Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs

Text-attributed Graphs (TAGs) are commonly found in the real world, such as social networks and citation networks, and consist of nodes represented by textual descriptions. Currently, mainstream machine learning methods on TAGs involve a two-stage modeling approach: (1) unsupervised node feature extraction with pre-trained language models (PLMs); and (2) supervised learning using Graph Neural Networks (GNNs). However, we observe that these representations, which have undergone large-scale pre-training, do not significantly improve performance with a limited amount of training samples. The main issue is that existing methods have not effectively integrated information from the graph and downstream tasks simultaneously. In this paper, we propose a novel framework called G-Prompt, which combines a graph adapter and task-specific prompts to extract node features. First, G-Prompt introduces a learnable GNN layer (\emph{i.e.,} adaptor) at the end of PLMs, which is fine-tuned to better capture the masked tokens considering graph neighborhood information. After the adapter is trained, G-Prompt incorporates task-specific prompts to obtain \emph{interpretable} node representations for the downstream task. Our experiment results demonstrate that our proposed method outperforms current state-of-the-art (SOTA) methods on few-shot node classification. More importantly, in zero-shot settings, the G-Prompt embeddings can not only provide better task interpretability than vanilla PLMs but also achieve comparable performance with fully-supervised baselines.Comment: Under revie

GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

The advancement of Large Language Models (LLMs) has remarkably pushed the boundaries towards artificial general intelligence (AGI), with their exceptional ability on understanding diverse types of information, including but not limited to images and audio. Despite this progress, a critical gap remains in empowering LLMs to proficiently understand and reason on graph data. Recent studies underscore LLMs' underwhelming performance on fundamental graph reasoning tasks. In this paper, we endeavor to unearth the obstacles that impede LLMs in graph reasoning, pinpointing the common practice of converting graphs into natural language descriptions (Graph2Text) as a fundamental bottleneck. To overcome this impediment, we introduce GraphLLM, a pioneering end-to-end approach that synergistically integrates graph learning models with LLMs. This synergy equips LLMs with the ability to proficiently interpret and reason on graph data, harnessing the superior expressive power of graph learning models. Our empirical evaluations across four fundamental graph reasoning tasks validate the effectiveness of GraphLLM. The results exhibit a substantial average accuracy enhancement of 54.44%, alongside a noteworthy context reduction of 96.45% across various graph reasoning tasks

Theoretical Study and Experimental Validation on the Applicable Refrigerant for Space Heating Air Source Heat Pump

The air source heat pump (ASHP) is developing rapidly and is widely used for space heating due to its potential for increasing energy efficiency and reducing greenhouse gas emissions. The choice of appropriate low global warming potential (GWP) alternative refrigerants is one of the challenges that ASHP systems face. Alternative refrigerants also affect the energy performance of these systems. Thus, it is essential to evaluate the performance of ASHP using environmentally friendly refrigerants to facilitate reasonable refrigerant selection. A theoretical model for simulating ASHP performance with different refrigerants is developed in this study. Experiments are conducted to validate the theoretical model. The simulation and the experimental results are found to be in good agreement. The ASHP performance indices, such as compression ratio (CR), discharging temperature (DT) and coefficients of performance (COP), are investigated using R22, R417A, R410A, R134a, R152a, R161 and R1234yf as working fluids. The results show that R152a has an average COP of 2.7% higher than R22, and R161 has an average COP of 1.4% higher than R22. R152a and R161 also have a higher CR but a lower DT than R22 under the same design conditions. In addition, R152a and R161 have ozone depletion potentials (ODP) of zero and extremely low GWPs; thus, they can be candidates to replace R22 in ASHP heating systems. This research provides a reference on refrigerant replacements for ASHP heating systems in North China

Simulation study on the interaction between the battery module and busbar under typical driving conditions of electric vehicles

Accurate simulation of the battery thermoelectric coupling characteristics is the key to the thermal design and thermal management. As the electrically connected component between the battery electrodes, the heat production and heat transfer of the busbars have a significant impact on the battery thermoelectric behavior. A small number of battery thermal management studies have begun to model busbars recently, but the thickness, length, and material of the busbars are diverse. There is a lack of perfect simulation analysis and optimization methods. There is an urgent need to conduct in-depth research on the influence of characteristic parameters of the busbars on the battery thermoelectric behavior. Based on the MSMD-NTGK model, this paper investigates the influence of the thickness, length, and material of the busbars on the battery thermoelectric behavior without considering the economic cost and differences in the battery structure design. In this paper, a simulation analysis method and an ideal improvement solution for the design of the busbars are proposed. This paper compares the changes in the battery thermal behavior before and after the improvement of the busbars under the constant-rate discharge process, FTP75 condition, NEDC condition and WLTC condition respectively. This study can improve the realism of the battery thermal behavior simulation, provide a reliable simulation analysis method and reference basis for the industrial design and optimization of the busbars, and further improve the reliability of the subsequent battery thermal management simulation