Evolving Networks and Social Network Analysis Methods And
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Discover the Golden Paths, Unique Sequences and Marvelous Associations out of Your Big Data Using Link Analysis in SAS® Enterprise Miner TM
MWSUG 2016 – Paper AA04 Discover the golden paths, unique sequences and marvelous associations out of your big data using Link Analysis in SAS® Enterprise Miner TM Delali Agbenyegah, Alliance Data Systems, Columbus, OH Candice Zhang, Alliance Data Systems, Columbus, OH ABSTRACT The need to extract useful information from large amount of data to positively influence business decisions is on the rise especially with the hyper expansion of retail data collection and storage and the advancement in computing capabilities. Many enterprises now have well established databases to capture Omni channel customer transactional behavior at the product or Store Keeping Unit (SKU) level. Crafting a robust analytical solution that utilizes these rich transactional data sources to create customized marketing incentives and product recommendations in a timely fashion to meet the expectations of the sophisticated shopper in our current generation can be daunting. Fortunately, the Link Analysis node in SAS® Enterprise Miner TM provides a simple but yet powerful analytical tool to extract, analyze, discover and visualize the relationships or associations (links) and sequences between items in a transactional data set up and develop item-cluster induced segmentation of customers as well as next-best offer recommendations. In this paper, we discuss the basic elements of Link Analysis from a statistical perspective and provide a real life example that leverages Link Analysis within SAS Enterprise Miner to discover amazing transactional paths, sequences and links. INTRODUCTION A financial fraud investigator may be interested in exploring the relationship between the financial transactions of suspicious customers, the BNI may want to analyze the social network of individuals identified as terrorists to conduct further investigation or a medical doctor may be interested in understanding the association between different medical treatments on patients and their corresponding results. -
Opinion-Based Centrality in Multiplex Networks: a Convex Optimization Approach
Opinion-Based Centrality in Multiplex Networks: A Convex Optimization Approach Alexandre Reiffers-Masson and Vincent Labatut Laboratoire Informatique d'Avignon (LIA) EA 4128 Universit´ed'Avignon, France June 12, 2017 Abstract Most people simultaneously belong to several distinct social networks, in which their relations can be different. They have opinions about certain topics, which they share and spread on these networks, and are influenced by the opinions of other persons. In this paper, we build upon this observation to propose a new nodal centrality measure for multiplex networks. Our measure, called Opinion centrality, is based on a stochastic model representing opinion propagation dynamics in such a network. We formulate an optimization problem consisting in maximizing the opinion of the whole network when controlling an external influence able to affect each node individually. We find a mathematical closed form of this problem, and use its solution to derive our centrality measure. According to the opinion centrality, the more a node is worth investing external influence, and the more it is central. We perform an empirical study of the proposed centrality over a toy network, as well as a collection of real-world networks. Our measure is generally negatively correlated with existing multiplex centrality measures, and highlights different types of nodes, accordingly to its definition. Cite as: A. Reiffers-Masson & V. Labatut. Opinion-based Centrality in Multiplex Networks. Network Science, 5(2):213-234, 2017. Doi: 10.1017/nws.2017.7 1 Introduction In our ultra-connected world, many people simultaneously belong to several distinct social networks, in which their relations can be different. -
Syllabus Organizational Network Analysis V2.2
SYLLABUS Organizational Network Analysis Self-paced online Learn to set up an organizational network analysis and create masterclass an x-ray vision into the inner workings of your organization. INDEX About the AIHR Academy Page 3 Masterclass overview Page 4 Learning objectives Page 6 Who you will learn from Page 7 What you will learn Page 8 Your success team Page 13 Frequently Asked Questions Page 14 Enroll Now Page 16 Organizational Network Analysis | Syllabus Copyright © HR Analytics Academy | Page 2 ABOUT THE AIHR ACADEMY With the HR Analytics Academy we recorded so that you can learn whenever and teach the skills that you need in order wherever it’s most convenient. By placing to succeed in the field of People you in the driver’s seat we enable you to optimize your own learning curve and Analytics. By teaching you to leverage complete each course at your own pace. the power of data, we enable you to claim the strategic impact that you Practical bite-sized lessons deserve. The practical nature of our courses and the People Analytics is about leveraging data in way they are structured is what sets us apart. order to make better informed (data-driven) Our lessons are bite-sized and conveniently people decisions. Decisions which in the end split up into several modules. Within each drive better outcomes for both the business module you will typically find a combination and employees. of 3 – 4 video lessons, a short quiz, reading materials that provide extra context, a piece Online learning portal of bonus content, and a practical assignment All of our courses and masterclasses are that will help you put your new skills into delivered through the online learning portal. -
