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Projects: “Community Wikipedias: A MachineHuman Partnership Approach ” and
, 2007
"... “Managing Unstructured Data using Information Extraction, Information Integration, ..."
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“Managing Unstructured Data using Information Extraction, Information Integration,
First Class Honors
, 2008
"... Proposed principles, developed solutions, and implemented a software platform for enabling efficient user feedback into structured Web databases. Adopted the platform to build the feedbackenabled version of DBLife, a Web portal for the database research community: • Designed, implemented, and optim ..."
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Proposed principles, developed solutions, and implemented a software platform for enabling efficient user feedback into structured Web databases. Adopted the platform to build the feedbackenabled version of DBLife, a Web portal for the database research community: • Designed, implemented, and optimized a temporal entityrelationship database. • Designed and implemented a Webbased user interface for efficient user interactions. • Developed mechanisms for synchronizing different user feedback. Teaching Assistant, University of WisconsinMadison 2007 – 2008 Instructed a class of thirty students of the course “Introduction to Programming”.
Measuring qualities of articles contributed by online communities
 In Proc. of WI’06
, 2006
"... Using open source Web editing software (e.g., wiki), online community users can now easily edit, review and publish articles collaboratively. While much useful knowledge can be derived from these articles, content users and critics are often concerned about their qualities. In this paper, we develop ..."
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Cited by 17 (5 self)
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Using open source Web editing software (e.g., wiki), online community users can now easily edit, review and publish articles collaboratively. While much useful knowledge can be derived from these articles, content users and critics are often concerned about their qualities. In this paper, we develop two models, namely basic model and peer review model, for measuring the qualities of these articles and the authorities of their contributors. We represent collaboratively edited articles and their contributors in a bipartite graph. While the basic model measures an article’s quality using both the authorities of contributors and the amount of contribution from each contributor, the peer review model extends the former by considering the review aspect of article content. We present results of experiments conducted on some Wikipedia pages and their contributors. Our result show that the two models can effectively determine the articles’ qualities and contributors ’ authorities using the collaborative nature of online communities.
SharedStorage Auction
, 2004
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
C.: Expertise Matching via ConstraintBased Optimization
 In: Proc. of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
, 2010
"... Abstract—Expertise matching, aiming to find the alignment between experts and queries, is a common problem in many real applications such as conference paperreviewer assignment, productreviewer alignment, and productendorser matching. Most of existing methods for this problem usually find “relev ..."
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Cited by 4 (0 self)
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Abstract—Expertise matching, aiming to find the alignment between experts and queries, is a common problem in many real applications such as conference paperreviewer assignment, productreviewer alignment, and productendorser matching. Most of existing methods for this problem usually find “relevant ” experts for each query independently by using, e.g., an information retrieval method. However, in realworld systems, various domainspecific constraints must be considered. For example, to review a paper, it is desirable that there is at least one senior reviewer to guide the reviewing process. An important question is: “Can we design a framework to efficiently find the optimal solution for expertise matching under various constraints? ” This paper explores such an approach by formulating the expertise matching problem in a constraintbased optimization framework. Interestingly, the problem can be linked to a convex cost flow problem, which guarantees an optimal solution under given constraints. We also present an online matching algorithm to support incorporating user feedbacks in real time. The proposed approach has been evaluated on two different genres of expertise matching problems. Experimental results validate the effectiveness of the proposed approach. KeywordsExpertise matching; Constrained optimization; Paperreviewer assignment
Compression of Weighted Graphs
"... We propose to compress weighted graphs (networks), motivated by the observation that large networks of social, biological, or other relations can be complex to handle and visualize. In the process also known as graph simplification, nodes and (unweighted) edges are grouped to supernodes and superedg ..."
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Cited by 9 (1 self)
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We propose to compress weighted graphs (networks), motivated by the observation that large networks of social, biological, or other relations can be complex to handle and visualize. In the process also known as graph simplification, nodes and (unweighted) edges are grouped to supernodes and superedges, respectively, to obtain a smaller graph. We propose models and algorithms for weighted graphs. The interpretation (i.e. decompression) of a compressed, weighted graph is that a pair of original nodes is connected by an edge if their supernodes are connected by one, and that the weight of an edge is approximated to be the weight of the superedge. The compression problem now consists of choosing supernodes, superedges, and superedge weights so that the approximation error is minimized while the amount of compression is maximized. In this paper, we formulate this task as the ’simple weighted graph compression problem’. We then propose a much wider class of tasks under the name of ’generalized weighted graph compression problem’. The generalized task extends the optimization to preserve longerrange connectivities between nodes, not just individual edge weights. We study the properties of these problems and propose a range of algorithms to solve them, with different balances between complexity and quality of the result. We evaluate the problems and algorithms experimentally on real networks. The results indicate that weighted graphs can be compressed efficiently with relatively little compression error.
