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Found 29 papers, 11 papers with code
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties.
Graph Neural Machine: A New Model for Learning with Tabular Data
We then propose a new machine learning model for tabular data, the so-called Graph Neural Machine (GNM), which replaces the MLP's directed acyclic graph with a nearly complete graph and which employs a synchronous message passing scheme.
Time-Parameterized Convolutional Neural Networks for Irregularly Sampled Time Series
We evaluate TPCNN on both interpolation and classification tasks involving real-world irregularly sampled multivariate time series datasets.
Supervised Attention Using Homophily in Graph Neural Networks
Among the different variants of graph neural networks, graph attention networks (GATs) have been applied with great success to different tasks.
Path Neural Networks: Expressive and Accurate Graph Neural Networks
We derive three different variants of the PathNN model that aggregate single shortest paths, all shortest paths and all simple paths of length up to K. We prove that two of these variants are strictly more powerful than the 1-WL algorithm, and we experimentally validate our theoretical results.
Ranked #7 on
Graph Classification
on Peptides-func
What Do GNNs Actually Learn? Towards Understanding their Representations
In recent years, graph neural networks (GNNs) have achieved great success in the field of graph representation learning.
Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations
In recent years, graph neural networks (GNNs) have emerged as a promising tool for solving machine learning problems on graphs.
Time Series Forecasting Models Copy the Past: How to Mitigate
Recently neural network architectures have been widely applied to the problem of time series forecasting.
Image Keypoint Matching using Graph Neural Networks
Image matching is a key component of many tasks in computer vision and its main objective is to find correspondences between features extracted from different natural images.
NLP Research and Resources at DaSciM, Ecole Polytechnique
DaSciM (Data Science and Mining) part of LIX at Ecole Polytechnique, established in 2013 and since then producing research results in the area of large scale data analysis via methods of machine and deep learning.
Permute Me Softly: Learning Soft Permutations for Graph Representations
In this paper, we propose a new graph neural network model, so-called $\pi$-GNN which learns a "soft" permutation (i. e., doubly stochastic) matrix for each graph, and thus projects all graphs into a common vector space.
Geometric Random Walk Graph Neural Networks via Implicit Layers
The proposed model retains the transparency of Random Walk Graph Neural Networks since its first layer also consists of a number of trainable ``hidden graphs'' which are compared against the input graphs using the geometric random walk kernel.
Ego-based Entropy Measures for Structural Representations on Graphs
Machine learning on graph-structured data has attracted high research interest due to the emergence of Graph Neural Networks (GNNs).
An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks
Graph neural networks and graph kernels have achieved great success in solving machine learning problems on graphs.
Random Walk Graph Neural Networks
The first layer of the model consists of a number of trainable ``hidden graphs'' which are compared against the input graphs using a random walk kernel to produce graph representations.
Transfer Graph Neural Networks for Pandemic Forecasting
Furthermore, to account for the limited amount of training data, we capitalize on the pandemic's asynchronous outbreaks across countries and use a model-agnostic meta-learning based method to transfer knowledge from one country's model to another's.
EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs
Then, we employ a generative model which predicts the topology of the graph at the next time step and constructs a graph instance that corresponds to that topology.
Ego-based Entropy Measures for Structural Representations
Moreover, on graph classification tasks, we suggest the utilization of the generated structural embeddings for the transformation of an attributed graph structure into a set of augmented node attributes.
Message Passing Attention Networks for Document Understanding
In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD).
Ranked #1 on
Multi-Modal Document Classification
on Reuters-21578
document understanding
Multi-Modal Document Classification
+2
k-hop Graph Neural Networks
We show that the proposed architecture can identify fundamental graph properties.
Graph Kernels: A Survey
Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data.
Ranked #11 on
Graph Classification
on NCI1
Rep the Set: Neural Networks for Learning Set Representations
In several domains, data objects can be decomposed into sets of simpler objects.
Ranked #1 on
Document Classification
on Twitter
Message Passing Graph Kernels
The first component is a kernel between vertices, while the second component is a kernel between graphs.
GraKeL: A Graph Kernel Library in Python
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.
Kernel Graph Convolutional Neural Nets
Graph kernels have been successfully applied to many graph classification problems.
Kernel Graph Convolutional Neural Networks
Graph kernels have been successfully applied to many graph classification problems.
Shortest-Path Graph Kernels for Document Similarity
In this paper, we present a novel document similarity measure based on the definition of a graph kernel between pairs of documents.
Graph Classification with 2D Convolutional Neural Networks
Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations.
Ranked #3 on
Graph Classification
on RE-M12K
Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization
Recently, there has been a lot of activity in learning distributed representations of words in vector spaces.
