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09/19/2023
TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions
Many real-world datasets are represented as tensors, i.e., multi-dimensi...
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08/21/2023
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs
Real-world graphs are dynamic, constantly evolving with new interactions...
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06/10/2023
On Improving the Cohesiveness of Graphs by Merging Nodes: Formulation, Analysis, and Algorithms
Graphs are a powerful mathematical model, and they are used to represent...
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06/05/2023
Classification of Edge-dependent Labels of Nodes in Hypergraphs
A hypergraph is a data structure composed of nodes and hyperedges, where...
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06/04/2023
Towards Deep Attention in Graph Neural Networks: Problems and Remedies
Graph neural networks (GNNs) learn the representation of graph-structure...
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05/16/2023
Interplay between Topology and Edge Weights in Real-World Graphs: Concepts, Patterns, and an Algorithm
What are the relations between the edge weights and the topology in real...
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02/10/2023
Characterization of Simplicial Complexes by Counting Simplets Beyond Four Nodes
Simplicial complexes are higher-order combinatorial structures which hav...
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02/09/2023
NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors
Many real-world data are naturally represented as a sparse reorderable m...
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01/20/2023
Hypercore Decomposition for Non-Fragile Hyperedges: Concepts, Algorithms, Observations, and Applications
Hypergraphs are a powerful abstraction for modeling high-order relations...
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01/01/2023
Graphlets over Time: A New Lens for Temporal Network Analysis
Graphs are widely used for modeling various types of interactions, such ...
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12/05/2022
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition
The Weather4Cast competition (hosted by NeurIPS 2022) required competito...
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11/26/2022
BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning
Continual Learning (CL) is the process of learning ceaselessly a sequenc...
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10/20/2022
Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data
Recently, many deep-learning techniques have been applied to various wea...
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10/07/2022
Set2Box: Similarity Preserving Representation Learning of Sets
Sets have been used for modeling various types of objects (e.g., a docum...
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06/09/2022
I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs
Although machine learning on hypergraphs has attracted considerable atte...
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04/29/2022
HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams
Sequences of group interactions, such as emails, online discussions, and...
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04/13/2022
AHP: Learning to Negative Sample for Hyperedge Prediction
Hypergraphs (i.e., sets of hyperedges) naturally represent group relatio...
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03/29/2022
Are Edge Weights in Summary Graphs Useful? – A Comparative Study
Which one is better between two representative graph summarization model...
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03/28/2022
Personalized Graph Summarization: Formulation, Scalable Algorithms, and Applications
Are users of an online social network interested equally in all connecti...
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03/19/2022
Meta-Learning for Online Update of Recommender Systems
Online recommender systems should be always aligned with users' current ...
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02/17/2022
Effective Training Strategies for Deep-learning-based Precipitation Nowcasting and Estimation
Deep learning has been successfully applied to precipitation nowcasting....
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01/04/2022
On the Persistence of Higher-Order Interactions in Real-World Hypergraphs
A hypergraph is a generalization of an ordinary graph, and it naturally ...
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12/10/2021
SLUGGER: Lossless Hierarchical Summarization of Massive Graphs
Given a massive graph, how can we exploit its hierarchical structure for...
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10/28/2021
Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs
A variety of tasks on dynamic graphs, including anomaly detection, commu...
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06/11/2021
Learning to Pool in Graph Neural Networks for Extrapolation
Graph neural networks (GNNs) are one of the most popular approaches to u...
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02/23/2021
SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams
Consider traffic data (i.e., triplets in the form of source-destination-...
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02/16/2021
Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers
Consider multiple seasonal time series being collected in real-time, in ...
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11/26/2020
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
Given a dynamic graph stream, how can we detect the sudden appearance of...
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11/10/2020
Two-stage Training of Graph Neural Networks for Graph Classification
Graph Neural Networks (GNNs) have received massive attention in the fiel...
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09/17/2020
Real-Time Streaming Anomaly Detection in Dynamic Graphs
Given a stream of graph edges from a dynamic graph, how can we assign an...
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06/17/2020
Incremental Lossless Graph Summarization
Given a fully dynamic graph, represented as a stream of edge insertions ...
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06/01/2020
SSumM: Sparse Summarization of Massive Graphs
Given a graph G and the desired size k in bits, how can we summarize G w...
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03/04/2020
Hypergraph Motifs: Concepts, Algorithms, and Discoveries
Hypergraphs naturally represent group interactions, which are omnipresen...
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01/30/2020
How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction
Hypergraphs provide a natural way of representing group relations, whose...
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01/24/2020
MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Social Networks
Influence maximization (IM) is one of the most important problems in soc...
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11/11/2019
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
Given a stream of graph edges from a dynamic graph, how can we assign an...
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02/12/2018
DiSLR: Distributed Sampling with Limited Redundancy For Triangle Counting in Graph Streams
Given a web-scale graph that grows over time, how should its edges be st...
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02/04/2018
Out-of-Core and Distributed Algorithms for Dense Subtensor Mining
How can we detect fraudulent lockstep behavior in large-scale multi-aspe...
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09/10/2017
WRS: Waiting Room Sampling for Accurate Triangle Counting in Real Graph Streams
If we cannot store all edges in a graph stream, which edges should we st...
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06/11/2017
DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams
Consider a stream of retweet events - how can we spot fraudulent lock-st...
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05/20/2017
