August , , San Francisco, CA. April 30, The annual SIGKDD doctoral dissertation award recognizes excellent research by doctoral candidates in the field of data mining and knowledge discovery. The methods produce quality topics, phrases and relations with no or little supervision. The winner and runners-up will be invited to present his or her work in a special session at the KDD conference. The emergence of the cloud, internet of things, social media etc. Submissions must be received by the submission deadline see below.
For dissertations selected as award recipients, a copyright transfer form signed by the candidate is required giving permission for the dissertation to appear on KDD. Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i. As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. Today’s social and internet networks contain millions or even billions of nodes, and copious amounts of side information context such as text, attribute, temporal, image and video data. A thorough analysis of a social network should consider both the graph and the associated side information, yet we also expect the algorithm to execute in a reasonable amount of time on even the largest networks. Nominations due Apr
The final dissertation defense should take place at the nominee’s host institution before the submission deadline. PDF format is preferred for all materials.
Modeling Large Social Networks in Context. Towards the goal of rich analysis on societal-scale networks, this thesis provides 1 modeling and algorithmic techniques for incorporating network context into existing network analysis algorithms based on statistical models, and 2 strategies for network data representation, model design, algorithm design and distributed multi-machine programming that, together, ensure scalability to large networks.
The winner and runners-up will be invited to present his or her work in a special session at the KDD conference. It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem.
Award Presentation at KDD We received 15 nominations this year. The dissertation defense must not have taken place prior to January 1st, The doctoral candidate must have successfully defended the nominated dissertation, and the dissertation must have been accepted by the candidate’s academic unit before the submission deadline. The award winner and up to two runners-up will be recognized at the KDD conference, and their dissertations will have the opportunity to be published on the KDD Web site http: After receiving the nominations, we invited leading experts to serve on the award selection committee from all over the world.
The methods produce quality topics, phrases and relations with no or little supervision.
AugustSydney, Australia. This is a change from previous years’ policy that each department can only nominate one student. However, little attention is paid on distrust in social media. During the second phase, all members without COI were invited to rank the top 6 nominations.
As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. The methods presented herein combine the flexibility of statistical models with key ideas and empirical observations from the data mining and social networks communities, and are supported by distributed systems research for cluster computing.
Award Presentation at KDD The pervasive use of social media generates massive data at an unprecedented rate. Diesertation media differs from the physical world: The award winner will also receive a free registration to attend the KDD conference. Twitter Feed Follow Us on Twitter.
The runners-up will receive a plaque at the conference.
SIGKDD Data Science/Data Mining PhD Dissertation Award – Nominations due Apr 30
It has been proven to be an effective way to mitigate information overload and credibility problems and has attracted increasing attention. Submissions must be received by the submission deadline. These unique properties of social media present novel challenges for computing distrust in social media: The chief objective of this dissertation is jdd figure out solutions to these challenges via innovative research and novel methods.
Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i.
They are scalable and the runtime is orders of magnitude faster than alternatives in large datasets. The thesis studies how to uncover semantically rich structures, such as topical hierarchies and relationships among entities, from massive data that may contain both unstructured text and interconnected entities.
Proposed methodologies are demonstrated in applications to a variety of domains, such as academic service, event log and news article explorer, and product review analytics. All nomination materials must be in English.