The Potential of Social Network Analysis in Intelligence
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Internet of Things (IoT): The Third Wave

Internet of Things (IoT): The Third Wave | The Potential of Social Network Analysis in Intelligence | Scoop.it
The Internet of Things (IoT) is the network of physical objects accessed through the Internet. These objects contain embedded technology to interact with internal states or the external environment. In other words, when objects can sense and communicate, it changes how and where decisions are made, and who makes them. For example Nest thermostats.

The Internet of Things (IoT) is emerging as the third wave in the development of the Internet. The 1990s’ Internet wave connected 1 billion users while the 2000s’ mobile wave connected another 2 billion. The IoT has the potential to connect 10X as many (28 billion) “things” to the Internet by 2020, ranging from bracelets to cars. Breakthroughs in the cost of sensors, processing power and bandwidth to connect devices are enabling ubiquitous connections right now. Smart products like smart watches and thermostats (Nest) are already gaining traction as stated in Goldman Sachs Global Investment Research’s report.

IoT has key attributes that distinguish it from the “regular” Internet, as captured by Goldman Sachs’s S-E-N-S-E framework: Sensing, Efficient, Networked, Specialized, Everywhere. These attributes may tilt the direction of technology development and adoption, with significant implications for Tech companies – much like the transition from the fixed to the mobile Internet shifted the center of gravity from Intel to Qualcomm or from Dell to Apple.

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IBM Builds a Brain-Inspired Chip Using Phase-Change Memory

IBM Builds a Brain-Inspired Chip Using Phase-Change Memory | The Potential of Social Network Analysis in Intelligence | Scoop.it
A new form of computer memory might help machines match the capabilities of the human brain when it comes to tasks such as interpreting images or video footage.

Researchers at IBM used what’s known as phase-change memory to build a device that processes data in a way inspired by the workings of a biological brain. Using a prototype phase-change memory chip, the researchers configured the system to act like a network of 913 neurons with 165,000 connections, or synapses, between them. The strength of those connections change as the chip processes incoming data, altering how the virtual neurons influence one another. By exploiting that property, the researchers got the system to learn to recognize handwritten numbers.

Phase-change memory is expected to hit the market in the next few years. It can write information more quickly, and pack it more densely, than the memory used in computers today (see “A Preview of Future Disk Drives”). A phase-change memory chip consists of a grid of “cells” that can each switch between two states to represent a digital bit of information—a 1 or a 0. In IBM’s experimental system, each “synapse” is represented by a pair of memory cells working together.

Computer scientists have been working for some time on chips that crudely mimic neurons and synapses. Such “neuromorphic” designs are radically different from the chips we use today. But they promise to make computers that are efficient at tasks computers normally find challenging, such as learning from experience or understanding video (see “Thinking in Silicon”).

Earlier this year, IBM announced the most complex neuromorphic chip yet (see “IBM Chip Processes Data Similar to the Way Your Brain Does”). It was made using the techniques and components used to build smartphone processors.

The experimental system announced by IBM researchers this week is much less powerful than that chip. But the fact the new system’s 165,000 synapses are made using phase-change memory is significant, says Geoff Burr, a researcher at IBM’s Almaden Research Center in San Jose, California.

Phase-change memory is thought to be particularly well suited to neuromorphic computer systems because it stores data so densely, making it possible to create brain-inspired systems with many more synapses, says Burr. Phase-change memory is also simpler to reprogram. That makes it practical for building a neuromorphic system that is able to “learn” by adjusting its behavior as it is fed new data.

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Want to influence the world? Map reveals the best languages to speak

Want to influence the world? Map reveals the best languages to speak | The Potential of Social Network Analysis in Intelligence | Scoop.it

Speak or write in English, and the world will hear you. Speak or write in Tamil or Portuguese, and you may have a harder time getting your message out. Now, a new method for mapping how information flows around the globe identifies the best languages to spread your ideas far and wide. One hint: If you’re considering a second language, try Spanish instead of Chinese.

 

The study was spurred by a conversation about an untranslated book, says Shahar Ronen, a Microsoft program manager whose Massachusetts Institute of Technology (MIT) master’s thesis formed the basis of the new work. A bilingual Hebrew-English speaker from Israel, he told his MIT adviser, César Hidalgo (himself a Spanish-English speaker), about a book written in Hebrew whose translation into English he wasn’t yet aware of. “I was able to bridge a certain culture gap because I was multilingual,” Ronen says. He began thinking about how to create worldwide maps of how multilingual people transmit information and ideas.

