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Monday, October 26, 2009
Network Science discovered by looking for polytechnic salaries!
http://www.rpi.edu/
http://www.rpi.edu/about/inside/issue/v3n11/cognitive.html
Boleslaw Szymanski, center director and Claire & Roland Schmitt Distinguished Professor of Computer Science at Rensselaer
With $16.75 million in funding from the Army Research Laboratory (ARL), Rensselaer will launch a new interdisciplinary research center devoted to the study of social and cognitive networks.
The Center for Social and Cognitive Networks is part of the newly created Collaborative Technology Alliance (CTA) of the ARL, which includes a total of four nationwide centers focused on different aspects of the emerging field of network science.
Rensselaer will receive $8.6 million of the $16.75 million in total funding to lead the new center for its first five years. An additional $18.75 million is anticipated from the ARL for a second phase, which would bring the total funding for the interdisciplinary center to $35.5 million over 10 years.
Rensselaer will be joined by corporate and academic partners from IBM Corp., Northeastern University, and the City University of New York, and collaborators from Harvard University, Massachusetts Institute of Technology, New York University, Northwestern University, the University of Notre Dame, the University of Maryland, and Indiana University.
“Rensselaer offers a unique research environment to lead this important new network science center,” said President Shirley Ann Jackson. “We have assembled an outstanding team of researchers, and built powerful new research platforms. The team will work with one of the largest academic supercomputing centers in the world — the Rensselaer Computational Center for Nanotechnology Innovations — and the leading visualization and simulation capabilities within our new Experimental Media and Performing Arts Center. The Center for Social and Cognitive Networks will bring together our world-class scientists in the areas of computer science, cognitive science, physics, Web science, and mathematics in an unprecedented collaboration to investigate all aspects of the ever-changing and global social climate of today.”
“Together with other centers of the CTA, we are creating the new discipline of network science,” said Boleslaw Szymanski, the center director and Claire & Roland Schmitt Distinguished Professor of Computer Science at Rensselaer. “The centers will be in the leading position to define this new discipline in all its complexity. Rensselaer researchers are very pleased to be a leading part of this transformation.”
The Center for Social and Cognitive Networks will link together top social scientists, neuroscientists, and cognitive scientists with leading physicists, computer scientists, mathematicians, and engineers in the search to uncover, model, understand, and foresee the complex social interactions that take place in today’s society. All aspects of social networks, from the origins of adversarial networks to gauging the level of trust within vast social networks, will be investigated within the center.
The center will enable stronger and more closely integrated collaborations among the team of top interdisciplinary researchers in the emerging field of network science that already existed informally, according to Szymanski.
“I explored those earlier links and collaboration when organizing the team for the center,” he said. “The impact of our work will be far-reaching. We are in an entirely new world where Twitter, cell phones, and wireless communication change the way we interact with each other. Together and with the support of the ARL, the researchers in the center will be able to investigate how technology enhances social interactions and how those technologies and relationships can be used to better measure and understand people’s interactions with each other.”
Several Rensselaer faculty members will take part in the center research. Szymanski will be leading the interdisciplinary team that includes James Hendler, senior professor of the Tetherless World Research Constellation and head of information technology; Wayne Gray, professor of cognitive science and acting head of the School of Humanities, Arts, and Social Sciences; Sibel Adali, associate professor of computer science; Malik Magdon-Ismail, associate professor of computer science; Mark Goldberg, professor of computer science; Chjan Lim, professor of mathematical sciences; William Wallace, professor of decision sciences and engineering systems; Gyorgy Korniss, associate professor of physics, applied physics, and astronomy; and Michael Schoelles, research associate professor of cognitive science.
The center will study the fundamentals of social and cognitive networks and their roles in today’s society and organizations, including the U.S. Army. The goal will be to gain a deeper understanding of these networks and build a firm scientific basis in the field of network science. The work will include research on large social networks, with a focus on networks with mobile agents. An example of a mobile agent is someone who is interacting (e.g., communicating, observing, helping, distracting, interrupting, etc.) with others while moving around the environment. The U.S. Army and the societies within which it operates are primary examples of such networks, according to Szymanski.
