I will speak about the value of a network perspective for the discovery of fraud and corruption in financial data at the December 9th session of the World Bank’s upcoming meeting of the Stolen Asset Recovery Initiative.
“The World Bank Group’s International Corruption Hunters Alliance (ICHA) brings together heads and senior officials of corruption investigating bodies and prosecuting authorities, anti-corruption experts, academics, and representatives of international organizations from over 130 countries. The 2014 meeting of the Alliance will focus on fighting corruption – and the vast illicit outflows generated by corruption – by sharing know-how and experiences in the use of both traditional and alternative corruption fighting approaches.”
All financial transactions create a network as one person transfers money from one account to another. A list of transactions creates a web of connections with an emergent shape or pattern. Within these patterns are key positions occupied by people with special power in the network. Mapping these transaction networks can reveal the hidden traces of financial crime.
I am delighted to return to South Africa where I will participate in the Mammoth BI conference in Cape Town, on November 17-18, 2014 at the Cape Town International Conference Centre, Convention Square, 1 Lower Long Street, Cape Town, 8001, Western Cape, South Africa.
The theme of the event is “How to Feed Consumers with a #Digital @ppetite”
I will speak about the ways that restaurants and dining experiences are discussed in social media. I will show network maps that visualize the relationships among people who talk about restaurants created with the free and open NodeXL social media network analysis and visualization application.
Here are some recent NodeXL social media network maps for mentions of major chain restaurants featured in the NodeXL Graph Gallery: DunkinDonuts
These maps illustrate the shape of the crowd that gathers around the names of major chain restaurants. A few Twitter user accounts occupy key positions in these network crowds, these are the influential voices that are repeated widely by others.
Closer inspection (click through for details) reveals smaller groups or clusters which form as a smaller set of people interact with one another more than with the larger population. These groups have distinct topics of interest which are summarized in the content report associated with each visualization.
The network and content report can reveal the topics of interest to various groups in the discussion as well as the key people within each group.
People talk about the products and services the use, love or hate all the time in social media. These conversations can be better understood through perspective of social network analysis. Network theory views the world as a web of connected people. Network analysis builds measures and visualizations of collections of connections to reveal the key people, groups and issues in these conversations. Using social media network maps and reports the confusing landscape of tweets and posts comes into focus. Information visualizations of the virtual crowds of people gathered around every brand, product, event, or service highlights the range of variation in the shape of these crowds. Six different patterns have been identified so far, allowing social media managers to recognize the nature of the brand network they have and the nature of the network they want to have. Network measures are useful as KPIs for tracking not just the size and volume of a social media stream, but also the shape and structure of the pattern of connections. The six patterns: divided, unified, fragmented, clustered, and in and out hub and spoke, are a useful guide to strategic engagement in social media.
lennyism OR insightnovation OR #IIeX Twitter NodeXL SNA Map and Report for Monday, 09 June 2014
The graph represents a network of 611 Twitter users whose tweets in the requested date range contained “lennyism OR insightnovation OR #IIeX”, or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 09 June 2014 at 00:24 UTC.
The requested date range was from Tuesday, 01 April 2014 at 00:00 UTC through Sunday, 08 June 2014 at 23:59 UTC.
The tweets in the network were tweeted over the 67-day, 4-hour, 35-minute period from Tuesday, 01 April 2014 at 00:26 UTC to Saturday, 07 June 2014 at 05:01 UTC.
There is an edge for each “replies-to” relationship in a tweet, an edge for each “mentions” relationship in a tweet, and a self-loop edge for each tweet that is not a “replies-to” or “mentions”.
The graph is directed.
The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values.
Top 10 Vertices, Ranked by Betweenness Centrality:
This will be the 2nd Networks in the Global World conference (for information on the 1st one held in 2012 see http://www.ngw.spbu.ru). This conference series brings together researchers exploring cutting edge networks research from around the globe. In 2014 the focus is on linking theory and methods that integrate information, communication, semantic and cultural networks.
The primarygoal of the NetGloW conferenceseriesis to bring together networks researchers from around the globe, to unite the efforts of various scientific disciplines in response to the key challenges faced by network studies today, and to exchange local research results – thus allowing an analysis of global processes. It is also crucial for us to support junior researchers’ orientation in the complex landscape of network science, and to encourage applications of network analysis by practitioners.
The idea of the 2014 event is to discuss the key current issues and problems of linking theoretical and methodological developments in network analysis.
The structure of the conference: June 27th – workshops and seminar for practitioners; June 28th & 29th – presentations.
