Today we had a talk by Cyrus Hall of the University of Lugano. He proposed a p2p sampling algorithm to allow p2p admins to monitor and maintain their nets. He evaluated his algorithm against synthetic p2p topologies, which included kleinberg’s social net model and barabasi’s. For years now, it has been assumed that those traditional models reflect real social nets. However, after the talk, Damon pointed at the existence of exponential random graph models. Traditional models do not assume the knowledge of any global property (e.g., clustering coefficient) of a social network – they do the modeling without any input. By contrast, random graph models are able to model any type of social network on input of the network’s global properties. here is a paper titled “Recent developments in exponential random graph (p*) models for social networks” (pdf). Enjoy!
Archive for April, 2009
- Huan Liu‘s recent work includes two video lectures: (1) WSDM’08 Identifying Influential Bloggers in a Community; and (2) KDD’08 Tutorial on Blogosphere: Research, Tools, and Applications.
- keynote presentation by Phillip Bonacich (UCLA, Emeritus), about Power and Exploitation in Exchange Networks: A Social-Psychological Model.
- Mary Lou Maher (NSF) talked about Research Challenges for Computationally Enabled Social and Collective Intelligence.She gave a number of collective intelligence examples, including open source systems, recommender systems, search engines, and Wikipedia.
- William H. Batchelder, a leading expert on psychology and social sciences, talked about Cultural Consensus Theory, which is an approach to pooling information from different sources.Batchelder showed that a social network model, with a good deal of math, a Bayesian formulation and MCMC methods, can be used to estimate the consensus answers.
- Shade Shutters (ASU) talked about Punishment, Rational Expectations, and Relative Payoffs.
- Many posters were presented during a workshop dinner the first night
- On the second workshop day, Alex Penland from MIT Media Lab gave a keynote talk on Reality Mining: From Profiles and Demographics to Behavior .Dr. Penland is very sensitive to privacy issues and says these sensors should not be used to spy on employees. He suggests that deployment should be on voluntary data with individuals owning the data and have the opportunity to review their data each day. Learning can be done effectively from anonymized profiles. Dr. Penland company Sense Networks is now commercializing these applications in macrosense™ and Citysense™
If you are on Twitter, feel free to join our newly formed recommender system-interest twibe:
BOSS (Build your Own Search Service) is Yahoo!’s open search web services platform. … Developers, start-ups, and large Internet companies can use BOSS to build and launch web-scale search products that utilize the entire Yahoo! Search index.
(from Brad Karp’s email)
Dr Ranveer Chandra
[Talk title TBA]
10:30 AM, Thursday, 16th April
Roberts G06 (Sir Ambrose Fleming LT)
Bio: Ranveer Chandra is a researcher in the Networking Research Group at Microsoft Research. He completed his undergraduate studies from the Indian Institute of Technology, Kharagpur and a PhD in Computer Science from Cornell University. He was the recipient of the Microsoft Graduate Research Fellowship during his PhD and his dissertation on VirtualWiFi was nominated by Cornell for the ACM Dissertation Award. VirtualWiFi has been downloaded more than 100,000 times and is the third most downloaded software ever to be released by Microsoft Research. Ranveer has authored more than 25 research papers and filed more than 30 patents. He is active in the networking and mobile systems community, and has served on the program committees of several conferences.
Professor Michael Mitzenmacher
Some Results on Coding for Flash Memory
11:15 AM, Thursday, 16th April
Roberts G06 (Sir Ambrose Fleming LT)
Abstract: Flash memory is rapidly becoming the technology of choice for storage in several settings. But flash memory behaves differently than other memory systems, making us rethink the basic ways we represent data. In this talk we’ll consider the question of how to code data for flash memory systems. Although our framework will be primarily theoretical, it will shed light on some of the basic issues underlying the use of flash memory systems, including what considerations need to be kept in mind when designing algorithms or data structures for such systems.
Bio: Michael Mitzenmacher is a Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Michael has authored or co-authored over 140 conference and journal publications on a variety of topics, including Internet algorithms, hashing, load-balancing, erasure codes, error-correcting codes, compression, bin-packing, and power laws. His work on low-density parity-check codes shared the 2002 IEEE Information Theory Society Best Paper Award. His textbook on probabilistic techniques in computer science, co-written with Eli Upfal, was published in 2005 by Cambridge University Press. This year, he is serving as chair of STOC 2009 and on the PC of SIGCOMM 2009.
Michael Mitzenmacher graduated summa cum laude with a degree in mathematics and computer science from Harvard in 1991. After studying math for a year in Cambridge, England, on the Churchill Scholarship, he obtained his Ph. D. in computer science at U.C. Berkeley in 1996. He then worked at Digital Systems Research Center until joining the Harvard faculty in 1999.
“Albert-László Barabási and colleagues monitored 100,000 users for 6 months and recorded the location of the cell phone tower that transmitted each call or text message. The team found that:
- most people stayed close to home, and a select few regularly took long trips. … regardless of how mobile they were, people returned over and over to a few top locations with similar probability. For example, two users would have roughly the same chance of being found in their third-favorite spots, whether it was the gym or the theater. These hangouts were often located near the path between their top two destinations–usually home and work
- most people traveled very short distances most of the time, while some traveled great distances.
- each individual’s data fit into the same mathematical model—a type of power law—that predicts the probability of finding a person in a certain location. That probability distribution is dependent on an individual’s average travel distance and decreases the further he or she roams. Human mobility and how we travel is so amazingly complex,” says Max Planck’s Brockmann. “What is very strange is that despite this complexity, all the traveling behavior can be accounted for by very simple mathematical laws.”
Potential applications: traffic forecasting and urban planning.
From Alex Kerr on momolondon:”I thought this might be of interest to any developers on this list who are interested in learning to develop for the iPhone, particularly those non-corporate folks (e.g. students etc) who are on lower budgets. Stanford University have just started a new taught iPhone development class this semester and are putting it all up on iTunesU for free (at the same speed as the course is taught), and some of the material is available on the ordinary website.More details here
Few videos featuring our colleagues
Across the Department:
In the Software Systems group:
In the Networks Group:
More videos here.
Yesterday I was watching Genius and I loved it!!!!!! Members of the public write to the comedian Dave Gorman with their funny ideas. Then Dave gets a guest on the show to decide if the ideas are Genius, or not.”
Brendan put forward a brilliant idea: how to crowdsource bus driving (1 and half minute of madness!) – every passenger has a steering wheel and the direction of the bus is determined by what the majority of passengers tell it to do. Unfortunately, it got the thumbs down