# Publications

## Publications

### Distributed Random Walks

Performing random walks in networks is a fundamental primitive that has found applications in many areas of computer science, including distributed computing. In this article, we focus on the problem of sampling random walks efficiently in a distributed network and its applications. Given bandwidth constraints, the goal is to minimize the number of rounds required to obtain random walk samples. All previous algorithms that compute a random walk sample of length ℓ as a subroutine always do so naively, that is, in O(ℓ) rounds.

The main contribution of this article is a fast distributed algorithm for performing random walks. We present a sublinear time distributed algorithm for performing random walks whose time complexity is sublinear in the length of the walk. Our algorithm performs a random walk of length ℓ in Õ(√ℓD) rounds (Õ hides polylog n factors where n is the number of nodes in the network) with high probability on an undirected network, where D is the diameter of the network. For small diameter graphs, this is a significant improvement over the naive O(ℓ) bound.

Furthermore, our algorithm is optimal within a poly-logarithmic factor as there exists a matching lower bound [Nanongkai et al. 2011]. We further extend our algorithms to efficiently perform k independent random walks in Õ(√kℓD + k) rounds. We also show that our algorithm can be applied to speedup the more general Metropolis-Hastings sampling. Our random-walk algorithms can be used to speed up distributed algorithms in applications that use random walks as a subroutine. We present two main applications.

First, we give a fast distributed algorithm for computing a random spanning tree (RST) in an arbitrary (undirected unweighted) network which runs in Õ(√mD) rounds with high probability (m is the number of edges). Our second application is a fast decentralized algorithm for estimating mixing time and related parameters of the underlying network. Our algorithm is fully decentralized and can serve as a building block in the design of topologically-aware networks.

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### Visual Search: A Large-Scale Perspective

### Query Suggestion with Large Scale Data

### Arrival and Departure Dynamics in Social Networks

In this paper, we consider the natural arrival and departure of users in a social network, and ask whether the dynamics of arrival, which have been studied in some depth, also explain the dynamics of departure, which are not as well studied.

Through study of the DBLP co-authorship network and a large online social network, we show that the dynamics of departure behave differently from the dynamics of formation.

In particular, the probability of departure of a user with few friends may be understood most accurately as a function of the raw number of friends who are active. For users with more friends, however, the probability of departure is best predicted by the overall fraction of the user's neighborhood that is active, independent of size.

We then study global properties of the sub-graphs induced by active and inactive users, and show that active users tend to belong to a core that is densifying and is significantly denser than the inactive users. Further, the inactive set of users exhibit a higher density and lower conductance than the degree distribution alone can explain. These two aspects suggest that nodes at the fringe are more likely to depart and subsequent departure are correlated among neighboring nodes in tightly-knit communities.

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### Data Design for Personalization: Current Challenges and Emerging Opportunities

Personalization is central to most Internet experiences. Personalization is a data-driven process, whether the data are explicitly gathered (e.g., by asking people to fill out forms) or implicitly (e.g. through analysis of behavioral data).

It is clear that designing for effective personalization poses interesting engineering and computer science challenges. However, personalization is also a user experience issue. We believe that encouraging dialogue and collaboration between data mining experts, content providers, and user-focused researchers will offer gains in the area of personalization for search and for other domains.

This workshop is part of a larger effort we are developing: D2D: Data to Design - Design to Data.

Our vision is to provide a forum for researchers and practitioners in computer and systems sciences, data sciences, machine learning, information retrieval, interaction and interface design, and human computer interaction to interact.

Our goal is to explore issues surrounding content and presentation personalization across different devices, and to set an agenda for cross-discipline, collaborative engagement.

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### On the Embeddability of Random Walk Distances

Analysis of large graphs is critical to the ongoing growth of search engines and social networks. One class of queries centers around node affinity, often quantified by random-walk distances between node pairs, including hitting time, commute time, and personalized PageRank (PPR).

Despite the potential of these "metrics," they are rarely, if ever, used in practice, largely due to extremely high computational costs. In this paper, we investigate methods to scalably and efficiently compute random-walk distances, by "embedding" graphs and distances into points and distances in geometric coordinate spaces.

We show that while existing graph coordinate systems (GCS) can accurately estimate shortest path distances, they produce significant errors when embedding random-walk distances.

Based on our observations, we propose a new graph embedding system that explicitly accounts for per-node graph properties that affect random walk. Extensive experiments on a range of graphs show that our new approach can accurately estimate both symmetric and asymmetric random-walk distances.

Once a graph is embedded, our system can answer queries between any two nodes in 8 microseconds, orders of magnitude faster than existing methods. Finally, we show that our system produces estimates that can replace ground truth in applications with minimal impact on application output.

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### Efficient Distributed Random Walk Computation and Applications

### Probabilistic Combination of Classifier and Cluster Ensembles for Non-transductive Learning

Unsupervised models can provide supplementary soft constraints to help classify new target data under the assumption that similar objects in the target set are more likely to share the same class label. Such models can also help detect possible dierences between training and target distributions,

which is useful in applications where concept drift may take place. This paper describes a Bayesian frame work that takes as input class labels from existing classefiers (designed based on labeled data from the source domain),

as well as cluster labels from a cluster ensemble operating solely on the target data to be classified and yields a con-ensus labeling of the target data. This framework is particularly useful when the statistics of the target data drift or change from those of the training data.

We also show that the proposed framework is privacy-aware and allows performing distributed learning when data/models have sharing restrictions. Experiments show that our framework can yield superior results to those provided by applying classifier ensembles only.

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### Building a Network of E-commerce Concepts

We present a method for developing a network of e-commerce concepts. We define concepts as collection of terms that represent product entities or commerce ideas that users are interested in. We start by looking at large corpora (Billions) of historical eBay buyer queries and seller item titles.

We approach the problem of concept extraction from corpora as a market-baskets problem by adapting statistical measures of support and confidence. The concept-centric meta-data extraction pipeline is built over a map-reduce framework. We constrain the concepts to be both popular and concise.

Evaluation of our algorithm shows that high precision concept sets can be automatically mined. The system mines the full spectrum of precise e-commerce concepts ranging all the way from "ipod nano" to "I'm not a plastic bag" and from "wakizashi sword" to "mastodon skeleton".

Once the concepts are detected, they are linked into a network using different metrics of semantic similarity between concepts. This leads to a rich network of e-commerce vocabulary. Such a network of concepts can be the basis of enabling powerful applications like e-commerce search and discover as well as automatic e-commerce taxonomy generation. We present details about the extraction platform, and algorithms for segmentation of short snippets of e-commerce text as well as detection and linking of concepts.

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