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07/16/2022
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
In the past few years, graph neural networks (GNNs) have become the de f...
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01/10/2022
Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach
Graph Neural Networks (GNNs) have become the state-of-the-art method for...
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08/19/2021
odeN: Simultaneous Approximation of Multiple Motif Counts in Large Temporal Networks
Counting the number of occurrences of small connected subgraphs, called ...
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06/07/2021
SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds
Betweenness centrality is a popular centrality measure with applications...
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01/18/2021
PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts
The identification and counting of small graph patterns, called network ...
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12/12/2020
A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
Graph Neural Networks (GNNs) are a framework for graph representation le...
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06/16/2020
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining
We present MCRapper, an algorithm for efficient computation of Monte-Car...
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06/06/2020
Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation
Sensor-based human activity recognition (HAR) requires to predict the ac...
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06/06/2020
Are Graph Convolutional Networks Fully Exploiting Graph Structure?
Graph Convolutional Networks (GCNs) generalize the idea of deep convolut...
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03/03/2020
Scalable Distributed Approximation of Internal Measures for Clustering Evaluation
The most widely used internal measure for clustering evaluation is the s...
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01/07/2013