications in the academy and industry. We will see a use case where tensor decomposition was used to extract structural and temporal signatures from a time-varying social network collected from wearable proximity sensors. Tensor decompositions have gained a steadily increasing popularity in data mining applications. Data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as multidimensional structures such as tensors. For example, time-varying social networks collected from wearable proximity sensors can be represented as 3-way tensors. By representing this data as tensors, we can use tensor decomposition to extract community structures with their structural and temporal signatures. The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications. Slides available here: http://www.slideshare.net/panisson/exploring-temporal-graph-data-with-python-a-study-on-tensor-decomposition-of-wearable-sensor-data Github repo: https://github.com/panisson/ntf-school
Views: 1536 PyData
The beauty of data visualization - David McCandless
 
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View full lesson: http://ed.ted.com/lessons/david-mccandless-the-beauty-of-data-visualization David McCandless turns complex data sets, like worldwide military spending, media buzz, and Facebook status updates, into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut -- and it may just change the way we see the world. Talk by David McCandless.
Views: 574841 TED-Ed
Data Mining Open Flights Social Networking Presentation INFS770
 
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This is a presentation created for my Final Assignment in my social networking class. It contains a social networking presentation that I analyzed in gephi.
Views: 216 Tom Austin
Predicting Stock Prices - Learn Python for Data Science #4
 
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In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library. The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62 I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/ Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft Please share this video, like, comment and subscribe! That's what keeps me going. and please support me on Patreon!: https://www.patreon.com/user?u=3191693 Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 528745 Siraj Raval
Evolution of snap (Gource Visualization)
 
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Gource visualization of snap (https://github.com/snap-stanford/snap). Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library. This visualization was generated with the following command: gource \ --path path/to/repo \ --seconds-per-day 0.15 \ --title "snap" \ -1280x720 \ --file-idle-time 0 \ --auto-skip-seconds 0.75 \ --multi-sampling \ --stop-at-end \ --highlight-users \ --hide filenames,mouse,progress \ --max-files 0 \ --background-colour 000000 \ --disable-bloom \ --font-size 24 \ --output-ppm-stream - \ --output-framerate 30 \ -o - \ | ffmpeg \ -y \ -r 60 \ -f image2pipe \ -vcodec ppm \ -i - \ -vcodec libx264 \ -preset ultrafast \ -pix_fmt yuv420p \ -crf 1 \ -threads 0 \ -bf 0 \ path/to/output.mp4 Installation (OS X): brew install gource brew install ffmpeg More information: http://gource.io/ https://github.com/acaudwell/Gource Why make this visualization? - I'm studying how popular projects evolve
Views: 364 Landon Wilkins
[Webinar] How to Improve Fraud Detection using Social Network Analytics | #SuccessSeries
 
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In this webinar, you will learn how to: - use social networks for fraud detection - build a social network fraud classifier - evaluate a social network fraud classifier
Views: 723 MorganMcKinley
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
Visualize your social network
 
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VisualSage for Facebook provides innovative and interactive graph representations of your social network.
Views: 2250 VisualSage
Dense Subgraph Discovery - Part 2
 
01:39:37
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
Dense Subgraph Discovery - Part 1
 
01:39:42
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
Twitter Sentiment Analysis in Python using Tweepy and TextBlob
 
25:45
In this tutorial we will do sentiment analysis in python by analyzing tweets about any topic happening in the world to see how positive or negative it's emotion is. We will use tweepy for fetching tweets and textblob for natural language processing (nlp) Text Based Tutorial http://www.letscodepro.com/Twitter-Sentiment-Analysis/ Github link for project https://github.com/the-javapocalypse/Twitter-Sentiment-Analysis Further Reading Material http://docs.tweepy.org/en/v3.5.0/api.html http://textblob.readthedocs.io/en/dev/ Please Subscribe! And like. And comment. That's what keeps me going. Follow Me Facebook: https://www.facebook.com/javapocalypse Instagram: https://www.instagram.com/javapocalypse
Views: 25109 Javapocalypse
Lead Generation Techniques & Data Visualisation Tools - Growth Insights #5
 
