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The Flavor Connection [Interactive]: Scientific American

The Flavor Connection [Interactive]: Scientific American | Social Network Analysis #sna | Scoop.it
Scientists link common flavor compounds across the world's favorite ingredients
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Julia Child famously said that fat carries flavor, but perhaps instead we should give thanks to 4-methylpentanoic acid. Unique combinations of such chemical compounds give foods their characteristic flavors. Science-minded chefs have gone so far as to suggest that seemingly incongruous ingredients—chocolate and blue cheese, for example—will taste great together as long as they have enough flavor compounds in common. Scientists recently put this hypothesis to the test by creating a flavor map, a variant of which we have reproduced here. Lines connect foods that have components in common; thick lines mean many components are shared. By comparing the flavor network with various recipe databases, the researchers conclude that chefs do tend to pair ingredients with shared flavor compounds—but only in Western cuisine. Dishes from a database of recipes from East Asia tend to combine ingredients with few overlapping flavors.

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Jay Ratcliff's curator insight, August 27, 2013 10:00 AM

Very good Network Analysis.  I can wait to read the source data at http://www.nature.com/srep/2011/111215/srep00196/full/srep00196.html

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Automatically Discover Website Connections Through Tracking Codes

Automatically Discover Website Connections Through Tracking Codes | Social Network Analysis #sna | Scoop.it
This article originally appeared on the AutomatingOSINT.com blog. Fellow Bellingcat contributor Lawrence Alexander did some really interesting OSINT work on analyzing the hidden links between websi...
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Trend of Narratives in the Age of Misinformation

Trend of Narratives in the Age of Misinformation | Social Network Analysis #sna | Scoop.it
 Social media enabled a direct path from producer to consumer of contents changing the way users get informed, debate, and shape their worldviews. Such a disintermediation might weaken consensus on social relevant issues in favor of rumors, mistrust, or conspiracy thinking—e.g., chem-trails inducing global warming, the link between vaccines and autism, or the New World Order conspiracy. Previous studies pointed out that consumers of conspiracy-like content are likely to aggregate in homophile
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Social Network Analysis of the Global Shapers Community — Medium

Social Network Analysis of the Global Shapers Community — Medium | Social Network Analysis #sna | Scoop.it
Structure of the World Economic Forum’s Global Shapers Community
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Contracting and simplifying a network graph

Contracting and simplifying a network graph | Social Network Analysis #sna | Scoop.it

#sna by Andrie de Vries In a previous post, I used page rank and community structure to create a plot of CRAN. This plot used vibrant colours to allow us to see some of the underlying structure of CRAN. However, much of this structure was still obfuscated by the amount of detail. Concretely, a large number of dots (packages) made it difficult to easily see the community structure. 

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Using Social network analysis measures

Using Social network analysis measures | Social Network Analysis #sna | Scoop.it
Using network visualisation and SNA measures, our intern dissects the connections in the Enron corpus to uncover management structures and play detective!
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Efficiently identifying critical nodes in large complex networks

The critical node detection problem (CNDP) aims to fragment a graph G=(V,E) by removing a set of vertices R with cardinality |R|≤k, such that the residual graph has minimum pairwise connectivity for user-defined value k. Existing optimization algorithms are incapable of finding a good set R in graphs with many thousands or millions of vertices due to the associated computational cost. Hence, there exists a need for a time- and space-efficient approach for evaluating the impact of removing any v∈V in the context of the CNDP. In this paper, we propose an algorithm based on a modified depth-first search that requires O(k(|V|+|E|)) time complexity. We employ the method within in a greedy algorithm for quickly identifying R. Our experimental results consider small- (≤250 nodes) and medium-sized (≤25,000 nodes) networks, where it is possible to compare to known optimal solutions or results obtained by other heuristics. Additionally, we show results using six real-world networks. The proposed algorithm can be easily extended to vertex- and edge-weighted variants of the CNDP.
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Computational Social Networks : Long-range degree correlations in complex networks

Social networks are often degree correlated between nearest neighbors, an effect termed homophily, wherein individuals connect to nearest neighbors of similar connectivity. Whether friendships or other associations are so correlated beyond the first-neighbors, and whether such correlations are an inherent property of the network or are dependent on other specifics of social interactions, remains unclear. 

