Message Passing Neural Networks (MPNNs) hold a key position in machine learning on graphs, but they struggle with unintended behaviors, such as over-smoothing and over-squashing, due to irregular data structures. The observation and formulation of these limitations have become foundational in constructing more informative graph representations. In this paper, we delve into the limitations of MPNNs, focusing on aspects that have previously been overlooked. Our observations reveal that even within a single layer, the information specific to an individual node can become significantly diluted. To delve into this phenomenon in depth, we present the concept of Over-dilution and formulate it with two dilution factors: intra-node dilution for attribute-level and inter-node dilution for node-level representations. We also introduce a transformer-based solution that alleviates over-dilution and complements existing node embedding methods like MPNNs. Our findings provide new insights and contribute to the development of informative representations. The implementation and supplementary materials are publicly available at https://github.com/LeeJunHyun/NATR.

Graph neural networks (GNNs) have shown high potential for a variety of real-world, challenging applications, but one of the major obstacles in GNN research is the lack of large-scale flexible datasets. Most existing public datasets for GNNs are relatively small, which limits the ability of GNNs to generalize to unseen data. The few existing large-scale graph datasets provide very limited labeled data. This makes it difficult to determine if the GNN model's low accuracy for unseen data is inherently due to insufficient training data or if the model failed to generalize. Additionally, datasets used to train GNNs need to offer flexibility to enable a thorough study of the impact of various factors while training GNN models.

Most existing Time series classification (TSC) models lack interpretability and are difficult to inspect. Interpretable machine learning models can aid in discovering patterns in data as well as give easy-to-understand insights to domain specialists. In this study, we present Neuro-Symbolic Time Series Classification (NSTSC), a neuro-symbolic model that leverages signal temporal logic (STL) and neural network (NN) to accomplish TSC tasks using multi-view data representation and expresses the model as a human-readable, interpretable formula. In NSTSC, each neuron is linked to a symbolic expression, i.e., an STL (sub)formula. The output of NSTSC is thus interpretable as an STL formula akin to natural language, describing temporal and logical relations hidden in the data. We propose an NSTSC-based classifier that adopts a decision-tree approach to learn formula structures and accomplish a multiclass TSC task. The proposed smooth activation functions enable the model to be learned in an end-to-end fashion. We test NSTSC on a real-world wound healing dataset from mice and benchmark datasets from the UCR time-series repository, demonstrating that NSTSC achieves comparable performance with the state-of-the-art models. Furthermore, NSTSC can generate interpretable formulas that match domain knowledge.

Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-experts, a label aggregation model that infers the true label from noisy crowdsourced labels is required. In this article, we propose a novel framework based on graph neural networks for aggregating crowd labels. We construct a heterogeneous graph between workers and tasks and derive a new graph neural network to learn the representations of nodes and the true labels. Besides, we exploit the unknown latent interaction between the same type of nodes (workers or tasks) by adding a homogeneous attention layer in the graph neural networks. Experimental results on 13 real-world datasets show superior performance over state-of-the-art models.

There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels inrich-dataenvironments (e.g., intensive care units). However, in many other practical situations, we can only access data with much fewer feature channels in apoor-dataenvironments (e.g., at home), which often results in predictive models with poor performance. How can we boost the performance of models learned from suchpoor-dataenvironment by leveraging knowledge extracted from existing models trained usingrich datain a related environment? To address this question, we develop a knowledge infusion framework namedCHEERthat can succinctly summarize suchrich modelinto transferable representations, which can be incorporated into thepoor modelto improve its performance. The infused model is analyzed theoretically and evaluated empirically on several datasets. Our empirical results showed thatCHEERoutperformed baselines by 5.60 to 46.80 percent in terms of the macro-F1 score on multiple physiological datasets.

Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however, neglecting the deep structured features of query texts and source codes, both of which contain rich semantic information. In this article, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval. To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet. In particular, DGMS not only captures more structural information for individual query texts or code snippets, but also learns the fine-grained similarity between them by cross-attention based semantic matching operations. We evaluate the proposed DGMS model on two public code retrieval datasets with two representative programming languages (i.e., Java and Python). Experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets. Moreover, our extensive ablation studies systematically investigate and illustrate the impact of each part of DGMS.

Clinical narratives that describe complex medical events are often accompanied by meta-information such as a patient’s demographics, diagnoses and medications. This structured information implicitly relates to the logical and semantic structure of the entire narrative, and thus affects vocabulary choices for the narrative composition. To leverage this meta-information, we propose a supervised topic compositional neural language model, called MeTRNN, that integrates the strength of supervised topic modeling in capturing global semantics with the capacity of contextual recurrent neural networks (RNN) in modeling local word dependencies. MeTRNN generates interpretable topics from global meta-information and uses them to facilitate contextual RNNs in modeling local dependencies of text. For efficient training of MeTRNN, we develop an autoencoding variational Bayes inference method. We evaluate MeTRNN on the word prediction tasks using public text datasets. MeTRNN consistently outperforms all baselines across all datasets in perplexity ranging from 5% to 40%. Our case studies on real world electronic health records (EHR) data show that MeTRNN can learn and benefit from meaningful topics.

Many real data come in the form of non-grid objects, i.e. graphs, from social networks to molecules. Adaptation of deep learning from grid-alike data (e.g. images) to graphs has recently received unprecedented attention from both machine learning and data mining communities, leading to a new cross-domain field---Deep Graph Learning (DGL). Instead of painstaking feature engineering, DGL aims to learn informative representations of graphs in an end-to-end manner. It has exhibited remarkable success in various tasks, such as node/graph classification, link prediction, etc.

Leveraging massive electronic health records (EHR) brings tremendous promises to advance clinical and precision medicine informatics research. However, it is very challenging to directly work with multifaceted patient information encoded in their EHR data. Deriving effective representations of patient EHRs is a crucial step to bridge raw EHR information and the endpoint analytical tasks, such as risk prediction or disease subtyping. In this paper, we propose Health-ATM, a novel and integrated deep architecture to uncover patients' comprehensive health information from their noisy, longitudinal, heterogeneous and irregular EHR data. Health-ATM extracts comprehensive multifaceted patient information patterns with attentive and time-aware modulars (ATM) and a hybrid network structure composed of both Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). The learned features are finally fed into a prediction layer to conduct the risk prediction task. We evaluated the Health-ATM on both artificial and real world EHR corpus and demonstrated its promising utility and efficacy on representation learning and disease onset predictions.

Document summarization plays an important role in the area of natural language processing and text mining. This paper proposes several novel information-theoretic models for multi-document summarization. They consider document summarization as a transmission system and assume that the best summary should have the minimum distortion. By defining a proper distortion measure and a new representation method, the combination of the last two models (the linear representation model and the facility location model) gains good experimental results on the DUC2002 and DUC2004 datasets. Moreover, we also indicate that the model has high interpretability and extensibility.