The center’s research will focus on five topics:
To read more about the center’s research focus, go to http://news.rpi.edu/update.do?artcenterkey=2647
http://en.wikipedia.org/wiki/Network_science
Network science
From Wikipedia, the free encyclopedia
Network science is a new and emerging scientific discipline that examines the interconnections among diverse physical or engineered networks, information networks, biological networks, cognitive and semantic networks, and social networks. This field of science seeks to discover common principles, algorithms and tools that govern network behavior. The National Research Council defines Network Science as "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena."
1 Background and history
2 Department of Defense Initiatives
3 See also
4 References
5 Further reading
6 External links
In the 1930s Jacob Moreno, a psychologist in the Gestalt tradition, arrived in the United States. He developed the sociogram and presented it to the public in April 1933 at a convention of medical scholars. Moreno claimed that "before the advent of sociometry no one knew what the interpersonal structure of a group 'precisely' looked like (Moreno, 1953). The sociogram was a representation of the social structure of a group of elementary school students. The boys were friends of boys and the girls were friends of girls with the exception of one boy who said he liked a single girl. The feeling was not reciprocated. This network representation of social structure was found so intriguing that it was printed in the The New York Times(April 3, 1933, page 17). The sociogram has found many applications and has grown into the field of social network analysis.
Probabilistic theory in network science developed as an off-shoot of graph theory with Paul Erdős and Alfréd Rényi's eight famous papers on random graphs. For social networks the exponential random graph model or p* graph is a notational framework used to represent the probability space of a tie occurring in a social network. An alternate approach to network probability structures is the network probability matrix, which models the probability of edges occurring in a network, based on the historic presence or absence of the edge in a sample of networks.
In the 1998, David Krackhardt and Kathleen Caley introduced the idea of a meta-network with the PCANS Model. They suggest that "all organizations are structured along these three domains, Individuals, Tasks, and Resources. Their paper introduced the concept that networks occur across multiple domains and that they are interrelated. This field has grown into another sub-discipline of network science called dynamic network analysis
More recently other network science efforts have focused on mathematically describing different network topologies. Duncan Watts reconciled empirical data on networks with mathematical representation, describing the small-world network. Albert-László Barbási and Reka Albert developed the scale-free network which is a loosely defined network topology that contains hub vertices with many connections, that grow in a way to maintain a constant ratio in the number of the connections versus all other nodes. Although many networks, such as the internet, appear to maintain this aspect, other networks have long tailed distributions of nodes that only approximate scale free ratios.
Today, network science is an exciting and growing field. Scientists from many diverse fields are working together. Network science holds the promise of increasing collaboration across disciplines, by sharing data, algorithms, and software tools.
[edit] Department of Defense Initiatives
The U.S. military first became interested in network-centric warfare as an operational concept based on network science in 1996. John A. Parmentola, the U.S. Army Director for Research and Laboratory Management, proposed to the Army’s Board on Science and Technology (BAST) on December 1st, 2003 that Network Science become a new Army research area. The BAST, the Division on Engineering and Physical Sciences for the National Research Council (NRC) of the National Academies, serves as a convening authority for the discussion of science and technology issues of importance to the Army and oversees independent Army-related studies conducted by the National Academies. The BAST conducted a study to find out whether identifying and funding a new field of investigation in basic research, Network Science, could help close the gap between what is needed to realize Network-Centric Operations and the current primitive state of fundamental knowledge of networks.
As a result, the BAST issued the NRC study in 2005 titled Network Science (referenced above) that defined a new field of basic research in Network Science for the Army. Based on the findings and recommendations of that study and the subsequent 2007 NRC report titled Strategy for an Army Center for Network Science, Technology, and Experimentation, Army basic research resources were redirected to initiate a new basic research program in Network Science. To build a new theoretical foundation for complex networks, some of the key Network Science research efforts now ongoing in Army laboratories address:
• Mathematical models of network behavior to predict performance with network size, complexity, and environment
• Optimized human performance required for network-enabled warfare
• Networking within ecosystems and at the molecular level in cells.
As initiated in 2004 by Frederick I. Moxley with support provided by David S. Alberts, the Department of Defense helped to establish the first Network Science Center in conjunction with the U.S. Army at the United States Military Academy. Subsequently, the U.S. Department of Defense has funded numerous research projects in the area of Network Science.
