The proliferation of rich social media, on-line communities, and collectively produced knowledge resources has accelerated the convergence of technological and social networks, producing environments that reflect both the architecture of the underlying information systems and the social structure on their members. In studying the consequences of these developments, we are faced with the opportunity to analyze social network data at unprecedented levels of scale and temporal resolution; this has led to a growing body of research at the intersection of the computing and social sciences.

We discuss some of the current challenges in the analysis of large-scale social network data, focusing on two themes in particular: the inference of social processes from data, and the problem of maintaining individual privacy in studies of social networks. While early research on this type of data focused on structural questions, recent work has extended this to consider the social processes that unfold within the networks. Particular lines of investigation have focused on processes in on-line social systems related to communication, community formation, information-seeking and collective problem-solving, marketing, the spread of news, and the dynamics of popularity. There are a number of fundamental issues, however, for which we have relatively little understanding, including the extent to which the outcomes of these types of social processes are predictable from their early stages, the differences between properties of individuals and properties of aggregate populations in these types of data, and the extent to which similar social phenomena in different domains have uniform underlying explanations.

The second theme we pursue is concerned with the problem of privacy. While much of the research on large-scale social systems has been carried out on data that is public, some of the richest emerging sources of social interaction data come from settings such as e-mail, instant messaging, or phone communication in which users have strong expectations of privacy. How can such data be made available to researchers while protecting the privacy of the individuals represented in the data? Many of the standard approaches here are variations on the principle of anonymization � the names of individuals are replaced with meaningless unique identifiers, so that the network structure is maintained while private information has been suppressed.

In recent joint work with Lars Backstrom and Cynthia Dwork, we have identified some fundamental limitations on the power of network anonymization to ensure privacy. In particular, we describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes. The attacks are based on the uniqueness of small random subgraphs embedded in an arbitrary network, using ideas related to those found in arguments from Ramsey theory. Combined with other recent examples of privacy breaches in data containing rich textual or time-series information, these results suggest that anonymization contains pitfalls even in very simple settings. In this way, our approach can be seen as a step toward understanding how techniques of privacy-preserving data mining can inform how we think about the protection of even the most skeletal social network data.

Usama Fayyad, Yahoo!
Tuesday 9:00 am ~ 10:00 am, Imperial

As the Internet continues to change the way we live, find information, communicate, and do business, it has also been taking on a dramatically increasing role in marketing and advertising. Unlike any prior mass medium, the Internet is a unique medium when it comes to interactivity and offers ability to target and program messaging at the individual level. Coupled with its uniqueness in the richness of the data that is available for measurability, in the variety of ways to utilize the data, and in the great dependence of effective marketing on applications that are heavily data-driven, makes data mining and statistical data analysis, modeling, and reporting an essential mission-critical part of running the on-line business.

However, because of its novelty and the scale of data sets involved, few companies have figured out how to properly make use of this data. In this talk, I will review some of the challenges and opportunities in the utilization of data to drive this new generation of marketing systems. I will provide several examples of how data is utilized in critical ways to drive some of these capabilities. The discussion will be framed with the More general framework of Grand Challenges for data mining: pragmatic and technical.

I will conclude this presentation with a consideration of the larger issues surrounding the Internet as a technology that is ubiquitous in our lives, yet one where very little is understood, at the scientific level, in defining and understanding many of the basics the Internet enables: Community, Personalization, and the new Microeconomics of the web. This leads to an overview of the new Yahoo! Research organization and its aims: inventing the new sciences underlying what we do on the Internet, focusing on areas that have received little attention in the traditional academic circles. Some illustrative examples will be reviewed to make the ultimate goals more concrete.

Chris Anderson, Wired Magazine
Wednesday 11:15 am ~ 12:15 pm, Imperial

He is the author of New York Times bestselling book The Long Tail: Why the Future of Business is Selling Less of More, which as published in 2006, and runs a blog on the subject at longtail.com. In 2007 he was named one of the �Time 100,� the newsmagazine�s list of the 100 men and women whose power, talent or moral example is transforming the world.

Previously, he was at The Economist, where he served as U.S. Business Editor, Asia Business Editor (based in Hong Kong); and Technology Editor. He started The Economist�s Internet coverage in 1994 and directed its initial web strategy. Mr. Anderson's media career began at the two premier science journals, Nature and Science, where he served in several editorial capacities. Prior to that he worked as a researcher at Los Alamos National Laboratory�s meson physics facility and served as research assistant to the Chief Scientist of the Department of Transportation. He holds a Bachelor of Science degree in Physics from George Washington University and studied Quantum Mechanics and Science Journalism at the University of California at Berkeley.

R1: Web/Text Mining (I)

Monday 10:30 am ~ 12:30 pm, Regency 1

R2: Graph Mining and Social Networks

Monday 10:30 am ~ 12:30 pm, Regency 2

R3: Filtering and Ranking

Monday 10:30 am ~ 12:30 pm, Crystal

R4: Web/Text Mining (II)

Monday 2pm ~ 3:20pm, Regency 1

R5: Classification (I)

Monday 2pm ~ 3:20pm, Regency 2

R6: Clustering (I)

Monday 2pm ~ 3:20pm, Crystal

R7: Web/Text Mining (III)

Monday 3:50 pm ~ 5:10 pm, Regency 1

R8: Pattern Discovery (I)

Monday 3:50 pm ~ 5:10 pm, Regency 2

R9: Clustering (II)

Monday 3:50 pm ~ 5:10 pm, Crystal

R10: Web/Text Mining (IV)

Tuesday 10:30 am ~ 11:50 am, Regency 1

R11: Pattern Discovery (II)

Tuesday 10:30 am ~ 11:50 am, Regency 2

R12: Anomaly/Template Detection

Tuesday 10:30 am ~ 11:50 am, Crystal

R13: Web/Text Mining (V)

Tuesday 2 pm ~ 3:20 pm, Regency 1

R14: Statistical Methods (I)

Tuesday 2 pm ~ 3:20 pm, Regency 2

R15: Clustering (III)

Tuesday 2 pm ~ 3:20 pm, Crystal

R16: Mining Data Streams

Tuesday 3:50 pm ~ 5:10 pm, Regency 1

R17: Statistical Methods (II)

Tuesday 3:50 pm ~ 5:10 pm, Regency 2

R18: Clustering (IV)

Tuesday 3:50 pm ~ 5:10 pm, Crystal

R19: Temporal Data Mining

Wednesday 9:00 am ~ 10:40 am, Regency 1

R20: Classification (II)

Wednesday 9:00 am ~ 10:40 am, Regency 2

R21: Statistical Methods (III)

Wednesday 9:00 am ~ 10:40 am, Crystal

I1: Data Mining Techniques

Monday 10:30 am ~ 12:30 pm, Regent Club

I2: Data mining on the web

Monday 2:00 pm - 3:20 pm, Regent Club

I3: User behavior mining

Monday 3:50 pm - 5:10 pm, Regent Club

I4: Data mining applications

Tuesday 10:30 am - 11:50 pm, Regent Club

I5: Short presentations

Tuesday 2:00 pm - 3:20 pm, Regent Club

At KDD-07 all accepted papers can be accompanied by a poster presentation. We heavily encourage making use of this opportunity to give the attendees an additional chance to discuss your work with you.

In order to better plan the poster sessions it is mandatory that you register your poster by sending a short note to Michael Berthold at berthold@ieee.org including the paper ID, the title of your poster and the author presenting it during the poster session. We will only reserve space for registered posters!

Posters will have to fit within a 3' x 4' (roughly 90cm x 120cm) area.

To access past submission information and call for proposals, please click here.