 

Ronen and co-authors from MIT, Harvard University, Northeastern University, and Aix-Marseille University tackled the problem by describing three global language networks based on bilingual tweeters, book translations, and multilingual Wikipedia edits. The book translation network maps how many books are translated into other languages. For example, the Hebrew book, translated from Hebrew into English and German, would be represented in lines pointing from a node of Hebrew to nodes of English and German. That network is based on 2.2 million translations of printed books published in more than 1000 languages. As in all of the networks, the thickness of the lines represents the number of connections between nodes. For tweets, the researchers used 550 million tweets by 17 million users in 73 languages. In that network, if a user tweets in, say, Hindi as well as in English, the two languages are connected. To build the Wikipedia network, the researchers tracked edits in up to five languages done by editors, carefully excluding bots.


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Flavour network and the principles of food pairing : Data Science in Gastronomy

Flavour network and the principles of food pairing : Data Science in Gastronomy | The Potential of Social Network Analysis in Intelligence | Scoop.it
The cultural diversity of culinary practice, as illustrated by the variety of regional cuisines, raises the question of whether there are any general patterns that determine the ingredient combinations used in food today or principles that transcend individual tastes and recipes. We introduce a flavor network that captures the flavor compounds shared by culinary ingredients. Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis. By contrast, East Asian cuisines tend to avoid compound sharing ingredients. Given the increasing availability of information on food preparation, our data-driven investigation opens new avenues towards a systematic understanding of culinary practice.

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Local Nash Equilibrium in Social Networks

Local Nash Equilibrium in Social Networks | The Potential of Social Network Analysis in Intelligence | Scoop.it
Nash equilibrium is widely present in various social disputes. As of now, in structured static populations, such as social networks, regular, and random graphs, the discussions on Nash equilibrium are quite limited. In a relatively stable static gaming network, a rational individual has to comprehensively consider all his/her opponents' strategies before they adopt a unified strategy. In this scenario, a new strategy equilibrium emerges in the system. We define this equilibrium as a local Nash equilibrium. In this paper, we present an explicit definition of the local Nash equilibrium for the two-strategy games in structured populations. Based on the definition, we investigate the condition that a system reaches the evolutionary stable state when the individuals play the Prisoner's dilemma and snow-drift game. The local Nash equilibrium provides a way to judge whether a gaming structured population reaches the evolutionary stable state on one hand. On the other hand, it can be used to predict whether cooperators can survive in a system long before the system reaches its evolutionary stable state for the Prisoner's dilemma game. Our work therefore provides a theoretical framework for understanding the evolutionary stable state in the gaming populations with static structures.

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Shock waves on complex networks : Scientific Reports : Nature Publishing Group

Shock waves on complex networks : Scientific Reports : Nature Publishing Group | The Potential of Social Network Analysis in Intelligence | Scoop.it
Power grids, road maps, and river streams are examples of infrastructural networks which are highly vulnerable to external perturbations. An abrupt local change of load (voltage, traffic density, or water level) might propagate in a cascading way and affect a significant fraction of the network. Almost discontinuous perturbations can be modeled by shock waves which can eventually interfere constructively and endanger the normal functionality of the infrastructure. We study their dynamics by solving the Burgers equation under random perturbations on several real and artificial directed graphs. Even for graphs with a narrow distribution of node properties (e.g., degree or betweenness), a steady state is reached exhibiting a heterogeneous load distribution, having a difference of one order of magnitude between the highest and average loads. Unexpectedly we find for the European power grid and for finite Watts-Strogatz networks a broad pronounced bimodal distribution for the loads. To identify the most vulnerable nodes, we introduce the concept of node-basin size, a purely topological property which we show to be strongly correlated to the average load of a node.

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Eli Levine's curator insight, May 20, 2014 8:19 AM

Indeed, this is intuitive enough without the mathematics to back it up.  This could be mapped out and used for prioritizing the defense or attack of various points within the network, either in the digital or analog worlds.

 

Way cool science!

 

Think about it.

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#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | The Potential of Social Network Analysis in Intelligence | Scoop.it

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luiy's curator insight, March 27, 2014 1:44 PM

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

António F Fonseca's curator insight, April 2, 2014 6:01 AM

Another paper about popularity prediction.

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Data Science for IoT: The role of hardware in analytics

Data Science for IoT: The role of hardware in analytics | The Potential of Social Network Analysis in Intelligence | Scoop.it
Often, Data Science for IoT differs from conventional data science due to the presence of hardware.
Hardware could be involved in integration with the Cloud or Processing at the Edge (which Cisco and others have called Fog Computing).
Alternately, we see entirely new classes of hardware specifically involved in Data Science for IoT(such as synapse chip for Deep learning)
Hardware will increasingly play an important role in Data Science for IoT.
A good example is from a company called Cognimem which natively implements classifiers(unfortunately, the company does not seem to be active any more as per their twitter feed)
In IoT, speed and real time response play a key role. Often it makes sense to process the data closer to the sensor.