Additionally in 2006, the U.S. Army and the United Kingdom (UK) formed the Network and Information Science International Technology Alliance, a collaborative partnership among the Army Research Laboratory, UK Ministry of Defense and a consortium of industries and universities in the U.S. and UK. The goal of the alliance is to perform basic research in support of Network- Centric Operations across the needs of both nations.
In addition, the Army is in the process of establishing a Network Science and Technology Research Center (NSTRC). The NSTRC will conduct research across the technical areas of information networks, social/cognitive networks, communication networks, and integration research and experiments which will bring the three other technical areas together as a single entity. The NSTRC will conduct these activities in partnership with other Department of Defense and government agencies, industry and academia to find solutions to the hard problems associated with developing adaptable and scalable mobile ad-hoc networks for the Army.
[edit] See also
Network theory
Systems Theory
Irregular Warfare
[edit] References
[edit] Further reading
"Understanding Network Science," http://www.zangani.com/blog/2007-1030-networkingscience
Linked: The New Science of Networks, A.-L. Barabási (Perseus Publishing, Cambridge (2002).
Network Science, Committee on Network Science for Future Army Applications, National Research Council. 2005. The National Academies Press (2005)ISBN 0-309-10026-7
Network Science Bulletin, USMA (2007) ISBN 978-1-934808-00-9
The Structure and Dynamics of Networks Mark Newman, Albert-László Barabási, & Duncan J. Watts (The Princeton Press, 2006) ISBN 0-691-11357-2
http://www.amazon.com/s/ref=nb_ss?url=search-alias%3Daps&field-keywords=0-691-11357-2+&x=7&y=14
http://www.amazon.com/Structure-Dynamics-Networks-Princeton-Complexity/product-reviews/0691113572/ref=sr_1_1_cm_cr_acr_img?ie=UTF8&showViewpoints=1
I was disappointed. The authors are leading scientists in the field, and I therefore expected a coherent exposition of the subject based on their combined knowledge and experience. Instead, the book is only a collection of reprints with some short paragraphs to use as linkage. It is not much more than a list of "best" papers in the field.
12 of 13 people found the following review helpful:
By Zabdorff - See all my reviews
Criticism of this book thus far stems from the following:
1) It is a collection of papers.
2) Some of those papers are fairly technical and may be a little daunting for beginners.
While both of these criticisms are accurate, they don't stop the book from being an excellent resource.
While it is a collection of papers, it is a very well-chosen one which includes some of the most important and influential papers in the field, and covers a wide array of subjects within it. Furthermore, each section has an intro written by the authors, which summarizes the works in the section in a fairly nontechnical manner.
As for (2), this book was my introduction to network analysis, and while I admit I was unable to read every paper, I was able to understand the main point of almost all of them. Caveat: I am a mathematician.
However, as I said, I was previously unfamiliar to the field--and my background gave me little advantage considering I didn't care to go through the equations in detail.
I would recommend this book to anyone with a mildly technical background (say, a few years of university-level science, engineering, or math under their belt) interested in learning about network theory.
External Links
http://www.netscience.usma.edu/NSW/Papers/Network_Science_Report_Vol1No1.pdf
http://press.princeton.edu/titles/8114.html
http://www.nap.edu/catalog.php?record_id=11516
http://www.ifr.ac.uk/netsci08/
http://www.netsci09.net/
Cyberinfrastructure for Network Science Center SLIS Indian University Bloomington
Retrieved from http://en.wikipedia.org/wiki/Network_science
Boleslaw Szymanski, center director and Claire & Roland Schmitt Distinguished Professor of Computer Science at Rensselaer
Rensselaer will be joined by corporate and academic partners from IBM Corp., Northeastern University, and the City University of New York, and collaborators from Harvard University, Massachusetts Institute of Technology, New York University, Northwestern University, the University of Notre Dame, the University of Maryland, and Indiana University.
