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Found 15 papers, 11 papers with code
Learning Prototype Classifiers for Long-Tailed Recognition
On the other hand, Prototype classifiers do not suffer from this shortcoming and can deliver promising results simply using Nearest-Class-Mean (NCM), a special case where prototypes are empirical centroids.
Ranked #5 on
Long-tail Learning
on CIFAR-100-LT (ρ=10)
Global Counterfactual Explainer for Graph Neural Networks
One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph.
Deep Representations for Time-varying Brain Datasets
Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications.
Incorporating Heterophily into Graph Neural Networks for Graph Classification
Graph neural networks (GNNs) often assume strong homophily in graphs, seldom considering heterophily which means connected nodes tend to have different class labels and dissimilar features.
Graph Neural Diffusion Networks for Semi-supervised Learning
To solve these two issues, we propose a new graph neural network called GND-Nets (for Graph Neural Diffusion Networks) that exploits the local and global neighborhood information of a vertex in a single layer.
A Broader Picture of Random-walk Based Graph Embedding
Graph embedding based on random-walks supports effective solutions for many graph-related downstream tasks.
Event Detection on Dynamic Graphs
In this paper, we propose DyGED, a simple yet novel deep learning model for event detection on dynamic graphs.
POLE: Polarized Embedding for Signed Networks
From the 2016 U. S. presidential election to the 2021 Capitol riots to the spread of misinformation related to COVID-19, many have blamed social media for today's deeply divided society.
Combining Physics and Machine Learning for Network Flow Estimation
The flow estimation problem consists of predicting missing edge flows in a network (e. g., traffic, power and water) based on partial observations.
Learning Deep Graph Representations via Convolutional Neural Networks
The learned deep representation for a graph is a dense and low-dimensional vector that captures complex high-order interactions in a vertex neighborhood.
Incorporating User's Preference into Attributed Graph Clustering
In contrast to global clustering, local clustering aims to find only one cluster that is concentrating on the given seed vertex (and also on the designated attributes for attributed graphs).
Tree++: Truncated Tree Based Graph Kernels
At the heart of Tree++ is a graph kernel called the path-pattern graph kernel.
Learning Heuristics over Large Graphs via Deep Reinforcement Learning
Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality.
Outlier Detection from Network Data with Subnetwork Interpretation
Detecting a small number of outliers from a set of data observations is always challenging.
A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks
In this work, we introduce Social Network Distance (SND) - a distance measure that quantifies the "cost" of evolution of one snapshot of a social network into another snapshot under various models of polar opinion propagation.
