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graph is included as keyword or extra keyword in 0 datasets, 0 tools and 3 publications.

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Publications

Title Author(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Wisdom in the social crowd: An analysis of Quora Gang Wang
Gill K.
Mohanlal M.
Hua Zheng
Zhao B.Y.
WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web English 2013 Efforts such as Wikipedia have shown the ability of user communities to collect, organize and curate information on the Internet. Recently, a number of question and answer (Q&A) sites have successfully built large growing knowledge repositories, each driven by a wide range of questions and answers from its users community. While sites like Yahoo Answers have stalled and begun to shrink, one site still going strong is Quora, a rapidly growing service that augments a regular Q&A system with social links between users. Despite its success, however, little is known about what drives Quora's growth, and how it continues to connect visitors and experts to the right questions as it grows. In this paper, we present results of a detailed analysis of Quora using measurements. We shed light on the impact of three different connection networks (or graphs) inside Quora, a graph connecting topics to users, a social graph connecting users, and a graph connecting related questions. Our results show that heterogeneity in the user and question graphs are significant contributors to the quality of Quora's knowledge base. One drives the attention and activity of users, and the other directs them to a small set of popular and interesting questions. Copyright is held by the International World Wide Web Conference Committee (IW3C2). 0 0
Exploring simultaneous keyword and key sentence extraction: Improve graph-based ranking using Wikipedia Xiaolong Wang
Lei Wang
Jing-Woei Li
Li S.
ACM International Conference Proceeding Series English 2012 Summarization and Keyword Selection are two important tasks in NLP community. Although both aim to summarize the source articles, they are usually treated separately by using sentences or words. In this paper, we propose a two-level graph based ranking algorithm to generate summarization and extract keywords at the same time. Previous works have reached a consensus that important sentence is composed by important keywords. In this paper, we further study the mutual impact between them through context analysis. We use Wikipedia to build a two-level concept-based graph, instead of traditional term-based graph, to express their homogenous relationship and heterogeneous relationship. We run PageRank and HITS rank on the graph to adjust both homogenous and heterogeneous relationships. A more reasonable relatedness value will be got for key sentence selection and keyword selection. We evaluate our algorithm on TAC 2011 data set. Traditional term-based approach achieves a score of 0.255 in ROUGE-1 and a score of 0.037 and ROUGE-2 and our approach can improve them to 0.323 and 0.048 separately. 0 0
Us vs. Them: Understanding social dynamics in wikipedia with revert graph visualizations Bongwon Suh
Chi E.H.
Pendleton B.A.
Aniket Kittur
VAST IEEE Symposium on Visual Analytics Science and Technology 2007, Proceedings English 2007 Wikipedia is a wiki-based encyclopedia that has become one of the most popular collaborative on-line knowledge systems. As in any large collaborative system, as Wikipedia has grown, conflicts and coordination costs have increased dramatically. Visual analytic tools provide a mechanism for addressing these issues by enabling users to more quickly and effectively make sense of the status of a collaborative environment. In this paper we describe a model for identifying patterns of conflicts in Wikipedia articles. The model relies on users' editing history and the relationships between user edits, especially revisions that void previous edits, known as "reverts". Based on this model, we constructed Revert Graph, a tool that visualizes the overall conflict patterns between groups of users. It enables visual analysis of opinion groups and rapid interactive exploration of those relationships via detail drill-downs. We present user patterns and case studies that show the effectiveness of these techniques, and discuss how they could generalize to other systems. 0 4