Works.bepress.com/cgi/viewcontent.cgi?article=1026&context=matt_bogard. Forest-fire model. Evolution of forest with p/f=100 In applied mathematics, a forest-fire model is any of a number of dynamical systems displaying self-organized criticality. Note, however, that according to Pruessner et al. (2002, 2004) the forest-fire model does not behave critically on very large, i.e. physically relevant scales. Early versions go back to Henley (1989) and Drossel and Schwabl (1992). The model is defined as a cellular automaton on a grid with Ld cells. L is the sidelength of the grid and d is its dimension.
A cell can be empty, occupied by a tree, or burning. A burning cell turns into an empty cellA tree will burn if at least one neighbor is burningA tree ignites with probability f even if no neighbor is burningAn empty space fills with a tree with probability p The controlling parameter of the model is p/f which gives the average number of trees planted between two lightning strikes (see Schenk et al. (1996) and Grassberger (1993)). Where Tsmax is the burn time of the largest cluster. Building data science teams. Starting in 2008, Jeff Hammerbacher (@hackingdata) and I sat down to share our experiences building the data and analytics groups at Facebook and LinkedIn.
In many ways, that meeting was the start of data science as a distinct professional specialization (see the“What makes a data scientist” section of this report for the story on how we came up with the title “Data Scientist”). Since then, data science has taken on a life of its own. The hugely positive response to “What Is Data Science? ,” a great introduction to the meaning of data science in today’s world, showed that we were at the start of a movement. There are now regular meetups, well-established startups, and even college curricula focusing on data science.
As McKinsey’s big data research report and LinkedIn’s data indicates, data science talent is in high demand. This increase in the demand for data scientists has been driven by the success of the major Internet companies. Courtesy LinkedIn Corp. Being data driven. Betweenness centrality. Definition[edit] The betweenness centrality of a node is given by the expression: where is the total number of shortest paths from node to node and is the number of those paths that pass through Note that the betweenness centrality of a node scales with the number of pairs of nodes as implied by the summation indices.
. , so that . For directed graphs and for undirected graphs, where is the number of nodes in the giant component. Which results in: Note that this will always be a scaling from a smaller range into a larger range, so no precision is lost. The load distribution in real and model networks[edit] Model networks[edit] It has been shown that the load distribution of a scale-free network follows a power law given by a load exponent this implies the scaling relation to the degree of the node, Where is the average load of vertices with degree .
Are not independent since equation (1) implies [11] For large g, and therefore large k, the expression becomes which proves the following equality: With time. 4 Ways To Create Brand Content People Actually Care About. Google Study: PPC Ads Do NOT Cannibalize Your Organic Traffic. Does Paid Search Really Cannibalize Your Organic Traffic? Though I’ve never met him personally, I admit to being a big Hal Varian fan. For those who don’t recognize the name, Dr. Varian is the Chief Economist at Google and like me, one of the oldest guys in his company. Over the past few years, he and his team of researchers have made my life simpler by providing pithy answers to some of search’s mythically difficult questions, like “How do search auctions work?” And “Does ad position effect conversion rates?” Last week, his team at Google released the results of their research that answers a question that paid search managers across the world get asked on a regular basis: “Why the [bleep] are we advertising on our own [bleeping] brand terms when we are ranked #1 for those [bleepety-bleep-bleeping] terms already?
Their findings, in true Varian-esque style, were simple, direct and memorable. Is A Google Study That Proves Google Paid Search Works Valid? Google’s own study was rigorous. James Fowler. How to Predict the Spread of News on Twitter. Twitter has revolutionised the way millions of people receive news and the type of news they get.
So it’s no surprise that there is huge interest in predicting what kind of stories are likely to spread furthest and fastest. One way to make this kind of prediction is to study how a story spreads soon after it is released into the wild. Various groups have shown that this early popularity can be a good predictor of a story’s later spread. A couple of years ago, Bernardo Huberman and pals at HP’s Social Computing Lab in Palo Alto used this approach to predict the eventual box office revenues based on the rate of tweets about a film soon after it was released. The problem with this method is that the structure of the network can have a profound effect on the way tweets spread and this has little to do with the content and its appeal.
So Huberman is now taking another approach. That’s pretty impressive and may herald important changes in the way articles are written and edited. Ad Value. The Social Media Measurement Imperative: Building Business Value - eMarketer. Irfan Kamal Senior Vice President, Digital/Social Strategy Ogilvy & Mather At Ogilvy & Mather, Irfan Kamal is responsible for helping clients execute digital marketing programs that meet business goals. As senior vice president for digital/social strategy, he has led research initiatives designed to measure the sales impact of social media marketing. In a conversation with eMarketer principal analyst Debra Aho Williamson, Kamal discussed why focusing on business metrics is crucial in social media marketing, how companies can move away from metrics such as fan count, and why it’s important to understand how social media contributes to customer purchase decisions. eMarketer: Why is measuring success in social media marketing so hard?
Irfan Kamal: We expect social media to be like other media that we’ve measured in the past. Another aspect is that we are measuring just because we can measure, instead of measuring what we need to measure. eMarketer: What should companies measure? Kamal: Yes.