Graph classification is a pivotal challenge in machine learning, especially within the realm of graph-based data, given its importance in numerous real-world applications such as social network analysis, recommendation systems, and bioinformatics. Despite its significance, graph classification faces several hurdles, including adapting to diverse prediction tasks, training across multiple target domains, and handling small-sample prediction scenarios. Current methods often tackle these challenges individually, leading to fragmented solutions that lack a holistic approach to the overarching problem. In this paper, we propose an algorithm aimed at addressing the aforementioned challenges. By incorporating insights from various types of tasks, our method aims to enhance adaptability, scalability, and generalizability in graph classification. Motivated by the recognition that the underlying subgraph plays a crucial role in GNN prediction, while the remainder is task-irrelevant, we introduce the Core Knowledge Learning (CKL) framework for graph adaptation and scalability learning. CKL comprises several key modules, including the core subgraph knowledge submodule, graph domain adaptation module, and few-shot learning module for downstream tasks. Each module is tailored to tackle specific challenges in graph classification, such as domain shift, label inconsistencies, and data scarcity. By learning the core subgraph of the entire graph, we focus on the most pertinent features for task relevance. Consequently, our method offers benefits such as improved model performance, increased domain adaptability, and enhanced robustness to domain variations. Experimental results demonstrate significant performance enhancements achieved by our method compared to state-of-the-art approaches. Specifically, our method achieves notable improvements in accuracy and generalization across various datasets and evaluation metrics, underscoring its effectiveness in addressing the challenges of graph classification.

Stance detection aims to determine the attitude expressed in text towards a given target. Zero-shot stance detection (ZSSD) has emerged to classify stances towards unseen targets during inference. Recent data augmentation techniques for ZSSD increase transferable knowledge between targets through text or target augmentation. However, these methods exhibit limitations. Target augmentation lacks logical connections between generated targets and source text, while text augmentation relies solely on training data, resulting in insufficient generalization. To address these issues, we propose an encoder-decoder data augmentation (EDDA) framework. The encoder leverages large language models and chain-of-thought prompting to summarize texts into target-specific if-then rationales, establishing logical relationships. The decoder generates new samples based on these expressions using a semantic correlation word replacement strategy to increase syntactic diversity. We also analyze the generated expressions to develop a rationale-enhanced network that fully utilizes the augmented data. Experiments on benchmark datasets demonstrate our approach substantially improves over state-of-the-art ZSSD techniques. The proposed EDDA framework increases semantic relevance and syntactic variety in augmented texts while enabling interpretable rationale-based learning.

Global weather forecast is an important spatial-temporal prediction problem, which can provide numerous societal benefits such as extreme weather forewarning, traffic scheduling, and agricultural planning. Though many spatial-temporal prediction models have been proposed, they suffer from two drawbacks for global weather forecasts, namely (i) ignoring the physical mechanism and spherical characteristics and (ii) not effectively exploiting the global and local correlations. To address the above drawbacks, in this paper, we formalize global weather state dynamics as partial differential equations (PDEs) in spherical space and infer the state of the global weather system by solving these PDEs. Specifically, we use Green’s function method to solve the PDEs and find that the solution of the spherical PDEs can be obtained by the spherical convolution. We further proposed a novel Spherical Neural Operator, SNO, which consists of spherical convolution and vanilla convolution. The former is used to solve these PDEs and model the global correlations in spherical space, and the latter is used to capture the local correlations. Upon the operator, a global weather prediction model is developed. Extensive experimental results demonstrate the effectiveness and superiority of our method over state-of-the-art approaches.

