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06/26/2023
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks
Brain graphs, which model the structural and functional relationships be...
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06/08/2023
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks
Research on GNNs has highlighted a relationship between high homophily (...
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06/01/2023
SpotTarget: Rethinking the Effect of Target Edges for Link Prediction in Graph Neural Networks
Graph Neural Networks (GNNs) have demonstrated promising outcomes across...
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05/24/2023
Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective
We investigate the question of whether the knowledge learned by graph ne...
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05/17/2023
Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation
Distributed training of GNNs enables learning on massive graphs (e.g., s...
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08/23/2022
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment
Network alignment, or the task of finding corresponding nodes in differe...
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07/10/2022
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods
We study the task of node classification for graph neural networks (GNNs...
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07/04/2022
Learning node embeddings via summary graphs: a brief theoretical analysis
Graph representation learning plays an important role in many graph mini...
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11/09/2021
Convolutional Neural Network Dynamics: A Graph Perspective
The success of neural networks (NNs) in a wide range of applications has...
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11/05/2021
Augmentations in Graph Contrastive Learning: Current Methodological Flaws Towards Better Practices
Graph classification has applications in bioinformatics, social sciences...
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10/27/2021
Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation
Multi-source entity linkage focuses on integrating knowledge from multip...
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06/14/2021
Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs
Recent studies have exposed that many graph neural networks (GNNs) are s...
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04/12/2021
Relational world knowledge representation in contextual language models: A review
Relational knowledge bases (KBs) are established tools for world knowled...
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02/26/2021
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding
While most network embedding techniques model the relative positions of ...
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02/15/2021
A Hidden Challenge of Link Prediction: Which Pairs to Check?
The traditional setup of link prediction in networks assumes that a test...
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02/12/2021
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
Most graph neural networks (GNN) perform poorly in graphs where neighbor...
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11/15/2020
Generating Negative Commonsense Knowledge
The acquisition of commonsense knowledge is an important open challenge ...
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09/28/2020
Graph Neural Networks with Heterophily
Graph Neural Networks (GNNs) have proven to be useful for many different...
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09/21/2020
From Static to Dynamic Node Embeddings
We introduce a general framework for leveraging graph stream data for te...
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09/16/2020
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
We present CoDEx, a set of knowledge graph completion datasets extracted...
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07/30/2020
G-CREWE: Graph CompREssion With Embedding for Network Alignment
Network alignment is useful for multiple applications that require incre...
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06/27/2020
Mining Persistent Activity in Continually Evolving Networks
Frequent pattern mining is a key area of study that gives insights into ...
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06/20/2020
Generalizing Graph Neural Networks Beyond Homophily
We investigate the representation power of graph neural networks in the ...
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06/15/2020
Neural Execution Engines: Learning to Execute Subroutines
A significant effort has been made to train neural networks that replica...
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05/10/2020
Consistent Network Alignment with Node Embedding
Network alignment, the process of finding correspondences between nodes ...
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04/02/2020
Improving the Utility of Knowledge Graph Embeddings with Calibration
This paper addresses machine learning models that embed knowledge graph ...
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03/23/2020
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization
Knowledge graphs (KGs) store highly heterogeneous information about the ...
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02/23/2020
Driving with Data in the Motor City: Mining and Modeling Vehicle Fleet Maintenance Data
The City of Detroit maintains an active fleet of over 2500 vehicles, spe...
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08/22/2019
From Community to Role-based Graph Embeddings
Roles are sets of structurally similar nodes that are more similar to no...
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04/18/2019
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching
Identity stitching, the task of identifying and matching various online ...
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05/03/2018
t-PINE: Tensor-based Predictable and Interpretable Node Embeddings
Graph representations have increasingly grown in popularity during the l...
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05/03/2018
SURREAL: SUbgraph Robust REpresentAtion Learning
The success of graph embeddings or node representation learning in a var...
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10/18/2017
Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit
The City of Detroit maintains an active fleet of over 2500 vehicles, spe...
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06/27/2014