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Found 17 papers, 6 papers with code
Conditional Constrained Graph Variational Autoencoders for Molecule Design
In recent years, deep generative models for graphs have been used to generate new molecules.
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances
Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints.
Explainable Predictive Process Monitoring
Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes.
RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design
Identifying molecules that exhibit some pre-specified properties is a difficult problem to solve.
On Filter Size in Graph Convolutional Networks
Recently, many researchers have been focusing on the definition of neural networks for graphs.
A Systematic Assessment of Deep Learning Models for Molecule Generation
In recent years the scientific community has devoted much effort in the development of deep learning models for the generation of new molecules with desirable properties (i. e. drugs).
A tree-based kernel for graphs with continuous attributes
While existing kernel methods are effective techniques for dealing with graphs having discrete node labels, their adaptation to non-discrete or continuous node attributes has been limited, mainly for computational issues.
An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams of Graphs
It turns out that, when strict memory budget constraints have to be enforced, working in feature space, given the current state of the art on graph kernels, is more than a viable alternative to dual approaches, both in terms of speed and classification performance.
Ordered Decompositional DAG Kernels Enhancements
In this paper, we show how the Ordered Decomposition DAGs (ODD) kernel framework, a framework that allows the definition of graph kernels from tree kernels, allows to easily define new state-of-the-art graph kernels.
Graph Kernels exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions
In this paper we present a novel graph kernel framework inspired the by the Weisfeiler-Lehman (WL) isomorphism tests.
Extending local features with contextual information in graph kernels
Different kernels consider different types of substructures.
Pre-training Graph Neural Networks with Kernels
Many machine learning techniques have been proposed in the last few years to process data represented in graph-structured form.
Polynomial Graph Convolutional Networks
In this paper, we propose a different strategy, considering a single graph convolution layer that independently exploits neighbouring nodes at different topological distances, generating decoupled representations for each of them.
Simple Graph Convolutional Networks
Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago.
Object-centric Process Predictive Analytics
The results are compared with a naive approach that overlooks the object interactions, thus illustrating the benefits of their use on the prediction quality.
An Explainable Decision Support System for Predictive Process Analytics
However, process stakeholders need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way.
Economic Recommender Systems -- A Systematic Review
In this work, we survey the existing literature on what we call Economic Recommender Systems based on a systematic review approach that helped us identify 133 relevant papers.
