Research

Time-based Models of Trust

Sat, 1 Oct 2011 17:39:50 -0400

Trust has been studied in many different contexts, but is coming into focus again in social networks. A great deal of information is exchanged today on social networks. Sometimes this exchange is between people who know each other and sometimes it is not. In any case, people are forced to make quick decisions about trust in many social contexts based on limited information. We examine the impact time plays in trust models.

In our first piece of work, we consider a model that incorporates factors impacting trust from many different time scales. At the lowest level, we consider the impact cognitive heuristics and biases have on trust decisions. These are the quickest factors that are included in the trust decision. The second level considers the more deliberate, system 2 type, trust decisions based on prior experiences with the trustee and other social factors. This is further followed by additional data that can be gathered from the network to support or refute the trust decision, if the trustor chooses to engage in them. This work is presented at the Trust in Agent Societies Workshop held in conjunction with AAMAS 2011.

In our second piece of work, we consider the case where the trustor has to incorporate two factors into the decision. The first one is called competence, it measures how capable is the trustee to accomplish a task. The second one is called willingness, it measures how much energy or bandwidth the trustee is willing to allocate to the trustee. Both factors play a significant role in deciding whether to trust someone for a time-critical mission. For example, if the objective is to spread information to the network quickly, the right combination of both factors is needed. We develop models based on two factors and show how they can be incorporated into decision making. This work is being presented at the 2012 IEEE Conference on Cognitive Methods in Situation Awareness and Decision Support, COGSIMA 2012.

Prominence in Social Networks

Fri, 1 Apr 2011 17:32:38 -0400

in this work, we consider prominence computation in social networks. Instead of only considering relationships between people, we ask a different questions: What about the links between the objects people create? How are they connected? People collaborate on objects and these objects form natural groups: like research collaborations, research areas or venues they appear in. We first use data mining algorithms to find the natural groupings between objects. These groupings show us that prominent people tend to belong to prominent groups with prominent objects.

Using this intuition, we compute prominence using an iterative algorithm. We show that our algorithm beats in performance using many different measures of performance many well known algorithms like hits, pagerank and various centrality measures for many different data sets like the Internet movie database (IMDB), Enron mail data and Academic research collaborations (DBLP).

The preliminary version of this work appears in ICWSM 2011.

Metamorphic Petrology

Mon, 15 Jan 2007 11:22:15 -0500

Metamorphic petrology is the study of the changes (metamorphosis) in rocks that are due to heat and pressure. We are in the process of building a database (MetPetDB) that will store and make available public data sets on metamorphic petrology. The data sets will contain information on metamorphic samples and subsamples, chemical analyses and images of samples, subsamples and other analyses. Furthermore, the database will contain information linking the data to existing publications, people and institutions. This data will be uploaded by the users of the system through an upload facility. The database will make it possible to search and browse the available data and run different daa analysis tools on top of data.

Related Information:
• Bouchra Bouqata, Adam Marcus, Frank Spear, and Boleslaw Szymanski, ``A day in the life of a metamorphic petrologist'', in Proceedings of the Semantic Web and Databases Workshop, 2006. [swdb2006.pdf][swdb_Presentation.pdf]
• Wiki for our development page.

Managing Activities

Tue, 14 Mar 2006 23:08:46 -0500

Users create and modify data as a function of activities they are involved in. However, most desktop management systems provide an object oriented view of a computer, where the emphasis is on describing the information relevant to an object. However, objects may be related to each other in different ways and in different contexts. We develop methods to explore objects on a desktop through activities. Our methods integrate discovered relationships between data objects based on their participation in different activities as well as other properties. Our activity model is well suited to support a query language that is able to alter the context and the definition of an activity to easily visualize complex relationships in data. We show that this new organization makes many new interesting desktop functionalities a reality.

Rank Aggregation vs. Ranker Quality

Tue, 14 Mar 2006 22:25:55 -0500

The rank aggregation problem has been studied extensively in recent years with a focus on how to combine several different rankers to obtain a consensus aggregate ranker. We study the rank aggregation problem from a different perspective: how the individual input rankers impact the performance of the aggregate ranker. We develop a general statistical framework based on a model of how the individual rankers depend on the ground truth ranker. Within this framework, one can study the performance of different aggregation methods. The individual rankers, which are the inputs to the rank aggregation algorithm, are statistical perturbations of the ground truth ranker. With rigorous experimental evaluation, we study how noise level and the misinformation of the rankers affect the performance of the aggregate ranker. We introduce and study a novel Kendall-tau rank aggregator and a simple aggregator called PrOpt, which we compare to some other well known rank aggregation algorithms such as average, median and Markov chain aggregators. Our results show that the relative performance of aggregators varies considerably depending on how the input rankers relate to the ground truth.