Weather prediction plays a crucial role in human development. Recently, deep learning has demonstrated promising prospects in weather forecasting by integrating convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, two main challenges still exist in multiple weather condition prediction. The first challenge considers multiple weather condition correlations in predictions. The second challenge is how to model long- and short-range spatial dependencies under multiple weather conditions. A novel operator named as tensor-based long- and short-range convolution (TLS-Conv) is proposed to address these challenges. Within this operator, the node & relation attention is utilized to identify the contributions of spatial grid points and weather conditions for prediction. Additionally, the adaptive tensor graph convolution (ATGCN) is tailored to dynamically capture long-range spatial dependencies within multiple weather conditions. Finally, the traditional convolution is integrated with the ATGCN to model both long- and short-range spatial dependencies and weather condition correlations. Building upon the TLS-Conv, the tensor-based long- and short-range convolution for multiple weather prediction (TLS-MWP) model is proposed to predict multiple weather conditions. Extensive experiments are conducted under real-world weather conditions to evaluate its performance. These results unequivocally demonstrate that TLS-MWP surpasses previous methods. The code is available on GitHub at: https://github.com/xuguangning1218/TLS_MWP.

A tropical cyclone is a highly destructive extreme weather phenomenon. Estimating the intensity of a tropical cyclone can help provide early warnings, guiding specific disaster defense measures. However, two main challenges hinder performance improvement. The first challenge is how to combine heterogeneous tropical cyclone data into a latent space so that the model can leverage the cloud structure of satellite imagery and the comprehensive meteorological information from reanalysis or forecast data for intensity estimation. The second challenge lies in detecting multiple pseudo-fine-grained areas for the final estimation since tropical cyclones are highly diverse extreme weather phenomena. Neglecting any pseudo-fine-grained areas or relying solely on a single one can potentially result in subpar estimation performance. To address the challenges mentioned above, a fine-grained heterogeneous data fusion framework named FHDTIE is proposed. Two key components in this framework can address the aforementioned challenges. One component is the HDF, which offers shape matching and channel fusing strategies for heterogeneous data fusion. The other component is called the fine-grained cluster features integrator (FCFI). It utilizes a clustering method to identify multiple pseudo-fine-grained areas. Within these areas, the U-Net is used to automatically learn pseudo-fine-grained area representations, and then the graph neural network handles information interaction across these representations. Extensive experiments were conducted to demonstrate the robustness and superiority of the proposed fine-grained heterogeneous data fusion framework. The code is available at GitHub:https://github.com/xuguangning1218/FHDTIE.

Event detection in a city is crucial for the government to listen to the voice of the citizens, be aware of the real occurrences in a city, and then make wiser policies. However, in reality some important events with few samples are easily to be overwhelmed by the massive information and hard to be recognized, and additionally the limited word description from the short texts even makes the recognition harder. To address the problems, we propose a knowledge-aware event detector by incorporating the external knowledge to detect the events with few examples. The external knowledge incorporation with different semantic relations is capable to enrich the short texts. In addition, we leverage the representative few shot learning framework to formulate the event detection as the text classification problem. The proposed model is evaluated on two widely event-detection datasets. The experiments show a consistent accuracy improvement. The findings validates that our model with the knowledge infusion is effective to detect the few shot events from the short texts.

Session-Based Recommendation (SBR) is a spotlight research problem. Although many efforts have been made, challenges still exist. The key to unlocking this shackle is the user intention, an intuitive but hard-to-model concept in the anonymous session. Unlike previous research, we suggest mining potential user intention by counting the number of item occurrences in a user session and considering the long interval between item re-interactions. Beyond these, we take user preference, a biased user intention, into account in the prediction stage. Forming these together, we propose a model named user Intention aware Graph Neural Network (Int-GNN) aiming at capturing user intention. Extensive experiments have been conducted on three real-world datasets, and the results show the superiority of our method. The code is available on GitHub: https://github.com/xuguangning1218/IntGNN_ICASSP2023

Stance detection is an important task, which aims to classify the attitude of an opinionated text toward a given target. In this paper, we develop an interpretable neural production system for stance detection (NPS4SD). NPS4SD is an end-to-end deep learning model, which consists of a set of knowledge rules that are applied by binding with specific entities. NPS4SD consists of two main components: a pretrained model for learning the text representation and a variable binding network (VBN) to bind the knowledge rules with text entities. Extensive experiments are conducted to evaluate the effectiveness of the proposed NPS4SD model on three real-world datasets with in-domain, cross-target and zero-shot setups. Experimental results demonstrate that NPS4SD achieves substantially better performance than the strong competitors for the stance detection task.