06:46
Welcome back to Growth Insights! In this latest episode (number 5 already?!) we’ll be sharing Lead Generation Techniques & Data Visualisation Tools - Growth Insights #5. The Growth Insights series is our jam-packed, fast-paced video format in which we’ll introduce you to the growth tools, techniques and hacks our team has come across over the past few weeks. All under 7 minutes on a tri-weekly basis. This particular episode focuses on Lead Generation Techniques & Data Visualisation Tools. If you come across a tool, website or article mentioned in the video that you want to look into further, check out the links below! 0:16 - Dux Soup https://www.dux-soup.com/ 0:37 - Grouply https://grouply.io/ 0:54 - Revealbot https://revealbot.com/ 1:18 - Instanobel https://www.instanobel.com/ 1:29 - Data Gif Maker https://datagifmaker.withgoogle.com/ 1:43 - Free open Slack channels a plenty! http://bit.ly/1000slack 1:55 - Chatviz https://moovel.github.io/teamchatviz/ 2:08 - Statista’s Referral graph http://bit.ly/2qusuUI 2:10 - Chart: How much time adults spend online http://www.kpcb.com/internet-trends 2:53 - Ecommerce benchmarks analysis http://bit.ly/2t1PFL2 3:10 - Add this to the end of a competitor’s shopify store URL to see their top selling products: /collections/all?sort_by=best-selling&page=1 3:41 - Referral traffic: search vs browse http://bit.ly/1MrgOV9 3:58 - Create a brand identity in 60 seconds with http://www.hipsterbusiness.name & https://builtbyemblem.com/ 5:13 - How important is retention? Read: http://bit.ly/1xH1n5b 5:26 - Data visualisation tool https://lists.design/ 5:40 - Andy Carvell’s push notifications quadrant http://bit.ly/2sIJecx 6:30 - Sign up for our first beta AI course here: grow.ac/aicourse 6:34 - Don’t forget to subscribe! https://www.youtube.com/channel/UCj6owuAZrJNsjQzc2ZtILIw?sub_confirmation=1 In this episode of our Growth Insights series we also cover: - lead generation automation - Facebook ad automation - AdWords automation - instagram automation tool - Instanobel - data gifmaker - public slack groups - community visualisation - referral traffic trends - E commerce benchmarks - competitor analysis hacks - how to create a brand identity in 60 sec - brand identity tools - data visualisation - push notifications framework ------------------------------------------------------- Amsterdam bound? Want to make AI your secret weapon? Join our A.I. for Marketing and growth Course! A 2-day course in Amsterdam. No previous skills or coding required! https://hubs.ly/H0dkN4W0 OR Check out our 2-day intensive, no-bullshit, skills and knowledge Growth Hacking Crash Course: https://hubs.ly/H0dkN4W0 OR our 6-Week Growth Hacking Evening Course: https://hubs.ly/H0dkN4W0 OR Our In-House Training Programs: https://hubs.ly/H0dkN4W0 OR The world’s only Growth & A.I. Traineeship https://hubs.ly/H0dkN4W0 Make sure to check out our website to learn more about us and for more goodies: https://hubs.ly/H0dkN4W0 London Bound? Join our 2-day intensive, no-bullshit, skills and knowledge Growth Marketing Course: https://hubs.ly/H0dkN4W0 ALSO! Connect with Growth Tribe on social media and stay tuned for nuggets of wisdom, updates and more: Facebook: https://www.facebook.com/GrowthTribeIO/ LinkedIn: https://www.linkedin.com/company/growth-tribe Twitter: https://twitter.com/GrowthTribe/ Instagram: https://www.instagram.com/growthtribe/ Video URL: https://youtu.be/-1mMoO0UZ_E -~-~~-~~~-~~-~- Please watch: "Artificial Intelligence Tools & Cold Emailing Tips - Growth Insights #8 " https://www.youtube.com/watch?v=mCp5zYl3hD4 -~-~~-~~~-~~-~-
Views: 9574 Growth Tribe
Visual Analysis of Social Networks
 
35:19
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: 7330 jengolbeck
Some social network analysis experiment
 
00:33
community detection and graph visualization using gephi
Views: 239 Mattia Dimauro
Analyzing Social Networks with Python
 
03:11:11
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: 1241 Next Day Video
Introduction to Cluster Analysis with R - an Example
 
18:11
Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi 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: 101515 Bharatendra Rai
Mining Social Media Data for Understanding Students’ Learning Experiences
 
09:09
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.
Large-Scale Graph Data Mining with MapReduce a Bag of Tricks, by Nima Sarshar, Ph.D. Intuit 20130624
 
52:56
Speaker: Nima Sarshar, Ph.D. Intuit Event Details Many modern large-scale data mining problems are defined on graphs (think of People you May Know) , or have a graph representation (think of Collaborative filtering and it's bi-partite graph representation). This makes Hadoop and the MapReduce framework natural candidates to tackle them. Some graph processing algorithms, e.g. global PageRank, can be ported into the MapReduce framework rather straightforwardly. Others require various degrees of combinatorial tricks. In this talk, we review several fundamental graph processing algorithms that require careful, and often beautiful, tricks to scale when dealing with very large graphs. These include enumerating triangles and rectangles (e.g., to find Friends in Common at scale) , creating induced latent networks and collaborative filtering on bi-partite graphs, Personalized PageRank and more. We will describe some of the applications of these algorithms at Intuit. Speaker Bio Nima is a Senior Data Scientist at Intuit. Before Intuit he was the co-founder and CTO of Haileo Inc, a Santa Clara based star up specializing in context-based video advertisement. Before that, he was an Associate Prof. of Software Engineering at the University of Regina, Canada.
Views: 2537 San Francisco Bay ACM