Empowering Simple Graph Convolutional Networks.

Many neural networks for graphs are based on the graph convolution (GC) operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, which tend to add complexity (and nonlinearity) to the model. Recently, however, a simplified GC operator, dubbed simple graph convolution (SGC), which aims to remove nonlinearities was proposed. Motivated by the good results reached by this simpler model, in this article we propose, analyze, and compare simple graph convolution operators of increasing complexity that rely on linear transformations or controlled nonlinearities, and that can be implemented in single-layer graph convolutional networks (GCNs). Their computational expressiveness is characterized as well. We show that the predictive performance of the proposed GC operators is competitive with the ones of other widely adopted models on the considered node classification benchmark datasets.

Multiresolution Reservoir Graph Neural Network.

Graph neural networks are receiving increasing attention as state-of-the-art methods to process graph-structured data. However, similar to other neural networks, they tend to suffer from a high computational cost to perform training. Reservoir computing (RC) is an effective way to define neural networks that are very efficient to train, often obtaining comparable predictive performance with respect to the fully trained counterparts. Different proposals of reservoir graph neural networks have been proposed in the literature. However, their predictive performances are still slightly below the ones of fully trained graph neural networks on many benchmark datasets, arguably because of the oversmoothing problem that arises when iterating over the graph structure in the reservoir computation. In this work, we aim to reduce this gap defining a multiresolution reservoir graph neural network (MRGNN) inspired by graph spectral filtering. Instead of iterating on the nonlinearity in the reservoir and using a shallow readout function, we aim to generate an explicit k -hop unsupervised graph representation amenable for further, possibly nonlinear, processing. Experiments on several datasets from various application areas show that our approach is extremely fast and it achieves in most of the cases comparable or even higher results with respect to state-of-the-art approaches.

Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition.

In many decision-making scenarios, ranging from recreational activities to healthcare and policing, the use of artificial intelligence coupled with the ability to learn from historical data is becoming ubiquitous. This widespread adoption of automated systems is accompanied by the increasing concerns regarding their ethical implications. Fundamental rights, such as the ones that require the preservation of privacy, do not discriminate based on sensible attributes (e.g., gender, ethnicity, political/sexual orientation), or require one to provide an explanation for a decision, are daily undermined by the use of increasingly complex and less understandable yet more accurate learning algorithms. For this purpose, in this work, we work toward the development of systems able to ensure trustworthiness by delivering privacy, fairness, and explainability by design. In particular, we show that it is possible to simultaneously learn from data while preserving the privacy of the individuals thanks to the use of Homomorphic Encryption, ensuring fairness by learning a fair representation from the data, and ensuring explainable decisions with local and global explanations without compromising the accuracy of the final models. We test our approach on a widespread but still controversial application, namely face recognition, using the recent FairFace dataset to prove the validity of our approach.

Idiopathic and acquired pedophilia as two distinct disorders: an insight from neuroimaging.

Pedophilia is a disorder of public concern because of its association with child sexual offense and recidivism. Previous neuroimaging studies of potential brain abnormalities underlying pedophilic behavior, either in idiopathic or acquired (i.e., emerging following brain damages) pedophilia, led to inconsistent results. This study sought to explore the neural underpinnings of pedophilic behavior and to determine the extent to which brain alterations may be related to distinct psychopathological features in pedophilia. To this aim, we run a coordinate based meta-analysis on previously published papers reporting whole brain analysis and a lesion network analysis, using brain lesions as seeds in a resting state connectivity analysis. The behavioral profiling approach was applied to link identified regions with the corresponding psychological processes. While no consistent neuroanatomical alterations were identified in idiopathic pedophilia, the current results support that all the lesions causing acquired pedophilia are localized within a shared resting state network that included posterior midlines structures, right inferior temporal gyrus and bilateral orbitofrontal cortex. These regions are associated with action inhibition and social cognition, abilities that are consistently and severely impaired in acquired pedophiles. This study suggests that idiopathic and acquired pedophilia may be two distinct disorders, in line with their distinctive clinical features, including age of onset, reversibility and modus operandi. Understanding the neurobiological underpinnings of pedophilic behavior may contribute to a more comprehensive characterization of these individuals on a clinical ground, a pivotal step forward for the development of more efficient therapeutic rehabilitation strategies.

Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'.

On 21 February 2020, a resident of the municipality of Vo', a small town near Padua (Italy), died of pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection1. This was the first coronavirus disease 19 (COVID-19)-related death detected in Italy since the detection of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province2. In response, the regional authorities imposed the lockdown of the whole municipality for 14 days3. Here we collected information on the demography, clinical presentation, hospitalization, contact network and the presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo' at two consecutive time points. From the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI): 2.1-3.3%). From the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% CI: 0.8-1.8%). Notably, 42.5% (95% CI: 31.5-54.6%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic (that is, did not have symptoms at the time of swab testing and did not develop symptoms afterwards). The mean serial interval was 7.2 days (95% CI: 5.9-9.6). We found no statistically significant difference in the viral load of symptomatic versus asymptomatic infections (P = 0.62 and 0.74 for E and RdRp genes, respectively, exact Wilcoxon-Mann-Whitney test). This study sheds light on the frequency of asymptomatic SARS-CoV-2 infection, their infectivity (as measured by the viral load) and provides insights into its transmission dynamics and the efficacy of the implemented control measures.

Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels.

When dealing with kernel methods, one has to decide which kernel and which values for the hyperparameters to use. Resampling techniques can address this issue but these procedures are time-consuming. This problem is particularly challenging when dealing with structured data, in particular with graphs, since several kernels for graph data have been proposed in literature, but no clear relationship among them in terms of learning properties is defined. In these cases, exhaustive search seems to be the only reasonable approach. Recently, the global Rademacher complexity (RC) and local Rademacher complexity (LRC), two powerful measures of the complexity of a hypothesis space, have shown to be suited for studying kernels properties. In particular, the LRC is able to bound the generalization error of an hypothesis chosen in a space by disregarding those ones which will not be taken into account by any learning procedure because of their high error. In this paper, we show a new approach to efficiently bound the RC of the space induced by a kernel, since its exact computation is an NP-Hard problem. Then we show for the first time that RC can be used to estimate the accuracy and expressivity of different graph kernels under different parameter configurations. The authors' claims are supported by experimental results on several real-world graph data sets.

Scuba: scalable kernel-based gene prioritization.

BACKGROUND: The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. RESULTS: We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. CONCLUSIONS: Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .

Tree-Based Kernel for Graphs With Continuous Attributes.

The availability of graph data with node attributes that can be either discrete or real-valued is constantly increasing. While existing Kernel methods are effective techniques for dealing with graphs having discrete node labels, their adaptation to nondiscrete or continuous node attributes has been limited, mainly for computational issues. Recently, a few kernels especially tailored for this domain, and that trade predictive performance for computational efficiency, have been proposed. In this brief, we propose a graph kernel for complex and continuous nodes' attributes, whose features are tree structures extracted from specific graph visits. The kernel manages to keep the same complexity of the state-of-the-art kernels while implicitly using a larger feature space. We further present an approximated variant of the kernel, which reduces its complexity significantly. Experimental results obtained on six real-world data sets show that the kernel is the best performing one on most of them. Moreover, in most cases, the approximated version reaches comparable performances to the current state-of-the-art kernels in terms of classification accuracy while greatly shortening the running times.

An efficient graph kernel method for non-coding RNA functional prediction.

MOTIVATION: The importance of RNA protein-coding gene regulation is by now well appreciated. Non-coding RNAs (ncRNAs) are known to regulate gene expression at practically every stage, ranging from chromatin packaging to mRNA translation. However the functional characterization of specific instances remains a challenging task in genome scale settings. For this reason, automatic annotation approaches are of interest. Existing computational methods are either efficient but non-accurate or they offer increased precision, but present scalability problems. RESULTS: In this article, we present a predictive system based on kernel methods, a type of machine learning algorithm grounded in statistical learning theory. We employ a flexible graph encoding to preserve multiple structural hypotheses and exploit recent advances in representation and model induction to scale to large data volumes. Experimental results on tens of thousands of ncRNA sequences available from the Rfam database indicate that we can not only improve upon state-of-the-art predictors, but also achieve speedups of several orders of magnitude. AVAILABILITY AND IMPLEMENTATION: The code is available from http://www.bioinf.uni-freiburg.de/~costa/EDeN.tgz . CONTACT: f.costa@exeter.ac.uk.