The center will study the fundamentals of social and cognitive networks and their roles in today’s society and organizations, including the U.S. Army. The goal will be to gain a deeper understanding of these networks and build a firm scientific basis in the field of network science. The work will include research on large social networks, with a focus on networks with mobile agents. An example of a mobile agent is someone who is interacting (e.g., communicating, observing, helping, distracting, interrupting, etc.) with others while moving around the environment. The U.S. Army and the societies within which it operates are primary examples of such networks, according to Szymanski.
The center’s research will focus on five topics:
- Dynamic processes in networks, and the human interactions and the underlying technological infrastructure they utilize.
- Organizational networks, and the ways knowledge is spread from peer to peer in the modern military.
- The study of adversary networks, dealing with terrorists and other hidden groups within a society.
- Trust in social networks, seeking to measure the level of trust within a network and the impacts of trust in a network.
- The impacts of human error in social networks, using computational systems that predict how human error or bias will influence judgment.
“As the diversification of nations and societies progresses, understanding of social and cognitive networks and their impacts on people’s behavior and operation will become increasingly important,” Szymanski said. “These networks impact the Army in all aspects of its operations, from internal cohesiveness to their ability to perform complex missions in increasingly complex international social environments. Equally important is that these networks impact our society in a very similar way, as the complexity of social interactions grows and the influence of other societies on our lives increases.”
http://en.wikipedia.org/wiki/Network_science
From Wikipedia, the free encyclopedia
2 Department of Defense Initiatives
3 See also
4 References
5 Further reading
6 External links
The study of networks has emerged in diverse disciplines as a means of analyzing complex relational data. The earliest known paper in this field is the famous Seven Bridges of Königsberg written by Leonhard Euler in 1736. Euler's mathematical description of vertices and edges was the foundation of graph theory, a branch of mathematics that studies the properties of pairwise relations in a network structure. The field of graph theory continued to develop and found applications in chemistry (Sylvester, 1878).
More recently other network science efforts have focused on mathematically describing different network topologies. Duncan Watts reconciled empirical data on networks with mathematical representation, describing the small-world network. Albert-László Barbási and Reka Albert developed the scale-free network which is a loosely defined network topology that contains hub vertices with many connections, that grow in a way to maintain a constant ratio in the number of the connections versus all other nodes. Although many networks, such as the internet, appear to maintain this aspect, other networks have long tailed distributions of nodes that only approximate scale free ratios.
• Optimized human performance required for network-enabled warfare
• Networking within ecosystems and at the molecular level in cells.
Complex network
Collaborative innovation network
Dynamic Network Analysis Higher category theory
Polytely Systems Theory
Irregular Warfare
[edit] References
Linked: The New Science of Networks, A.-L. Barabási (Perseus Publishing, Cambridge (2002).
http://www.amazon.com/Structure-Dynamics-Networks-Princeton-Complexity/product-reviews/0691113572/ref=sr_1_1_cm_cr_acr_img?ie=UTF8&showViewpoints=1
I was disappointed. The authors are leading scientists in the field, and I therefore expected a coherent exposition of the subject based on their combined knowledge and experience. Instead, the book is only a collection of reprints with some short paragraphs to use as linkage. It is not much more than a list of "best" papers in the field.
Comprehensive literature review, November 20, 2008
2) Some of those papers are fairly technical and may be a little daunting for beginners.
While both of these criticisms are accurate, they don't stop the book from being an excellent resource.
While it is a collection of papers, it is a very well-chosen one which includes some of the most important and influential papers in the field, and covers a wide array of subjects within it. Furthermore, each section has an intro written by the authors, which summarizes the works in the section in a fairly nontechnical manner.
This book saved me from days of basic research and frustration in trying to locate full-text articles, and when I was done with it I felt that I had a fairly good working knowledge of the field. I imagine it will be fairly handy for future reference as well.
However, as I said, I was previously unfamiliar to the field--and my background gave me little advantage considering I didn't care to go through the equations in detail.
I would recommend this book to anyone with a mildly technical background (say, a few years of university-level science, engineering, or math under their belt) interested in learning about network theory.
http://www.netscience.usma.edu/NSW/Papers/Network_Science_Report_Vol1No1.pdf
http://press.princeton.edu/titles/8114.html
http://www.nap.edu/catalog.php?record_id=11516
http://www.ifr.ac.uk/netsci08/