Collaborative Graph Neural Networks for Attributed Network Embedding

Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. However, existing efforts mainly focus on exploiting network structures, while the exploitation of node attributes is rather limited as they only serve as node features at the initial layer. This simple strategy impedes the potential of node attributes in augmenting node connections, leading to limited receptive field for inactive nodes with few or even no neighbors. Furthermore, the training objectives (i.e., reconstructing network structures) of most GNNs also do not include node attributes, although studies have shown that reconstructing node attributes is beneficial. Thus, it is encouraging to deeply involve node attributes in the key components of GNNs, including graph convolution operations and training objectives. However, this is a nontrivial task since an appropriate way of integration is required to maintain the merits of GNNs. To bridge the gap, in this paper, we propose COllaborative graph Neural Networks--CONN, a tailored GNN architecture for attribute network embedding. It improves model capacity by 1) selectively diffusing messages from neighboring nodes and involved attribute categories, and 2) jointly reconstructing node-to-node and node-to-attribute-category interactions via cross-correlation. Experiments on real-world networks demonstrate that CONN excels state-of-the-art embedding algorithms with a great margin

Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant

Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant provides a powerful tool for clinical decision making, including personalized treatment and organ allocation. However, traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis due to two major data and model-related challenges. To address this, we propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly. Specifically, we develop a novel tree distillation strategy for multi-task learning, which combines the strength of both the tree model and multi-task learning. Experimental results are presented to show the precise and reliable CoD predictions of our framework. A case study is conducted to demonstrate the clinical importance of our method in the liver transplant

Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To tackle the issue of fairness, this work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The proposed framework considers the inconsistency between the embedding of inclusion and exclusion criteria among patients of different sensitive groups. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased

OpenGSL: A Comprehensive Benchmark for Graph Structure Learning

Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node connections, resulting from the complex and contingent formation process of graphs, presents significant challenges in modeling them effectively. To tackle this issue, Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models. Despite the proposal of numerous GSL methods, the progress in this field remains unclear due to inconsistent experimental protocols, including variations in datasets, data processing techniques, and splitting strategies. In this paper, we introduce OpenGSL, the first comprehensive benchmark for GSL, aimed at addressing this gap. OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and splitting strategies. Through extensive experiments, we observe that existing GSL methods do not consistently outperform vanilla GNN counterparts. However, we do observe that the learned graph structure demonstrates a strong generalization ability across different GNN backbones, despite its high computational and space requirements. We hope that our open-sourced library will facilitate rapid and equitable evaluation and inspire further innovative research in the field of GSL. The code of the benchmark can be found in https://github.com/OpenGSL/OpenGSL.Comment: 9 pages, 4 figure

Effects of Different Exogenous Substances on the Protein Conformation and in Vitro Digestion Characteristics of Low-salt Tilapia Surimi

The effects of glutamine transaminase (TGase), hydroxypropyl distarch phosphate (HDP), gellan gum and their complex (THG) on the water distribution and protein conformation of low-salt tilapia surimi gel prepared with microwave and ultrasound were analyzed. In addition, the effects of different exogenous substances on the characteristics of low-salt tilapia fish cake were explored through in vitro digestion experiment. The results showed that compared with the control group, THG increased the bound water and immovable water of surimi to 98.71% and 14.75%, respectively, and significantly decreased the free water content (P<0.05). Moreover, THG promoted the transformation of α-helix to β-folding, β-turning and random curling structures. TGase and THG (0.4%) played important roles on gastric emptying rate, protein digestibility and protein hydrolysis degree of low-salt tilapia cake. THG significantly promoted protein decomposition into aggregates with smaller particle size (P<0.05). After the digestion of stomach and duodenum, color of the THG group products was more transparent and clear. And it could be observed by the laser confocal microscope that the red fluorescence highlights of the THG group were significantly reduced, indicating that proteins had been fully digested. Hence, compared with a single exogenous substance, THG not only promoted the binding of water molecules and proteins and induced the change of protein conformation, but also facilitated the exposure of hydrophobic protein groups and the interaction between proteins, and promoted the digestion and absorption of surimi products in the stomach and duodenum. This project provided a theoretical reference for the research on the gel properties of tilapia surimi and the development and application of tilapia fish cake

Towards Efficient Self-Supervised Learning on Graphs

Deep learning on graphs has garnered considerable attention across various machine learning applications, encompassing social science, transportation services, and biomedical informatics. Nonetheless, prevailing methods have predominantly focused on supervised learning, resulting in several limitations, such as heavy reliance on labels and subpar generalization. To address the scarcity of labels, self-supervised learning (SSL) has emerged as a promising approach for graph data. Traditional SSL methods for graphs primarily concentrate on enhancing model performance through advanced data augmentation strategies and contrastive loss functions. Despite the significant progress made by existing studies, they encounter severe efficiency challenges when dealing with large-scale graphs and resource-limited applications, such as online services. To bridge this gap, I have developed a series of graph SSL models that systematically enhance the efficiency of self-supervised learning on graphs across the stages of model training, inference, and deployment. Firstly, to improve training efficiency, we propose automating the data augmentation process through Graph Personalized Augmentation (GPA) and conducting augmentation-free training via model perturbation (PerturbGCL). Secondly, to expedite inference efficiency, we suggest distilling the fine-tuned classification model into a lightweight model using reliable knowledge distillation (Meta-MLP). Finally, to enhance deployment efficiency, we propose the development of a universal graph model (S2GAE) that enables the learned representation to generalize across different types of downstream tasks in the graph system. My research presents a significant contribution to the research community by advancing the efficiency and applicability of self-supervised learning on graphs, addressing challenges related to label scarcity and resource limitations. These innovations have the potential to revolutionize various machine learning applications across disciplines, ranging from social science to transportation services and biomedical informatics, ultimately paving the way for more effective and widespread adoption of deep learning techniques in real-world graph scenarios

Multi-Label Classification Based on Low Rank Representation for Image Annotation

Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR). MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover). We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images