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The Dominant Life Form in the Cosmos Is Probably Superintelligent Robots

The Dominant Life Form in the Cosmos Is Probably Superintelligent Robots | The Potential of Social Network Analysis in Intelligence | Scoop.it
If and when we finally encounter aliens, they probably won’t look like little green men, or spiny insectoids. It’s likely they won’t be biological creatures at all, but rather, advanced robots that outstrip our intelligence in every conceivable way. While scores of philosophers, scientists and futurists have prophesied the rise of artificial intelligence and the impending singularity, most have restricted their predictions to Earth. Fewer thinkers—outside the realm of science fiction, that is—have considered the notion that artificial intelligence is already out there, and has been for eons.

Susan Schneider, a professor of philosophy at the University of Connecticut, is one who has. She joins a handful of astronomers, including Seth Shostak, director of NASA’s Search for Extraterrestrial Intelligence, or SETI, program, NASA Astrobiologist Paul Davies, and Library of Congress Chair in Astrobiology Stephen Dick in espousing the view that the dominant intelligence in the cosmos is probably artificial. In her paper “Alien Minds," written for a forthcoming NASA publication, Schneider describes why alien life forms are likely to be synthetic, and how such creatures might think.

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Worm 'Brain' Uploaded Into Lego Robot

Worm 'Brain' Uploaded Into Lego Robot | The Potential of Social Network Analysis in Intelligence | Scoop.it
Can a digitally simulated brain on a computer perform tasks just like the real thing?

For simple commands, the answer, it would seem, is yes it can. Researchers at the OpenWorm project recently hooked a simulated worm brain to a wheeled robot. Without being explicitly programmed to do so, the robot moved back and forth and avoided objects—driven only by the interplay of external stimuli and digital neurons.

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Socilab - LinkedIn Social Network Visualization, Analysis, and Education | #SNA #influence

Socilab - LinkedIn Social Network Visualization, Analysis, and Education | #SNA #influence | The Potential of Social Network Analysis in Intelligence | Scoop.it

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luiy's curator insight, December 23, 2014 4:40 AM

Socilab is a free tool that allows users to visualize, analyze, and download data on their LinkedIn network. It works with the LinkedIn API to a) calculate structural hole metrics such as network density, hierarchy and constraint - and displays your percentile compared to other users of the tool, b) display a dynamic/interactive visualization of your ego network with node coloring by industry and an option to enable/disable connections to self using D3.js, and c) produce a CSV adjacency matrix or Pajek edgelist for download and import into your favorite SNA package. Users might find it useful for class tutorials and/or quickly and cheaply fielding crude network surveys. Former users of the now deprecated LinkedIn inMaps may find this to be a useful alternative.

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Viral Misinformation: The Role of Homophily and Polarization

Viral Misinformation: The Role of Homophily and Polarization | The Potential of Social Network Analysis in Intelligence | Scoop.it

The spreading of unsubstantiated rumors on online social networks (OSN) either unintentionally or intentionally (e.g., for political reasons or even trolling) can have serious consequences such as in the recent case of rumors about Ebola causing disruption to health-care workers. Here we show that indicators aimed at quantifying information consumption patterns might provide important insights about the virality of false claims. In particular, we address the driving forces behind the popularity of contents by analyzing a sample of 1.2M Facebook Italian users consuming different (and opposite) types of information (science and conspiracy news). We show that users' engagement across different contents correlates with the number of friends having similar consumption patterns (homophily), indicating the area in the social network where certain types of contents are more likely to spread. Then, we test diffusion patterns on an external sample of 4,709 intentional satirical false claims showing that neither the presence of hubs (structural properties) nor the most active users (influencers) are prevalent in viral phenomena. Instead, we found out that in an environment where misinformation is pervasive, users' aggregation around shared beliefs may make the usual exposure to conspiracy stories (polarization) a determinant for the virality of false information.

 

Viral Misinformation: The Role of Homophily and Polarization
Aris Anagnostopoulos, Alessandro Bessi, Guido Caldarelli, Michela Del Vicario, Fabio Petroni, Antonio Scala, Fabiana Zollo, Walter Quattrociocchi

http://arxiv.org/abs/1411.2893


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The Potential of Social Network Analysis in Intelligence

The Potential of Social Network Analysis in Intelligence | The Potential of Social Network Analysis in Intelligence | Scoop.it
Within its limits, SNA can be applied to identify individuals or organizations within a network, generate new leads and simulate flows of information or money throughout a network.

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Marc Tirel's curator insight, January 12, 2014 8:44 AM

huge field for research ...

Catherine Pascal's curator insight, February 25, 2014 8:26 AM

 Très intéressant