Community detection, aiming at partitioning a network into multiple substructures, is practically importance. Graph convolutional network (GCN), a new deep-learning technique, has recently been developed for community detection. Markov Random Fields (MRF) has been combined with GCN in the MRFasGCN method to improve accuracy. However, the existing GCN community-finding methods are semi-supervised, even though community finding is essentially an unsupervised learning problem. We developed a new GCN approach for unsupervised community detection under the framework of Autoencoder. We cast MRFasGCN as an encoder and then derived node community membership in the hidden layer of the encoder. We introduced a community-centric dual decoder to reconstruct network structures and node attributes separately in an unsupervised fashion, for faithful community detection in the input space. We designed a scheme of local enhancement to accommodate nodes to have more common neighbors and similar attributes with similar community memberships. Experimental results on real networks showed that our new method outperformed the best existing methods, showing the effectiveness of the novel decoding mechanism for generating links and attributes together over the commonly used methods for reconstructing links alone.
Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a unified architecture of network communityfinding methods to characterize the state-of-the-art of the field of community detection. Specifically, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
Obesity and its related severe consequences have been a major public health problem worldwide. A significant weight gain and intra-abdominal adipose tissue accumulation are observed as women begin the menopausal transition. A number of clinical and basic research indicate that ovarian hormone may play a crucial role. However, the underlying mechanisms are largely unknown. In this chapter, we aim to systematically review the literature in the influences of ovarian hormone on the physiology of lipid and glucose metabolism, obesity-related hormone, and the regulation of body weight by the dietary intake, feeding behavior. A variety of research modalities have been used to explore the effects of MHT in perimenopausal women. Hence, we will also summarize the latest progress of MHT use on the effect of body mass, body fat redistribution, and insulin resistance, which lead to protective cardiometabolic effects.
Visual Question Answering (VQA) aims to answer the natural language question about a given image by understanding multimodal content. However, the answer quality of most existing visual-language pre-training (VLP) methods is still limited, mainly due to: (1) Incompatibility. Upstream pre-training tasks are generally incompatible with downstream question answering tasks, which makes the knowledge from the language model not well transferable to downstream tasks, and greatly limits their performance in few-shot scenarios; (2) Under-fitting. They generally do not integrate human priors to compensate for universal knowledge from language models, so as to fit the challenging VQA problem and generate reliable answers. To address these issues, we propose HybridPrompt, a cloze- and verify-style hybrid prompt framework with bridging language models and human priors in prompt tuning for VQA. Specifically, we first modify the input questions into the cloze-style prompts to narrow the gap between upstream pre-training tasks and downstream VQA task, which ensures that the universal knowledge in the language model can be better transferred to subsequent human prior-guided prompt tuning. Then, we imitate the cognitive process of human brain to introduce topic and sample related priors to construct a dynamic learnable prompt template for human prior-guided prompt learning. Finally, we add fixed-length learnable free-parameters to further enhance the generalizability and scalability of prompt learning in the VQA model. Experimental results verify the effectiveness of HybridPrompt, showing that it achieves competitive performance against previous methods on widely-used VQAv2 dataset and obtains new state-of-the-art results. Our code is released at: https://github.com/zhizhi111/hybrid.
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