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Posted by Posted by Corinna Cortes, Head, Google Research NY

The 21st ACM conference on Knowledge Discovery and Data Mining (KDD’15), a main venue for academic and industry research in data management, information retrieval, data mining and machine learning, was held last week in Sydney, Australia. In the past several years, Google has been actively participating in KDD, with several Googlers presenting work at the conference in the research and industrial tracks. This year Googlers presented 12 papers at KDD (listed below, with Googlers in blue), all of which are freely available at the ACM Digital Library.

One of these papers, Efficient Algorithms for Public-Private Social Networks, co-authored by Googlers Ravi Kumar, Silvio Lattanzi, Vahab Mirrokni, former Googler intern Alessandro Epasto and research visitor Flavio Chierichetti, was awarded Best Research Paper. The inspiration for this paper comes from studying social networks and the importance of addressing privacy issues in analyzing such networks.

Privacy issues dictate the way information is shared among the members of the social network. In the simplest case, a user can mark some of her friends as private; this would make the connections (edges) between this user and these friends visible only to the user. In a different instantiation of privacy, a user can be a member of a private group; in this case, all the edges among the group members are to be considered private. Thus, each user in the social network has her own view of the link structure of the network. These privacy issues also influence the way in which the network itself can be viewed and processed by algorithms. For example, one cannot use the list of private friends of user X for suggesting potential friends or public news items to another user on the network, but one can use this list for the purpose of suggesting friends for user X.

As a result, enforcing these privacy guarantees translates to solving a different algorithmic problem for each user in the network, and for this reason, developing algorithms that process these social graphs and respect these privacy guarantees can become computationally expensive. In a recent study, Dey et al. crawled a snapshot of 1.4 million New York City Facebook users and reported that 52.6% of them hid their friends list. As more users make a larger portion of their social neighborhoods private, these computational issues become more important.

Motivated by the above, this paper introduces the public-private model of graphs, where each user (node) in the public graph has an associated private graph. In this model, the public graph is visible to everyone, and the private graph at each node is visible only to each specific user. Thus, any given user sees their graph as a union of their private graph and the public graph.

From algorithmic point of view, the paper explores two powerful computational paradigms for efficiently studying large graphs, namely, sketching and sampling, and focuses on some key problems in social networks such as similarity ranking, and clustering. In the sketching model, the paper shows how to efficiently approximate the neighborhood function, which in turn can be used to approximate various notions of centrality scores for each node - such centrality scores like the PageRank score have important applications in ranking and recommender systems. In the sampling model, the paper focuses on all-pair shortest path distances, node similarities, and correlation clustering, and develop algorithms that computes these notions on a given public-private graph and at the same time. The paper also illustrates the effectiveness of this model and the computational efficiency of the algorithms by performing experiments on real-world social networks.

The public-private model is an abstraction that can be used to develop efficient social network algorithms. This work leaves a number of open interesting research directions such as: obtaining efficient algorithms for the densest subgraph/community detection problems, influence maximization, computing other pairwise similarity scores, and most importantly, recommendation systems.

KDD’15 Papers, co-authored by Googlers:

Efficient Algorithms for Public-Private Social Networks (Best Paper Award)
Flavio Chierichetti, Alessandro Epasto, Ravi Kumar, Silvio Lattanzi, Vahab Mirrokni

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
Sungjin Ahn, Anoop Korattikara, Nathan Liu, Suju Rajan, Max Welling

TimeMachine: Timeline Generation for Knowledge-Base Entities
Tim Althoff, Xin Luna Dong, Kevin Murphy, Safa Alai, Van Dang, Wei Zhang

Algorithmic Cartography: Placing Points of Interest and Ads on Maps
Mohammad Mahdian, Okke Schrijvers, Sergei Vassilvitskii

Stream Sampling for Frequency Cap Statistics
Edith Cohen

Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams
Nan Du, Mehrdad Farajtabar, Amr Ahmed, Alexander J.Smola, Le Song

Adaptation Algorithm and Theory Based on Generalized Discrepancy
Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina (now at Google)

Estimating Local Intrinsic Dimensionality
Laurent Amsaleg, Oussama Chelly, Teddy Furon, Stéphane Girard, Michael E. Houle Ken-ichi Kawarabayashi, Michael Nett

Unified and Contrasting Cuts in Multiple Graphs: Application to Medical Imaging Segmentation
Chia-Tung Kuo, Xiang Wang, Peter Walker, Owen Carmichael, Jieping Ye, Ian Davidson

Going In-depth: Finding Longform on the Web
Virginia Smith, Miriam Connor, Isabelle Stanton

Annotating needles in the haystack without looking: Product information extraction from emails
Weinan Zhang, Amr Ahmed, Jie Yang, Vanja Josifovski, Alexander Smola

Focusing on the Long-term: Its Good for Users and Business
Diane Tang, Henning Hohnhold, Deirdre OBrien

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