KDD 2015 DISSERTATION AWARD

If there are any other PC or SPC members who you believe have, or may be perceived to have, a conflict of interest not covered above, please notify the PC Chairs by email research-chairs kdd. 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. Communities in networks often overlap as nodes can belong to multiple communities at once. Authors submitting a paper will be asked to select one primary subject area, and up to 5 secondary subject areas from the sets of terms below. It has been proven to be an effective way to mitigate information overload and credibility problems and has attracted increasing attention. We discuss insights from the best paper at ACM AVI , increasing interest in visualization, infographics, trends, challenges, advice and more. Submitted papers will go through a competitive peer review process.

Congratulations to all the outstanding students who were nominated and to the winners of this year. Papers due in June. KDnuggets Editor Gregory Piatetsky will moderate. The purpose of the student travel grants is to encourage graduate student participation at the conference by partially funding the costs of students who would otherwise be unable to attend. The pervasive use of social media generates massive data at an unprecedented rate.

Only PDF documents are accepted.

His research focuses on malicious user and information detection. Submissions must be received by the submission deadline.

Data-Driven Approaches towards Malicious Behavior Modeling

When you submit your paper to CMT, you will be asked to select which terms from a pre-defined list of subjects could best be used to describe the content of your paper.

Furthermore, all authors are required to identify all members of the program committee with which they have a conflict of interest.

After receiving the nominations, we invited leading experts to serve on the award selection committee from all over the world.

In this thesis, we develop a family of accurate and scalable community detection methods and apply them to large networks. Such submissions violate our dual submission policy.

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kdd 2015 dissertation award

In particular, computational tasks are designed to understand distrust, a innovative task, i. Route planner in real-time one of the most popular web GIS services in use today, and contest is to find shortest path under polygonal obstacles.

kdd 2015 dissertation award

Award Presentation at KDD Typically, the amount of user feedback e. Of particular interest are procedures that can be used on high dimensional data sets where the number of samples n is much smaller than the ambient dimension p.

KDD , August , Sydney

Tutorials on interdisciplinary directions, novel and fast growing directions, and significant applications are highly encouraged. All communications will be via email. Uncovering Structure in High-Dimensions: Each nominated dissertation must also have been successfully defended by the candidate, and the final version of each nominated dissertation must have been accepted by the candidate’s academic unit.

All applicants need to submit their applications at https: After receiving the nominations, we invited leading experts to serve on the award dissertztion committee from all over the world.

SIGKDD Data Science/Data Mining PhD Dissertation Award – Nominations due Apr 30

As the final part of the thesis, we present several extensions of our models such that we can detect communities with a bipartite connectivity structure and we awad the node attributes and the network structure for community detection. Our approach leads to novel insights that unify two fundamental organizing principles of networks: Moreover, most online activities involve interactions between multiple items and different users and interpreting such complex user-item interactions becomes intractable too.

This thesis develops flexible estimation procedures with provable theoretical guarantees for uncovering unknown hidden structures underlying data generating process. Encourage students too, and help support their participation at KDD.

kdd 2015 dissertation award

Possible workshop topics include all areas of data mining and knowledge discovery, machine learning, statisticsand data and information sciencesbut are not limited to these. KDD will host tutorials covering topics in data mining of interest to the research community as well as application developers. Michael Stonebraker, described as the greatest living contributor to database technology, on how he adjusts to the award and what trends he foresees in database management systems and big data.

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The application domains of interest include, but are not limited to education, public policy, industry, government, healthcare, e-commerce, telecommunications, law, or non-profit settings. Late submissions will not be accepted. Authors submitting a paper will be asked to select one primary subject area, and up to 5 secondary subject areas from the sets of terms below. We begin by challenging the conventional view that defines network communities as densely connected clusters of nodes.

Due to its importance for scientific discovery, we put emphasis on consistent structure recovery throughout the thesis.

Call for Participation, Papers, Workshops, Tutorials, Nominations

This tutorial will introduce the details of the general algorithms from the above three classes that can be applied to any platform and dataset. He has served as a member of the executive committee of SIGKDD; he has published over refereed articles, 17 book chapters and two monographs.

In order to achieve the proposed research objective, we develop the required technologies from three research directions, which are 1 understanding of temporal trends of image collections, 2 discovery of overlapping contents across image collections, and 3 reconstruction and applications of collective photo storylines.

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. The safety, reliability and usability of web platforms are often compromised by malicious entities, such as vandals on Wikipedia, bot connections on Twitter, fake likes on Facebook, and several more.

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