Search Results for author: Kijung Shin

Found 19 papers, 12 papers with code

Learning to Pool in Graph Neural Networks for Extrapolation

no code implementations11 Jun 2021 Jihoon Ko, Taehyung Kwon, Kijung Shin, Juho Lee

However, according to a recent study, a careful choice of pooling functions, which are used for the aggregation and readout operations in GNNs, is crucial for enabling GNNs to extrapolate.

SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams

1 code implementation23 Feb 2021 Taehyung Kwon, Inkyu Park, Dongjin Lee, Kijung Shin

SLICENSTITCH changes the starting point of each period adaptively, based on the current time, and updates factor matrices (i. e., outputs of CP decomposition) instantly as new data arrives.

Anomaly Detection Recommendation Systems +1

Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers

1 code implementation16 Feb 2021 Dongjin Lee, Kijung Shin

Consider multiple seasonal time series being collected in real-time, in the form of a tensor stream.

Imputation Time Series

Two-stage Training of Graph Neural Networks for Graph Classification

1 code implementation10 Nov 2020 Manh Tuan Do, Noseong Park, Kijung Shin

By adapting five GNN models to our method, we demonstrate the consistent improvement in accuracy and utilization of each GNN's allocated capacity over the original training method of each model up to 5. 4\% points in 12 datasets.

Classification General Classification +3

Summarizing graphs using configuration model

no code implementations19 Oct 2020 Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, HuaWei Shen, Xueqi Cheng

As a solution, graph summarization, which aims to find a compact representation that preserves the important properties of a given graph, has received much attention, and numerous algorithms have been developed for it.

Social and Information Networks

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

Evolution of Real-world Hypergraphs: Patterns and Models without Oracles

1 code implementation28 Aug 2020 Yunbum Kook, Jihoon Ko, Kijung Shin

What kind of macroscopic structural and dynamical patterns can we observe in real-world hypergraphs?

Social and Information Networks

Structural Patterns and Generative Models of Real-world Hypergraphs

no code implementations12 Jun 2020 Manh Tuan Do, Se-eun Yoon, Bryan Hooi, Kijung Shin

Graphs have been utilized as a powerful tool to model pairwise relationships between people or objects.

Social and Information Networks Physics and Society

SSumM: Sparse Summarization of Massive Graphs

1 code implementation1 Jun 2020 Kyuhan Lee, Hyeonsoo Jo, Jihoon Ko, Sungsu Lim, Kijung Shin

SSumM not only merges nodes together but also sparsifies the summary graph, and the two strategies are carefully balanced based on the minimum description length principle.

Databases Social and Information Networks H.2.8

Hypergraph Motifs: Concepts, Algorithms, and Discoveries

1 code implementation4 Mar 2020 Geon Lee, Jihoon Ko, Kijung Shin

(Q3) how can we identify domains which hypergraphs are from?

Social and Information Networks Databases Data Structures and Algorithms H.2.8

How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction

no code implementations30 Jan 2020 Se-eun Yoon, HyungSeok Song, Kijung Shin, Yung Yi

Hypergraphs provide a natural way of representing group relations, whose complexity motivates an extensive array of prior work to adopt some form of abstraction and simplification of higher-order interactions.

Hyperedge Prediction Link Prediction

MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks

1 code implementation24 Jan 2020 Jihoon Ko, Kyuhan Lee, Kijung Shin, Noseong Park

In this work, we present an inductive machine learning method, called Monte Carlo Simulator (MONSTOR), for estimating the influence of given seed nodes in social networks unseen during training.

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

Out-of-Core and Distributed Algorithms for Dense Subtensor Mining

1 code implementation4 Feb 2018 Kijung Shin, Bryan Hooi, Jisu Kim, Christos Faloutsos

Can we detect it when data are too large to fit in memory or even on a disk?

Databases Distributed, Parallel, and Cluster Computing Social and Information Networks H.2.8

WRS: Waiting Room Sampling for Accurate Triangle Counting in Real Graph Streams

1 code implementation10 Sep 2017 Kijung Shin

WRS exploits the temporal locality by always storing the most recent edges, which future edges are more likely to form triangles with, in the waiting room, while it uses reservoir sampling for the remaining edges.

Databases Data Structures and Algorithms H.2.8; G.2.2

Why You Should Charge Your Friends for Borrowing Your Stuff

no code implementations20 May 2017 Kijung Shin, Euiwoong Lee, Dhivya Eswaran, Ariel D. Procaccia

We consider goods that can be shared with k-hop neighbors (i. e., the set of nodes within k hops from an owner) on a social network.

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