With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

With the advent of brain imaging techniques and machine learning tools, much effort has been devoted to building computational models to capture the encoding of visual information in the human brain. One of the most challenging brain decoding tasks is the accurate reconstruction of the perceived natural images from brain activities measured by functional magnetic resonance imaging (fMRI). In this work, we survey the most recent deep learning methods for natural image reconstruction from fMRI. We examine these methods in terms of architectural design, benchmark datasets, and evaluation metrics and present a fair performance evaluation across standardized evaluation metrics. Finally, we discuss the strengths and limitations of existing studies and present potential future directions.

Discovering community structure in networks remains a fundamentally challenging task. From scientific domains such as biology, chemistry and physics to social networks the challenge of identifying community structures in different kinds of network is challenging since there is no universal definition of community structure. Furthermore, with the surge of social networks, content information has played a pivotal role in defining community structure, demanding techniques beyond its traditional approach. Recently, network representation learning have shown tremendous promise. Leveraging on recent advances in deep learning, one can exploit deep learning's superiority to a network problem. Most predominantly, successes in supervised and semi-supervised task has shown promising results in network representation learning tasks such as link prediction and graph classification. However, much has yet to be explored in the literature of community detection which is an unsupervised learning task. This paper proposes a deep generative model for community detection and network generation. Empowered with Bayesian deep learning, deep generative models are capable of exploiting non-linearities while giving insights in terms of uncertainty. Hence, this paper proposes Variational Graph Autoencoder for Community Detection (VGAECD). Extensive experiment shows that it is capable of outperforming existing state-of-the-art methods. The generalization of the proposed model also allows the model to be considered as a graph generator. Additionally, unlike traditional methods, the proposed model does not require a predefined community structure definition. Instead, it assumes the existence of latent similarity between nodes and allows the model to find these similarities through an automatic model selection process. Optionally, it is capable of exploiting feature-rich information of a network such as node content, further increasing its performance.

Modularity evaluates the quality of a division of network nodes into communities, and modularity optimization is the most widely used class of methods for detecting communities in networks. In bipartite networks, there are correspondingly bipartite modularity and bipartite modularity optimization. LPAb, a very fast label propagation algorithm based on bipartite modularity optimization, tends to become stuck in poor local maxima, yielding suboptimal community divisions with low bipartite modularity. We therefore propose LPAb+, a hybrid algorithm combining modified LPAb, or LPAb’, and MSG, a multistep greedy agglomerative algorithm, with the objective of using MSG to drive LPAb out of local maxima. We use four commonly used real-world bipartite networks to demonstrate LPAb+ capability in detecting community divisions with remarkably higher bipartite modularity than LPAb. We show how LPAb+ outperforms other bipartite modularity optimization algorithms, without compromising speed.