activity
20122018
most citedActive Learning for Graph Embedding

43 citations · 60 across the 5 of their papers we have counts for

collaborators

6 papers

cs.HC2018

Characterizing Scalability Issues in Spreadsheet Software using Online Forums

Kelly Mack, John Lee, Kevin Chang +2

In traditional usability studies, researchers talk to users of tools to understand their needs and challenges. Insights gained via such interviews offer context, detail, and backgr…

cs.LG201715 cited

Topological Recurrent Neural Network for Diffusion Prediction

Jia Wang, Vincent W. Zheng, Zemin Liu +1

In this paper, we study the problem of using representation learning to assist information diffusion prediction on graphs. In particular, we aim at estimating the probability of an…

cs.SI20172 cited

Relationship Profiling over Social Networks: Reverse Smoothness from Similarity to Closeness

Carl Yang, Kevin Chen-Chuan Chang

On social networks, while nodes bear rich attributes, we often lack the `semantics' of why each link is formed-- and thus we are missing the `road signs' to navigate and organize t…

cs.DB2017

Towards a Holistic Integration of Spreadsheets with Databases: A Scalable Storage Engine for Presentational Data Management

Mangesh Bendre, Vipul Venkataraman, Xinyan Zhou +2

Spreadsheet software is the tool of choice for interactive ad-hoc data management, with adoption by billions of users. However, spreadsheets are not scalable, unlike database syste…

cs.LG201743 cited

Active Learning for Graph Embedding

Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang

Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the…

cs.DB2012

Multiple Location Profiling for Users and Relationships from Social Network and Content

Rui Li, Shengjie Wang, Kevin Chen-Chuan Chang

Users' locations are important for many applications such as personalized search and localized content delivery. In this paper, we study the problem of profiling Twitter users' loc…