Browsing by Author "Swaminathan, Anand"
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- An Algorithm for Influence Maximization and Target Set Selection for the Deterministic Linear Threshold ModelSwaminathan, Anand (Virginia Tech, 2014-07-03)The problem of influence maximization has been studied extensively with applications that include viral marketing, recommendations, and feed ranking. The optimization problem, first formulated by Kempe, Kleinberg and Tardos, is known to be NP-hard. Thus, several heuristics have been proposed to solve this problem. This thesis studies the problem of influence maximization under the deterministic linear threshold model and presents a novel heuristic for finding influential nodes in a graph with the goal of maximizing contagion spread that emanates from these influential nodes. Inputs to our algorithm include edge weights and vertex thresholds. The threshold difference greedy algorithm presented in this thesis takes into account both the edge weights as well as vertex thresholds in computing influence of a node. The threshold difference greedy algorithm is evaluated on 14 real-world networks. Results demonstrate that the new algorithm performs consistently better than the seven other heuristics that we evaluated in terms of final spread size. The threshold difference greedy algorithm has tuneable parameters which can make the algorithm run faster. As a part of the approach, the algorithm also computes the infected nodes in the graph. This eliminates the need for running simulations to determine the spread size from the influential nodes. We also study the target set selection problem with our algorithm. In this problem, the final spread size is specified and a seed (or influential) set is computed that will generate the required spread size.
- Information Retrieval System EvaluationWei, Shiyi; Suwardiman, Victoria; Swaminathan, Anand (2012-10-03)The module introduces the evaluation in information retrieval. It focuses on the standard measurement of system effectiveness through relevance judgments.
- ProjOpenDSA - OpenDSA Log SupportWei, Shiyi; Suwardiman, Victoria; Swaminathan, Anand (2012-12-11)The OpenDSA project is an online eTextbook project that includes not only literature but other dynamic content to be used in Data Structures and Algorithms courses. OpenDSA contains exercises of various types to go along with the literature in order to provide automated self-assessment for students. What the research team seeks to do is to collect and log data regarding student interactions with these exercises, logging both the students’ performance, such as scores, as well as their interaction with the system, such as timestamps for button clicks. What we did to extend the current OpenDSA project is provide visualizations of the log data in meaningful ways as to be helpful to all users of the system. The OpenDSA Log Support Project, as we have called it, is designed to analyze the log data and provide views for the instructors who teach the course, the students who take the course, as well as for the developers who designed and are continually working on improving the system. Taking the various forms of log data collected from the students in the DSA course of the Fall 2012 semester, we developed three views: the teacher view, student view, and developer view. Each view displays information that is most useful to its user; for example, a comprehensive table of all students, their scores, and their status in each exercise is the most important data that a teacher will be interested in seeing. We developed our views using the Django web framework that the OpenDSA research team is currently using, pulling our data from the database that all of the data gets logged to. Using this data, we then created online views accessible to those with accounts, namely the instructor, students, and developers. Some challenges we ran into include the display of and performance of displaying the data in our views. This came up because of the amount of data logged, proving difficult to find efficient and readable ways to analyze and display the data. Though some solutions have been found, because this project is ongoing, future work include optimizing each view, improving the display of each view, as well as adding additional views for each user.