Network Science, a textbook for network science, is freely available under the Creative Commons licence. Follow its development onFacebook, Twitter or by signining up to our mailing list, so that we can notify you of new chapters and developments.
By studying the network of links between Indian recipes, computer scientists have discovered that the presence of certain spices makes a meal much less likely to contain ingredients with flavors in common.
Food pairing seems to be common in North American and Western European cuisines but absent in cuisines from southern Europe and East Asia.
Today, Anupam Jain and pals at the Indian Institute of Technology Jodhpur say the opposite effect occurs in Indian cuisine. In this part of the world, foods with common flavors are less likely to appear together in the same recipe. And the presence of certain spices make the negative food pairing effect even stronger.
SocioViz is new born social media analytics platform powered with Social Network Analysis metrics; actually is available for Twitter but in the near future will be extended to other main social media channels.
“ The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories—e.g., chemtrails, reptilians or the Illuminati—are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives—i.e. main stream scientific and conspiracy news—are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users’ content selection, we conclude our analysis measuring how users respond to 4,709 troll information—i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.”
Via Ashish Umre, Frédéric Amblard
We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.
Friso van Vollenhoven of Xebia uses a combination of Hadoop, Neo4j and browser based visualization and interactive tools to look at graphs, search for known interesting patterns in big graphs and do ad hoc querying against graphs.
In this video, he demonstrates the workflow of using Hadoop to create a graph out of data and bulk load the result into Neo4j for efficient ad hoc querying and visualization, potentially partitioning the graph in Hadoop, to create partitions of manageable volume for the database
We study a two states opinion formation model driven by PageRank node influence and report an extensive numerical study on how PageRank affects collective opinion formations in large-scale empirical directed networks. In our model the opinion of a node can be updated by the sum of its neighbor nodes' opinions weighted by the node influence of the neighbor nodes at each step.
First, we observe that all networks reach steady state opinion after a certain relaxation time. This time scale is decreasing with the heterogeneity of node influence in the networks. Second, we find that our model shows consensus and non-consensus behavior in steady state depending on types of networks: Web graph, citation network of physics articles, and LiveJournal social network show non-consensus behavior while Wikipedia article network shows consensus behavior. Third, we find that a more heterogeneous influence distribution leads to a more uniform opinion state in the cases of Web graph, Wikipedia, and Livejournal. However, the opposite behavior is observed in the citation network. Finally we identify that a small number of influential nodes can impose their own opinion on significant fraction of other nodes in all considered networks.
Text documents and Excel tables are essentially story-telling devices. They are very useful to communicate information in a logical and chronologically coherent way. However, as the digital networks proliferate, complexity of the stories that need to be told also increases. That’s why it makes sense to embrace networks as the new useful story-telling device
The Collective Intelligence Handbook [tentative title]Thomas W. Malone and Michael S. Bernstein (Editors)
Collective intelligence has existed at least as long as humans have, because families, armies, countries, and companies have all--at least sometimes--acted collectively in ways that seem intelligent. But in the last decade or so a new kind of collective intelligence has emerged: groups of people and computers, connected by the Internet, collectively doing intelligent things. In order to understand the possibilities and constraints of these new kinds of intelligence, a new interdisciplinary field is emerging.
This book will introduce readers to many disciplinary perspectives on behavior that is bothcollective and intelligent. By collective, we mean groups of individual actors, including, for example, people, computational agents, and organizations. By intelligent, we mean that the collective behavior of the group exhibits characteristics such as, for example, perception, learning, judgment, or problem solving."
“ To be a professor is to belong to a select few—an insider’s club of vanishing tenured faculty positions. It’s no secret that a fancy diploma can help grads vying for those coveted spots. But while working on his PhD and contemplating his career prospects, computer scientist Aaron Clauset wanted to know just how much weight a…”
Via Niklaus Grunwald
MonkeyLearn is a text mining platform that uses machine learning to help developers easily extract and classify information from text. Here’s how to use the MonkeyLearn API to analyze Twitter data to understand user interests.
It takes just a little talk with me to know that I'm a fan of the financial market and many subjects related to economics.
Given all these data, we conclude that the present relations in economic news actually reflect the data from our commercial relations. Maybe it was not different, but it is a way to show how everything is connected and in fact, given that markets are efficient (there is much discussion here and I tend to disagree with the theory), we have that trade relations will be reflected in some way in the behavior of market players and consequently,will be reflected upon pricing of financial assets.
Social scientists have never understood why some countries are more corrupt than others. But the first study that links corruption with wealth could help change that.
Paulus and Kristoufek use this data to search for find clusters of countries that share similar properties using a new generation of cluster-searching algorithms. And they say that the 134 countries they study fall neatly into four groups which are clearly correlated with the wealth of the nations within them.
The method that makes this possible is known as the average linkage clustering approach. It begins by assuming that each country represents a cluster in itself and then looking for the nearest neighbour in the ranking. This pair then become a new cluster and this cluster placed back into the list as a single entity. The process is then repeated until it produces a single cluster of all the countries.
Introduction Last weekend the G20 Conference was hosted in Brisbane Australia. According to the G20 official website the objectives of G20 are: The Group of Twenty (G20) is the premier forum for its members’ international economic cooperation and decision-making
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