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Here we address these problems by examining long-range degree correlations in three undirected online social and three undirected nonsocial (airport, transcriptional-regulatory) networks. Degree correlations were measured using Pearson correlation scores and by calculating the average neighbor degrees for nodes separated by up to 5 sequential links. We found that the online social networks exhibited primarily weak anticorrelation at the first-neighbor level, and tended more strongly towards disassortativity as separation distances increased. In contrast, the nonsocial networks were disassortative among first-neighbors, but exhibited assortativity at longer separation distances. In addition, the average degrees of the separated neighbors approached the average network connectivity after approximately 3-4 steps. Finally, we observed that two algorithms designed to grow networks on a node-by-node basis failed to reproduce all the correlative features representative of the social networks studied here.

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Ecology 2.0: Coexistence and domination among interacting networks

The overwhelming success of the web 2.0, with online social networks as key actors, has induced a paradigm shift in the nature of human interactions. 

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[Network analysis] Digital Humanities on Twitter, a small-world?

[Network analysis] Digital Humanities on Twitter, a small-world? | Social Network Analysis #sna | Scoop.it
Digital humanities, Data visualization, Network analysis
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Visualize Your Graph with RNeo4j and visNetwork #rstats #sna

Someone recently posted an issue on RNeo4j where they needed help visualizing their graph in visNetwork, which has turned out to be a pretty fun R package. I decided to turn my answer into a blog post.

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Network Under Joint Node and Link Attacks Vulnerability Assessment Methods and Analysis

“Network Under Joint Node and Link Attacks Vulnerability Assessment Methods and Analysis
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Analyzing Collaborative Governance Through Social Network Analysis: A Case Study of River Management Along the Waal River in The Netherlands #sna

Analyzing Collaborative Governance Through Social Network Analysis: A Case Study of River Management Along the Waal River in The Netherlands #sna | Social Network Analysis #sna | Scoop.it
Paperity: the 1st multidisciplinary aggregator of Open Access journals & papers. Free fulltext PDF articles from hundreds of disciplines, all in one place
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The Gremlin Graph Traversal Language

A presentation of Apache TinkerPop's Gremlin language with running examples over the MovieLens dataset. Presented August 19, 2015 at NoSQL NOW in San Jose, Cal…
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A survey of results on mobile phone datasets analysis

In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.

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New Laws Explain Why Fast-Growing Networks Break

New Laws Explain Why Fast-Growing Networks Break | Social Network Analysis #sna | Scoop.it
Researchers are uncovering the hidden laws that reveal how the Internet grows, how viruses spread, and how financial bubbles burst.
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Computational Social Networks: Handling big data of online social networks on a small machine

Dealing with big data in computational social networks may require powerful machines, big storage, and high bandwidth, which may seem beyond the capacity of small labs. We demonstrate that researchers with limited resources may still be able to conduct big-data research by focusing on a specific type of data. In particular, we present a system called MPT (Microblog Processing Toolkit) for handling big volume of microblog posts with commodity computers, which can handle tens of millions of micro posts a day. MPT supports fast search on multiple keywords and returns statistical results. We describe in this paper the architecture of MPT for data collection and phrase search for returning search results with statistical analysis. We then present different indexing mechanisms and compare them on the microblog posts we collected from popular online social network sites in mainland China.
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Computational Social Networks - a Springer Open journal

Computational Social Networks - a Springer Open journal | Social Network Analysis #sna | Scoop.it

mathematical aspects, and applications of social computing

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Focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media

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The Net Effect: Using social media data to understand the impact of a conference on social networks

The research uses social media data from Twitter to develop a methodology for understanding the effects of events and conferences.
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Keys to Developing Leaders When Your Team Grows Too Big

Keys to Developing Leaders When Your Team Grows Too Big | Social Network Analysis #sna | Scoop.it
When your team grows too big, it's time to consider developing leaders. We cover the keys to developing leaders and effectively managing your growing team.
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Analysing the Twitter Mentions Network

Analysing the Twitter Mentions Network | Social Network Analysis #sna | Scoop.it
By Douglas Ashton, Consultant One of the big successes of data analytics is the cultural change in how business decisions are being made. There is now wide spread acceptance of the role that data science has to play in decision making.
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Social Network Models and Data, EC'15 Tutorial

Social networks underly a broad range of social and economic research questions that are increasingly being understood through large-scale computational analyses. In particular, the study of social influence and information diffusion on social networks have rich modeling histories, while opportunities in online instrumentation and experimentations are now providing tremendous advances in our abilities for theory testing as well as theory development.
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 This tutorial will provide a brief overview of models of social networks and social influence, and then focus on giving an overview of recent evidence for how these processes behave empirically in diverse online settings. A particular emphasis will be placed on efforts to approach these problems through causal inference, moving beyond "big data" to "big experimentation".

 

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