KDD DISSERTATION AWARD 2015

Nominations due Apr Computing Distrust in Social Media. Award Presentation at KDD The annual SIGKDD doctoral dissertation award recognizes excellent research by doctoral candidates in the field of data mining and knowledge discovery. April 30, The annual SIGKDD doctoral dissertation award recognizes excellent research by doctoral candidates in the field of data mining and knowledge discovery. During the second phase, all members without COI were invited to rank the top 6 nominations. A nomination must include:

A nomination must include: Twitter Feed Follow Us on Twitter. These efforts come together in a novel mixed-membership triangle motif model that scales to large networks with over million nodes on just a few cluster machines, and can be readily extended to accommodate network context using the other techniques presented herein. They are scalable and the runtime is orders of magnitude faster than alternatives in large datasets. Social media differs from the physical world: The methods produce quality topics, phrases and relations with no or little supervision.

The emergence of the cloud, internet of things, social media etc.

kdd dissertation award 2015

Social media differs from the physical world: In general, the data are viewed as text-rich heterogeneous information networkswhich allow the data to be text-only unstructured datanetwork-only interconnected dataor text plus links. Based on this view, the thesis lays down a mining framework of: However, little attention is paid on distrust in social media.

April 30, The annual SIGKDD doctoral dissertation award recognizes excellent research by doctoral candidates in the field of data mining and knowledge discovery.

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It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem. 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. They are scalable and the runtime is orders of magnitude faster than alternatives in large datasets.

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Trust, providing evidence about with whom users can trust to share information and from whom users can accept information without additional verification, plays a crucial role in helping online users collect relevant and reliable information.

A nomination letter, written by the dissertation advisor of the candidate.

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All nomination materials must be in English. This is sward change from previous years’ policy that each department can only nominate one student. Submissions must be received by the submission deadline see below. Meanwhile, users in social media can be both passive content consumers and active content producers, causing the quality of user-generated dlssertation can vary dramatically from excellence to abuse or spam, which results in a problem of information credibility.

Haixun Wang, Facebook, haixun [at] fb.

Overall Presentation and Readability of Dissertation including organization, writing style and exposition, etc. Nominations due Apr PDF format is preferred for all materials.

kdd dissertation award 2015

For dissertations selected as award recipients, a copyright transfer form awarr by the candidate is required giving permission for the dissertation to appear on KDD. Tags sigkdd qirong ho jiliang tang jiawei han huan liu eric xing dissertation awards chi wang The winner and runners-up will be invited to present his or her work in a special session at the KDD conference. Each dissertation was reviewed by at least 3 experts who helped group the dissertations into two competing groups.

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The runners-up will receive a plaque at the conference. During the second phase, all members without COI were invited to rank the top 6 nominations. Furthermore, the final dissertation defense must not have taken place prior to January 1st, The pervasive use of social media generates massive data at an unprecedented rate.

Late submissions will not be accepted. The award winner will also receive a free registration to attend the KDD conference. Computing Distrust in Social Media. The chief objective of this dissertation is to figure out solutions to these aawrd via innovative research and novel methods.

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Modeling Large Social Networks in Context. Each nominated dissertation must also have been successfully defended awagd the candidate, and the final version of each nominated dissertation must have been accepted by the candidate’s academic unit. 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.

Twitter Feed Follow Us on Twitter. The award winner will also receive a free registration to attend the KDD conference.

These efforts come together in a novel mixed-membership triangle motif model that scales to large networks with over million nodes on just a few cluster machines, and can be readily extended to accommodate network context using the other techniques presented herein.

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