most citedExploring Communities in Large Profiled Graphs

20 citations · 25 across the 5 of their papers we have counts for

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cs.DB2019

A General Early-Stopping Module for Crowdsourced Ranking

Caihua Shan, Leong Hou U, Nikos Mamoulis +2

Crowdsourcing can be used to determine a total order for an object set (e.g., the top-10 NBA players) based on crowd opinions. This ranking problem is often decomposed into a set o…

cs.DB2019

Efficient Algorithms for Densest Subgraph Discovery

Yixiang Fang, Kaiqiang Yu, Reynold Cheng +2

Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been studied for decades, and is widely used in various areas, including network science, biologic…

cs.DB2019

A Survey of Community Search Over Big Graphs

Yixiang Fang, Xin Huang, Lu Qin +4

With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component…

cs.DB20192 cited

Detecting Data Errors with Statistical Constraints

Jing Nathan Yan, Oliver Schulte, Jiannan Wang +1

A powerful approach to detecting erroneous data is to check which potentially dirty data records are incompatible with a user's domain knowledge. Previous approaches allow the user…

cs.DB201920 cited

Exploring Communities in Large Profiled Graphs

Yankai Chen, Yixiang Fang, Reynold Cheng +3

Given a graph and a vertex , the community search (CS) problem aims to efficiently find a subgraph of whose vertices are closely related to . Communities are pre…

cs.DB2017

T-Crowd: Effective Crowdsourcing for Tabular Data

Caihua Shan, Nikos Mamoulis, Guoliang Li +3

Crowdsourcing employs human workers to solve computer-hard problems, such as data cleaning, entity resolution, and sentiment analysis. When crowdsourcing tabular data, e.g., the at…