Introduction to Network Science & Visualisation
IFC – Bank Indonesia International Workshop and Seminar on “Big Data for Central Bank Policies / Building Pathways for Policy Making with Big Data” Bali, Indonesia, 23-26 July 2018 Introduction to network science & visualisation1 Kimmo Soramäki, Financial Network Analytics 1 This presentation was prepared for the meeting. The views expressed are those of the author and do not necessarily reflect the views of the BIS, the IFC or the central banks and other institutions represented at the meeting. FNA FNA Introduction to Network Science & Visualization I Dr. Kimmo Soramäki Founder & CEO, FNA www.fna.fi Agenda Network Science ● Introduction ● Key concepts Exposure Networks ● OTC Derivatives ● CCP Interconnectedness Correlation Networks ● Housing Bubble and Crisis ● US Presidential Election Network Science and Graphs Analytics Is already powering the best known AI applications Knowledge Social Product Economic Knowledge Payment Graph Graph Graph Graph Graph Graph Network Science and Graphs Analytics “Goldman Sachs takes a DIY approach to graph analytics” For enhanced compliance and fraud detection (www.TechTarget.com, Mar 2015). “PayPal relies on graph techniques to perform sophisticated fraud detection” Saving them more than $700 million and enabling them to perform predictive fraud analysis, according to the IDC (www.globalbankingandfinance.com, Jan 2016) "Network diagnostics .. may displace atomised metrics such as VaR” Regulators are increasing using network science for financial stability analysis. (Andy Haldane, Bank of England Executive -
Modeling Customer Preferences Using Multidimensional Network Analysis in Engineering Design
Modeling customer preferences using multidimensional network analysis in engineering design Mingxian Wang1, Wei Chen1, Yun Huang2, Noshir S. Contractor2 and Yan Fu3 1 Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA 2 Science of Networks in Communities, Northwestern University, Evanston, IL 60208, USA 3 Global Data Insight and Analytics, Ford Motor Company, Dearborn, MI 48121, USA Abstract Motivated by overcoming the existing utility-based choice modeling approaches, we present a novel conceptual framework of multidimensional network analysis (MNA) for modeling customer preferences in supporting design decisions. In the proposed multidimensional customer–product network (MCPN), customer–product interactions are viewed as a socio-technical system where separate entities of `customers' and `products' are simultaneously modeled as two layers of a network, and multiple types of relations, such as consideration and purchase, product associations, and customer social interactions, are considered. We first introduce a unidimensional network where aggregated customer preferences and product similarities are analyzed to inform designers about the implied product competitions and market segments. We then extend the network to a multidimensional structure where customer social interactions are introduced for evaluating social influence on heterogeneous product preferences. Beyond the traditional descriptive analysis used in network analysis, we employ the exponential random graph model (ERGM) as a unified statistical -
Dynamic Social Network Analysis: Present Roots and Future Fruits
Dynamic Social Network Analysis: Present Roots and Future Fruits Ms. Nancy K Hayden Project Leader Defense Threat Reduction Agency Advanced Systems and Concepts Office Stephen P. Borgatti, Ronald L. Breiger, Peter Brooks, George B. Davis, David S. Dornisch, Jeffrey Johnson, Mark Mizruchi, Elizabeth Warner July 2009 DEFENSE THREAT REDUCTION AGENCY •ADVANCED SYSTEMS AND CONCEPTS OFFICE REPORT NUMBER ASCO 2009 009 The mission of the Defense Threat Reduction Agency (DTRA) is to safeguard America and its allies from weapons of mass destruction (chemical, biological, radiological, nuclear, and high explosives) by providing capabilities to reduce, eliminate, and counter the threat, and mitigate its effects. The Advanced Systems and Concepts Office (ASCO) supports this mission by providing long-term rolling horizon perspectives to help DTRA leadership identify, plan, and persuasively communicate what is needed in the near term to achieve the longer-term goals inherent in the agency’s mission. ASCO also emphasizes the identification, integration, and further development of leading strategic thinking and analysis on the most intractable problems related to combating weapons of mass destruction. For further information on this project, or on ASCO’s broader research program, please contact: Defense Threat Reduction Agency Advanced Systems and Concepts Office 8725 John J. Kingman Road Ft. Belvoir, VA 22060-6201 [email protected] Or, visit our website: http://www.dtra.mil/asco/ascoweb/index.htm Dynamic Social Network Analysis: Present Roots and Future Fruits Ms. Nancy K. Hayden Project Leader Defense Threat Reduction Agency Advanced Systems and Concepts Office and Stephen P. Borgatti, Ronald L. Breiger, Peter Brooks, George B. Davis, David S. -
