Work by Olaf Sporns, YY Ahn, Alessandro Flammini and colleagues was featured on the cover of Neuron. In the paper Cooperative and Competitive Spreading Dynamics on the Human Connectome, the authors present a simulation model of spreading dynamics, previously applied in studies of social networks, that offers a new perspective on how the connectivity of the human brain constrains neural communication processes. Local perturbations in a social network can trigger global cascades (orange and turquoise epicenters in background image). In the case of the brain, the spreading of such cascades follows organized patterns that are shaped by anatomical connections revealing how interactions among functional brain networks may give rise to the integration of information.
If you are contemplating grad school in general or applying for one of our PhD programs, and in particular if you are thinking about doing research with me, please take a look at these resources before you contact me:
- Should You Go To Grad School?
- Recipes for PhD
- 10 easy ways to fail a Ph.D.
- Advice for students and junior researchers
- Ten things I wish I knew before starting on a PhD
- Grad School Advice
- Grad Students Will Work for Food
- The PhD Experience
- Tons more of advice
- … and of course the most important guide to see what the PhD is really like: PhD Comics
As adaptive peer network systems becoming an increasingly important development in Web search technology, in this research, an alternative model for peer based Web search is introduced to address the scale problem of centralized search engines. Queries are first matched against the local engine, and then routed to neighbor peers to obtain more results. Initially the network has a random topology (like Gnutella) and queries are routed randomly as in the flood model. However, the protocol includes a learning algorithm by which each peer uses the results of its interactions with its neighbors to refine a model of the other peers. This model is used to dynamically route queries according to the predicted match with other peers’ knowledge. The network topology is thus modified on the fly based on learned contexts and current information needs.
This week at NaN I’ll be running through a very preliminary version of my thesis talk, “Structural Mining of Large-Scale Behavioral Data from the Internet,” which is all about the things you can discover using network flow data and Web clicks. The big things that I’ll be looking to get as feedback have to do with organization and pruning — I’m not terribly confident of the order in which I present the material, and I have a LOT more things to say than time to say it in, so I can use some suggestions on what to keep and what to just point to the actual document for.
(And, yes, there will be cookies.)
Online documents provide a rich information resource for aiding the generation of concept-map-based knowledge models, but analyzing resources to select concepts and links is a time consuming task. This work focuses on harnessing the information in unstructured text documents using text mining algorithms to generate preliminary concept maps automatically. These maps can be used to assist human users on question answering tasks or automatic document classification.
I’m presenting my pre-defense content of my research. There will also be cakes!
I will be using Tuesday (3/24) as a practice talk for AIRWEB 2009. Hope everyone had a nice spring break!
Title: Social Spam Detection
The popularity of social bookmarking sites has made them prime targets for spammers. Many of these systems require an administrator’s time and energy to manually filter or remove spam. Here we discuss the motivations ofsocial spam, and present a study of automatic detection of spammers in a social tagging system. We identify and analyze six distinct features that address various properties of social spam, finding that each of these features provides for a helpful signal to discriminate spammers from legitimate users. These features are then used in various machine learning
algorithms for classification, achieving over 98% accuracy in detecting social spammers with 2% false positives. These promising results provide a new baseline for future efforts on social spam. We make our dataset publicly
available to the research community.