The document discusses connective knowledge and learning networks. It makes three main points:
1. Knowledge is distributed, emerging from connections in networks rather than represented in individual minds. Concepts exist across interconnected units rather than in single locations.
2. Learning involves connecting specialized information sources. The ability to see connections between different fields and ideas is important. Nurturing connections facilitates continual learning.
3. Effective networks are decentralized, distributed, and disintermediated. Content and services are disaggregated and dynamic. The network encourages participation and emergence of patterns rather than top-down control.
A Perspective on Graph Theory and Network ScienceMarko Rodriguez
The graph/network domain has been driven by the creativity of numerous individuals from disparate areas of the academic and the commercial sector. Examples of contributing academic disciplines include mathematics, physics, sociology, and computer science. Given the interdisciplinary nature of the domain, it is difficult for any single individual to objectively realize and speak about the space as a whole. Any presentation of the ideas is ultimately biased by the formal training and expertise of the individual. For this reason, I will simply present on the domain from my perspective---from my personal experiences. More specifically, from my perspective biased by cognitive and computer science.
This is an autobiographical lecture on my life (so far) with graphs/networks.
A Perspective on Graph Theory and Network ScienceMarko Rodriguez
The graph/network domain has been driven by the creativity of numerous individuals from disparate areas of the academic and the commercial sector. Examples of contributing academic disciplines include mathematics, physics, sociology, and computer science. Given the interdisciplinary nature of the domain, it is difficult for any single individual to objectively realize and speak about the space as a whole. Any presentation of the ideas is ultimately biased by the formal training and expertise of the individual. For this reason, I will simply present on the domain from my perspective---from my personal experiences. More specifically, from my perspective biased by cognitive and computer science.
This is an autobiographical lecture on my life (so far) with graphs/networks.
An interactive presentation on social network theory and analysis. Content includes information on tie formation and social capital. Network relations are explained by using the example of The A Team. Granovetter's Strength of Weak Ties Theory (1973) is also covered and weak ties and strong ties are explained. Appropriate application of social network theory to individuals understanding how to best take advantage of social networking platforms to find jobs as well as companies taking advantage of social media platforms to find followers are introduced.
Is network theory the best hope for regulating systemic risk?Kimmo Soramaki
The presentation is organised around three policy questions:
1. How can we measure the systemic importance of a bank?
2. Can regulators promote a safer financial system by affecting its topology?
3. Is it possible to devise early-warning indicators from real-time data?
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
Structured data on the Web frequently referred to as knowledge graphs consists of large number of datasets representing diverse domains. Widely used commercial applications such as entity recommendation, search, question answering and knowledge discovery use these knowledge graphs as their knowledge source. Majority of these applications have a particular domain of interest, hence require only the segment of the Web of data representing that domain (e.g., movie, biomedical, sports). In fact, leveraging the entire Web of data for a domain-specific application is not only computationally intensive, but also the irrelevant portion negatively impact the accuracy of the application. Hence, finding the relevant portion of the Web of data for domain-specific applications has become a paramount issue. Identifying the relevant portion of the Web of data consists of two sub-tasks; 1) find the relevant datasets that contain knowledge on the domain of interest, and 2) extract the subgraph representing domain of interest from the knowledge graphs that represent multiple domains (e.g., DBpedia, YAGO, Freebase). In this talk, I will discuss both data-driven and knowledge-driven approaches to solve these two sub-tasks. The domain-specific subgraphs extracted by our approach were 80% less in size in terms of the number of paths compared to original KG and resulted in more than tenfold reduction of required computational time for domain-specific tasks, yet produced better accuracy on domain-specific applications. We believe that this work can significantly contribute for utilizing knowledge graphs for domain-specific applications, specially with the explosive growth in the creation of knowledge graphs.
1° Premio nella categoria “Web Branding” del Grand Prix Brand Identity 2008 p... eFlux Crossmedia Group
Un riconoscimento che premia la professionalità e il lavoro di gruppo tra il marketing di Snaidero, Brain Bank che ha curato la creatività e Wavenet che ha realizzato l'interattività del nuovo sito web www.snaidero.it.
An interactive presentation on social network theory and analysis. Content includes information on tie formation and social capital. Network relations are explained by using the example of The A Team. Granovetter's Strength of Weak Ties Theory (1973) is also covered and weak ties and strong ties are explained. Appropriate application of social network theory to individuals understanding how to best take advantage of social networking platforms to find jobs as well as companies taking advantage of social media platforms to find followers are introduced.