Link Analysis Using SAS Enterprise Miner
Link Analysis Using SAS® Enterprise Miner™ Ye Liu, Taiyeong Lee, Ruiwen Zhang, and Jared Dean SAS Institute Inc. ABSTRACT The newly added Link Analysis node in SAS® Enterprise MinerTM visualizes a network of items or effects by detecting the linkages among items in transactional data or the linkages among levels of different variables in training data or raw data. This node also provides multiple centrality measures and cluster information among items so that you can better understand the linkage structure. In addition to the typical linkage analysis, the node also provides segmentation that is induced by the item clusters, and uses weighted confidence statistics to provide next-best-offer lists for customers. Examples that include real data sets show how to use the SAS Enterprise Miner Link Analysis node. INTRODUCTION Link analysis is a popular network analysis technique that is used to identify and visualize relationships (links) between different objects. The following questions could be nontrivial: Which websites link to which other ones? What linkage of items can be observed from consumers’ market baskets? How is one movie related to another based on user ratings? How are different petal lengths, width, and color linked by different, but related, species of flowers? How are specific variable levels related to each other? These relationships are all visible in data, and they all contain a wealth of information that most data mining techniques cannot take direct advantage of. In today’s ever-more-connected world, understanding relationships and connections is critical. Link analysis is the data mining technique that addresses this need. In SAS Enterprise Miner, the new Link Analysis node can take two kinds of input data: transactional data and non- transactional data (training data or raw data). -
Multilayer Networks
Journal of Complex Networks (2014) 2, 203–271 doi:10.1093/comnet/cnu016 Advance Access publication on 14 July 2014 Multilayer networks Mikko Kivelä Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK Alex Arenas Departament d’Enginyeria Informática i Matemátiques, Universitat Rovira I Virgili, 43007 Tarragona, Spain Marc Barthelemy Downloaded from Institut de Physique Théorique, CEA, CNRS-URA 2306, F-91191, Gif-sur-Yvette, France and Centre d’Analyse et de Mathématiques Sociales, EHESS, 190-198 avenue de France, 75244 Paris, France James P. Gleeson MACSI, Department of Mathematics & Statistics, University of Limerick, Limerick, Ireland http://comnet.oxfordjournals.org/ Yamir Moreno Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza 50018, Spain and Department of Theoretical Physics, University of Zaragoza, Zaragoza 50009, Spain and Mason A. Porter† Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, by guest on August 21, 2014 Oxford OX2 6GG, UK and CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK †Corresponding author. Email: [email protected] Edited by: Ernesto Estrada [Received on 16 October 2013; accepted on 23 April 2014] In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such ‘multilayer’ features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize ‘traditional’ network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. -
A Python Library to Model and Analyze Diffusion Processes Over Complex Networks
International Journal of Data Science and Analytics (2018) 5:61–79 https://doi.org/10.1007/s41060-017-0086-6 APPLICATIONS NDlib: a python library to model and analyze diffusion processes over complex networks Giulio Rossetti2 · Letizia Milli1,2 · Salvatore Rinzivillo2 · Alina Sîrbu1 · Dino Pedreschi1 · Fosca Giannotti2 Received: 19 October 2017 / Accepted: 11 December 2017 / Published online: 20 December 2017 © Springer International Publishing AG, part of Springer Nature 2017 Abstract Nowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground. To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib,an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians. Keywords Social network analysis software · Epidemics · Opinion dynamics 1 Introduction be modeled as networks and, as such, analyzed. Undoubt- edly, such pervasiveness has produced an amplification in the In the last decades, social network analysis, henceforth SNA, visibility of network analysis studies thus making this com- has received increasing attention from several heterogeneous plex and interesting field one of the most widespread among fields of research. Such popularity was certainly due to the higher education centers, universities and academies. Given flexibility offered by graph theory: a powerful tool that the exponential diffusion reached by SNA, several tools were allows reducing countless phenomena to a common ana- developed to make it approachable to the wider audience pos- lytical framework whose basic bricks are nodes and their sible. -