Guided Learning for Role Discovery (GLRD): Framework, Algorithms, and Applications
 KDD'13
, 2013
"... Role discovery in graphs is an emerging area that allows analysis of complex graphs in an intuitive way. In contrast to community discovery, which finds groups of highly connected nodes, role discovery finds groups of nodes that share similar topological structure in the graph, and hence a common ro ..."
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Cited by 4 (0 self)
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Role discovery in graphs is an emerging area that allows analysis of complex graphs in an intuitive way. In contrast to community discovery, which finds groups of highly connected nodes, role discovery finds groups of nodes that share similar topological structure in the graph, and hence a common role (or function) such as being a broker or a periphery node. However, existing work so far is completely unsupervised, which is undesirable for a number of reasons. We provide an alternating least squares framework that allows convex constraints to be placed on the role discovery problem, which can provide useful supervision. In particular we explore supervision to enforce i) sparsity, ii) diversity, and iii) alternativeness in the roles. We illustrate the usefulness of this supervision on various data sets and applications.
Generative Models for Item Adoptions Using Social Correlation
, 2013
"... Users face many choices on the Web when it comes to choosing which product to buy, which video to watch, etc. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation which may be caused by the selection and social influe ..."
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Users face many choices on the Web when it comes to choosing which product to buy, which video to watch, etc. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation which may be caused by the selection and social influence effects. In this chapter, we focus on modeling social correlation on users item adoptions. Given a useruser social graph and an itemuser adoption graph, our research seeks to answer the following questions: whether the items adopted by a user correlate to items adopted by her friends, and how to model item adoptions using social correlation. We propose a social correlation measure that considers the degree of correlation from every user to the users friends, in addition to a set of latent factors representing topics of interests of individual users. We develop two generative models, namely sequential and unified, and
2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Expertise Matching via Constraintbased Optimization
"... Abstract—Expertise matching, aiming to find the alignment between experts and queries, is a common problem in many real applications such as conference paperreviewer assignment, productreviewer alignment, and productendorser matching. Most of existing methods for this problem usually find “releva ..."
Abstract
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Abstract—Expertise matching, aiming to find the alignment between experts and queries, is a common problem in many real applications such as conference paperreviewer assignment, productreviewer alignment, and productendorser matching. Most of existing methods for this problem usually find “relevant ” experts for each query independently by using, e.g., an information retrieval method. However, in realworld systems, various domainspecific constraints must be considered. For example, to review a paper, it is desirable that there is at least one senior reviewer to guide the reviewing process. An important question is: “Can we design a framework to efficiently find the optimal solution for expertise matching under various constraints? ” This paper explores such an approach by formulating the expertise matching problem in a constraintbased optimization framework. Interestingly, the problem can be linked to a convex cost flow problem, which guarantees an optimal solution under given constraints. We also present an online matching algorithm to support incorporating user feedbacks in real time. The proposed approach has been evaluated on two different genres of expertise matching problems. Experimental results validate the effectiveness of the proposed approach. KeywordsExpertise matching; Constrained optimization; Paperreviewer assignment
Probabilistic Latent Document Network Embedding
"... Abstract—A document network refers to a data type that can be represented as a graph of vertices, where each vertex is associated with a text document. Examples of such a data type include hyperlinked Web pages, academic publications with citations, and user profiles in social networks. Such data ha ..."
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Abstract—A document network refers to a data type that can be represented as a graph of vertices, where each vertex is associated with a text document. Examples of such a data type include hyperlinked Web pages, academic publications with citations, and user profiles in social networks. Such data have very highdimensional representations, in terms of text as well as network connectivity. In this paper, we study the problem of embedding, or finding a lowdimensional representation of a document network that “preserves ” the data as much as possible. These embedded representations are useful for various applications driven by dimensionality reduction, such as visualization or feature selection. While previous works in embedding have mostly focused on either the textual aspect or the network aspect, we advocate a holistic approach by finding a unified lowrank representation for both aspects. Moreover, to lend semantic inter
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