Search Results for author:
Found 17 papers, 8 papers with code
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries.
Time-varying Signals Recovery via Graph Neural Networks
The recovery of time-varying graph signals is a fundamental problem with numerous applications in sensor networks and forecasting in time series.
Higher-order Sparse Convolutions in Graph Neural Networks
Graph Neural Networks (GNNs) have been applied to many problems in computer sciences.
On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks
Graph Neural Networks (GNNs) have succeeded in various computer science applications, yet deep GNNs underperform their shallow counterparts despite deep learning's success in other domains.
Multiple Similarity Drug-Target Interaction Prediction with Random Walks and Matrix Factorization
Moreover, the validation of highly ranked non-interacting pairs also demonstrates the potential of MDMF2A to discover novel DTIs.
Topic-aware latent models for representation learning on networks
Network representation learning (NRL) methods have received significant attention over the last years thanks to their success in several graph analysis problems, including node classification, link prediction, and clustering.
Maximizing Influence with Graph Neural Networks
Finding the seed set that maximizes the influence spread over a network is a well-known NP-hard problem.
Multiple Kernel Representation Learning on Networks
In particular, we propose a weighted matrix factorization model that encodes random walk-based information about nodes of the network.
GraphSVX: Shapley Value Explanations for Graph Neural Networks
Graph Neural Networks (GNNs) achieve significant performance for various learning tasks on geometric data due to the incorporation of graph structure into the learning of node representations, which renders their comprehension challenging.
NodeSig: Binary Node Embeddings via Random Walk Diffusion
Graph Representation Learning (GRL) has become a key paradigm in network analysis, with a plethora of interdisciplinary applications.
Exponential Family Graph Embeddings
We introduce the generic \textit{exponential family graph embedding} model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions.
Kernel Node Embeddings
Learning representations of nodes in a low dimensional space is a crucial task with many interesting applications in network analysis, including link prediction and node classification.
Multi-task Learning for Influence Estimation and Maximization
The first part of our methodology is a multi-task neural network that learns embeddings of nodes that initiate cascades (influencer vectors) and embeddings of nodes that participate in them (susceptible vectors).
TNE: A Latent Model for Representation Learning on Networks
Although various approaches have been proposed to compute node embeddings, many successful methods benefit from random walks in order to transform a given network into a collection of sequences of nodes and then they target to learn the representation of nodes by predicting the context of each vertex within the sequence.
BiasedWalk: Biased Sampling for Representation Learning on Graphs
We have performed a detailed experimental evaluation comparing the performance of the proposed algorithm against various baseline methods, on several datasets and learning tasks.
Perturb and Combine to Identify Influential Spreaders in Real-World Networks
Some of the most effective influential spreader detection algorithms are unstable to small perturbations of the network structure.
Clustering and Community Detection in Directed Networks: A Survey
Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science.
