activity
20152022
most citedTowards Plug-and-Play Visual Graph Query Interfaces: Data-driven Canned Pattern Selection for Large Networks

11 citations · 22 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV202210 cited

Autoregressive Image Generation using Residual Quantization

Doyup Lee, Chiheon Kim, Saehoon Kim +2

For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for a…

cs.DB202111 cited

Towards Plug-and-Play Visual Graph Query Interfaces: Data-driven Canned Pattern Selection for Large Networks

Zifeng Yuan, Huey Eng Chua, Sourav S Bhowmick +3

Canned patterns (i.e. small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode.…

cs.DB20211 cited

Symmetric Continuous Subgraph Matching with Bidirectional Dynamic Programming

Seunghwan Min, Sung Gwan Park, Kunsoo Park +3

In many real datasets such as social media streams and cyber data sources, graphs change over time through a graph update stream of edge insertions and deletions. Detecting critica…

cs.DB2021

[Technical Report] Combining Sampling and Synopses with Worst-Case Optimal Runtime and Quality Guarantees for Graph Pattern Cardinality Estimation

Kyoungmin Kim, Hyeonji Kim, George Fletcher +1

Graph pattern cardinality estimation is the problem of estimating the number of embeddings of a query graph in a data graph. This fundamental problem arises, for example, during qu…

cs.DB2019

Fast and Robust Distributed Subgraph Enumeration

Xuguang Ren, Junhu Wang, Wook-Shin Han +1

We study the classic subgraph enumeration problem under distributed settings. Existing solutions either suffer from severe memory crisis or rely on large indexes, which makes them…

cs.DB2015

Taming Subgraph Isomorphism for RDF Query Processing

Jinha Kim, Hyungyu Shin, Wook-Shin Han +2

RDF data are used to model knowledge in various areas such as life sciences, Semantic Web, bioinformatics, and social graphs. The size of real RDF data reaches billions of triples.…