Intelligent Agents Lab

Creating Social Systems

Topics in Social Systems (Spring 2015)

Location: HEC-356
Time: 2 pm - 3:30 pm
Date Presenter Reading
Jan 21 Rahmatollah
Practice AAAI talk
Dissertation Practice
Jan 28 Erfan
Dissertation Practice
Feb 04 Dr. Sukthankar
Lab Meeting
Feb 11 Alireza
Negative Link Prediction in Social Media
(Jiliang Tang et al.)


Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we in- vestigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework.

Feb 18 Hamid
Matrix Factorization
Feb 25 Erfan
Dissertation Practice


Mar 04 Rey
POMDPs for Robotic Tasks with Mixed Observability
(Sylvie Ong et. al)


Partially observable Markov decision processes (POMDPs) provide a principled mathematical framework for motion planning of autonomous robots in uncertain and dynamic environments. They have been successfully applied to various robotic tasks, but a major challenge is to scale up POMDP algorithms for more complex robotic systems. Robotic systems often have mixed observability: even when a robot’s state is not fully observable, some components of the state may still be fully observable. Exploiting this, we use a factored model to represent separately the fully and partially observable components of a robot’s state and derive a compact lowerdimensional representation of its belief space. We then use this factored representation in conjunction with a point-based algorithm to compute approximate POMDP solutions. Separating fully and partially observable state components using a factored model opens up several opportunities to improve the efficiency of point-based POMDP algorithms. Experiments show that on standard test problems, our new algorithm is many times faster than a leading point-based POMDP algorithm.

Mar 11 Spring Break
Mar 18 Alireza
SBP Practice Talk
Mar 25 No Meeting
Apr 01 Rey
Theses Practice Talk
Apr 08 Rahmatollah
Dissertation Practice Talk

Time change: 1:30pm-3pm

Apr 15 Astrid

Further Reading:

Current work in the area of social media and data mining, providing compelling justification for why these research questions are important.

Machine Learning Topics.