Is network theory the best hope for regulating systemic risk?Kimmo Soramaki
The presentation is organised around three policy questions:
1. How can we measure the systemic importance of a bank?
2. Can regulators promote a safer financial system by affecting its topology?
3. Is it possible to devise early-warning indicators from real-time data?
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
Structured data on the Web frequently referred to as knowledge graphs consists of large number of datasets representing diverse domains. Widely used commercial applications such as entity recommendation, search, question answering and knowledge discovery use these knowledge graphs as their knowledge source. Majority of these applications have a particular domain of interest, hence require only the segment of the Web of data representing that domain (e.g., movie, biomedical, sports). In fact, leveraging the entire Web of data for a domain-specific application is not only computationally intensive, but also the irrelevant portion negatively impact the accuracy of the application. Hence, finding the relevant portion of the Web of data for domain-specific applications has become a paramount issue. Identifying the relevant portion of the Web of data consists of two sub-tasks; 1) find the relevant datasets that contain knowledge on the domain of interest, and 2) extract the subgraph representing domain of interest from the knowledge graphs that represent multiple domains (e.g., DBpedia, YAGO, Freebase). In this talk, I will discuss both data-driven and knowledge-driven approaches to solve these two sub-tasks. The domain-specific subgraphs extracted by our approach were 80% less in size in terms of the number of paths compared to original KG and resulted in more than tenfold reduction of required computational time for domain-specific tasks, yet produced better accuracy on domain-specific applications. We believe that this work can significantly contribute for utilizing knowledge graphs for domain-specific applications, specially with the explosive growth in the creation of knowledge graphs.
1° Premio nella categoria “Web Branding” del Grand Prix Brand Identity 2008 p... eFlux Crossmedia Group
Un riconoscimento che premia la professionalità e il lavoro di gruppo tra il marketing di Snaidero, Brain Bank che ha curato la creatività e Wavenet che ha realizzato l'interattività del nuovo sito web www.snaidero.it.
Os dejo una presentación muy reciente de Stephen Downes, como referencia hacia lo que debemos tender como formadores en el contexto del Proyecto Guadalinfo.
Slides to accompany Dr Louise Cooke's workshop session "An introduction to social network analysis" presented at DREaM Event 2.
For more information about the event, please visit http://lisresearch.org/dream-project/dream-event-2-workshop-tuesday-25-october-2011/
Open Grid Forum workshop on Social Networks, Semantic Grids and WebNoshir Contractor
Workshop organized by David De Roure at the Open Grid Forum XIX. Other participants included Carole Gobler, Jeremy Frey, Pamela Fox.
January 29, 2007, Chapel Hill, NC
Does artificial neural network support connectivism’s assumptions?Alaa Al Dahdouh
Connectivism was presented as a learning theory for the digital age and connectivists claim that recent developments in Artificial Intelligence (AI) and, more specifically, Artificial Neural Network (ANN) support their assumptions of knowledge connectivity. Yet, very little has been done to investigate this brave allegation. Does the advancement in artificial neural network studies support connectivism’s assumptions? And if yes, to what extent? This paper addresses the aforementioned question by tackling the core concepts of ANN and matching them with connectivist's assumptions. The study employed the qualitative content analysis approach where the researcher started with purposely selected and relatively small content samples in connectivism and ANN literature. The results revealed that ANN partially supports connectivism’s assumptions but this does not mean that other learning theories such as behaviorism and constructivism are not supported as well. The findings enlighten our understanding of connectivism and where it may be applied.
Presentación elaborada y compartida por George Siemens en su conferencia en Buenos Aires, invitado por Fundación Telefónica de Argentina, el 12 de septiembre de 2012.
DERI Stream Meeting 2010: What I'm working onjodischneider
A short presentation to re-introduce myself and my work to colleagues in DERI's Social Semantic Information Spaces stream. Besides my Ph.D. work on Social Semantic Argumentation, I talked about two W3C projects I participate in, Library Linked Data and HCLS's Scientific Discourse group. (Ontologies for Reading & Writing Science).
Our stream consists of two DERI units, Siegfried Handschuh's SmILE (Semantic Desktop) group and Alexandre Passant's USS (Social Software) group.
I was particularly proud of having only 1 slide overlap with the research talk I gave to the DERI institute meeting the previous week.
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![Cognitivism ,[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)