Examples of Online Social Network Analysis Social Networks
Examples of online social network analysis Social networks • Huge field of research • Data: mostly small samples, surveys • Multiplexity Issue of data mining • Longitudinal data McPherson et al, Annu. Rev. Sociol. (2001) New technologies • Email networks • Cellphone call networks • Real-world interactions • Online networks/ social web NEW (large-scale) DATASETS, longitudinal data New laboratories • Social network properties – homophily – selection vs influence • Triadic closure, preferential attachment • Social balance • Dunbar number • Experiments at large scale... 4 Another social science lab: crowdsourcing, e.g. Amazon Mechanical Turk Text http://experimentalturk.wordpress.com/ New laboratories Caveats: • online links can differ from real social links • population sampling biases? • “big” data does not automatically mean “good” data 7 The social web • social networking sites • blogs + comments + aggregators • community-edited news sites, participatory journalism • content-sharing sites • discussion forums, newsgroups • wikis, Wikipedia • services that allow sharing of bookmarks/favorites • ...and mashups of the above services An example: Dunbar number on twitter Fraction of reciprocated connections as a function of in- degree Gonçalves et al, PLoS One 6, e22656 (2011) Sharing and annotating Examples: • Flickr: sharing of photos • Last.fm: music • aNobii: books • Del.icio.us: social bookmarking • Bibsonomy: publications and bookmarks • … •“Social” networks •“specialized” content-sharing sites •Users expose profiles (content) and links -
Homophily and Long-Run Integration in Social Networks∗
Homophily and Long-Run Integration in Social Networks∗ Yann Bramoulléy Sergio Currariniz Matthew O. Jacksonx Paolo Pin{ Brian W. Rogersk January 19, 2012 Abstract We study network formation in which nodes enter sequentially and form connections through a combination of random meetings and network-based search, as in Jackson and Rogers (2007). We focus on the impact of agents' heterogeneity on link patterns when connections are formed under type-dependent biases. In particular, we are concerned with how the local neighborhood of a node evolves as the node ages. We provide a surprising general result on long-run in- tegration whereby the composition of types in a node's neighborhood approaches the global type distribution, provided that the search part of the meeting process is unbiased. Integration, however, occurs only for suciently old nodes, while the aggregate distribution of connections still reects the bias of the random process. For a special case of the model, we analyze the form of these biases with regard to type-based degree distributions and group-level homophily patterns. Finally, we illustrate aspects of the model with an empirical application to data on citations in physics journals. JEL Codes: A14, D85, I21. ∗Following the suggestion of JET editors, this paper draws from two working papers developed independently: Bramoullé and Rogers (2010) and Currarini, Jackson and Pin (2010b). We gratefully acknowledge nancial support from the NSF under grant SES-0961481 and we thank Vincent Boucher for his research assistance. We also thank Habiba Djebbari, Andrea Galeotti, Sanjeev Goyal, James Moody, Betsy Sinclair, Bruno Strulovici, and Adrien Vigier, as well as numerous seminar participants. -
Network Science, Homophily and Who Reviews Who in the Linux Kernel? Working Paper[+] Open-Access at ECIS2020-Arxiv.Pdf
Network Science, Homophily and Who Reviews Who in the Linux Kernel? Working paper[P] Open-access at http://users.abo.fi/jteixeir/pub/linuxsna/ ECIS2020-arxiv.pdf José Apolinário Teixeira Åbo Akademi University Finland # jteixeira@abo. Ville Leppänen University of Turku Finland Sami Hyrynsalmi arXiv:2106.09329v1 [cs.SE] 17 Jun 2021 LUT University Finland [P]As presented at 2020 European Conference on Information Systems (ECIS 2020), held Online, June 15-17, 2020. The ocial conference proceedings are available at the AIS eLibrary (https://aisel.aisnet.org/ecis2020_rp/). Page ii of 24 ? Copyright notice ? The copyright is held by the authors. The same article is available at the AIS Electronic Library (AISeL) with permission from the authors (see https://aisel.aisnet.org/ ecis2020_rp/). The Association for Information Systems (AIS) can publish and repro- duce this article as part of the Proceedings of the European Conference on Information Systems (ECIS 2020). ? Archiving information ? The article was self-archived by the rst author at its own personal website http:// users.abo.fi/jteixeir/pub/linuxsna/ECIS2020-arxiv.pdf dur- ing June 2021 after the work was presented at the 28th European Conference on Informa- tion Systems (ECIS 2020). Page iii of 24 ð Funding and Acknowledgements ð The rst author’s eorts were partially nanced by Liikesivistysrahasto - the Finnish Foundation for Economic Education, the Academy of Finland via the DiWIL project (see http://abo./diwil) project. A research companion website at http://users.abo./jteixeir/ECIS2020cw supports the paper with additional methodological details, additional data visualizations (plots, tables, and networks), as well as high-resolution versions of the gures embedded in the paper.