Confirmed invited speakers:
Jana Diesner, University of Illinois at Urbana-Champaign, USA
Loet Leydesdorff, University of Amsterdam, Netherlands
Tom Valente, University of Southern California, USA
Dimitris Christopoulos, MODUL University Vienna, Austria
Mario Diani, University of Trento, Italy
Peter Groenewegen, VU University Amsterdam, Netherlands
Wouter de Nooy, University of Amsterdam, Netherlands
Johanne Saint-Charles, University of Quebec at Montreal, Canada
Marc Smith, Connected Action Consulting Group, USA
The sessions of the conference will include the following:
Networks in Science, Technology, and Innovation: Traditional Approaches and Operationalization of New Theories
Words and Networks
Network Perspectives on Knowledge, Communication and Culture
Making Sense of Big Network Data: Testing Hypotheses on New Data
Social Movements and Collective Action as the Network Phenomena
Network Analysis of Political and Policy-making Domains
The conference will host workshops on the following network analysis software: ConText, Pajek, NodeXL, UCINet, RSiena.
The conference will includeseminar Network analysis: How can it be used by globally operating practitioners?
Host: Center for German and European Studies (St. Petersburg State University – Bielefeld University).
Working language: English.
Abstracts (200 words) will be published as a part of conference programme. Full papers (2500 words) should be submitted prior to the event. Selected full papers will be published in peer-reviewed journal(s).
Freeaccommodation will be provided for MA and PhD students who submitted the best abstracts.
Fee:Participation in the conference is free of charge.
NodeXL has new updates to its importers for Twitter users and lists.
We have released an updated version of NodeXL that simplifies and merges the previously separate User and List importers.
The new, streamlined importer treats an individual user as a list of one.
Lists can be defined by pointing to an existing Twitter List or simply entering a list of delimited user names into the text box.
The updated importer now collects many more tweets per person and parses these messages to generate reply and mention edges.
You can now define a group of Twitter users and find out how much they reply and mention one another.
You can even pull in the followers of each person, to see if they reply or mention people they also follow.
But ever since June 11, 2013, Twitter has made access to the “follows” edge data very difficult (its just very slow). Designed and implemented prior to the update that restricted access to the follower network, the original NodeXL Twitter list importers relied mostly on queries that are now impractically slow for all but the smallest lists of users who have small collections of followers.
The update to these User and List importer is partially an adaptation to these changes. The importer shifts away from the follower network to focus on the communication interaction data in the content of Tweets. Since Twitter offers more generous access to Tweets than to information about who follows who, we are obliged to make better use of what they do offer.
The Social Media Research Foundation team has innovated at multiple levels: organizationally we are a modern, virtual, distributed group of collaborators. Technically, we have focused our project on ease of use and automation rather than scale and sophistication, our users are not programmers. We have implemented many innovative network analysis and visualization techniques because we have been driven by a need to serve a diverse user population. The contributors to the project are themselves from a diverse range of disciplinary backgrounds, making it easier to shape the tool for the broadest audience.
Imported Twitter networks now have an “in-reply-to tweet ID” column. This is a useful data element for building “paths” that capture how information flows through a network.
When you lay out each of the graph’s groups in its own box, you can now select how the boxes are laid out. Go to NodeXL>Graph>Layout>Layout Options in the Excel ribbon. (Thanks to Cody Dunne for this feature.)
The Check for Updates item has been removed from the Excel ribbon. NodeXL now automatically checks for updates once a day. Once this release is installed, NodeXL will automatically update itself when a new release is available. You will no longer have to manually download and install new releases. This release and those that follow will all be referred to as “NodeXL Excel Template 2014.” New releases will continue to have version numbers, but the numbers will be less important in light of the new auto-update feature.
If you use third-party graph data importers, such as the Social Network Importer for NodeXL, note that the folder where the importers are stored must be specified in the NodeXL>Data>Import>Import Options dialog:
If you use the NodeXL Network Server, an advanced command-line program that downloads a network from Twitter and stores the network on disk in several file formats, note that the program is no longer a part of NodeXL Excel Template. See “Using the NodeXL Network Server command-line program with NodeXL Excel Template 2014” at http://nodexl.codeplex.com/discussions/522830.
When a Twitter network is imported, the hashtags in the “Hashtags in Tweet” (or “Hashtags in Latest Tweet”) column are now all in lower case. Previously, identical strings with different case letters would be counted differently. This is no longer the case and the result is that terms that had been divided are now unified. These terms will now have higher values and there will be more diversity in the top ten list.
Thanks for using NodeXL and stay tuned for additional updates!
Lee Rainie, director of the Pew Internet Research Center was interviewed by Bob Garfield on OnTheMedia this week about the recently released report on mapping Twitter topic networks. The report found six distinct patterns of social media networks in Twitter: divided, unified, fragmented, clustered, and in and out hub and spoke patterns. They discuss the prospects for overcoming polarization in social media and the hopeful signs that many other forms of social network structures exist in addition to the divided network pattern.