Search Results for author: Minji Yoon

Found 7 papers, 4 papers with code

Scalable Privacy-enhanced Benchmark Graph Generative Model for Graph Convolutional Networks

no code implementations10 Jul 2022 Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Ruslan Salakhutdinov

A surge of interest in Graph Convolutional Networks (GCN) has produced thousands of GCN variants, with hundreds introduced every year.

Graph Generation Node Classification

A Dataset on Malicious Paper Bidding in Peer Review

1 code implementation24 Jun 2022 Steven Jecmen, Minji Yoon, Vincent Conitzer, Nihar B. Shah, Fei Fang

The performance of these detection algorithms can be taken as a baseline for future research on detecting malicious bidding.

Zero-shot Transfer Learning on Heterogeneous Graphs via Knowledge Transfer Networks

no code implementations3 Mar 2022 Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi

We propose a zero-shot transfer learning module for HGNNs called a Knowledge Transfer Network (KTN) that transfers knowledge from label-abundant node types to zero-labeled node types through rich relational information given in the HG.

Domain Adaptation Graph Learning +1

Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off

1 code implementation26 Nov 2020 Minji Yoon, Théophile Gervet, Bryan Hooi, Christos Faloutsos

We first define a unified framework UNIFIEDGM that integrates various message-passing based graph algorithms, ranging from conventional algorithms like PageRank to graph neural networks.

Graph Mining Unity

Real-Time Anomaly Detection in Edge Streams

3 code implementations17 Sep 2020 Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?

Anomaly Detection Anomaly Detection in Edge Streams

MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

3 code implementations11 Nov 2019 Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?

Anomaly Detection in Edge Streams

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