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

Can GNN be Good Adapter for LLMs?

Recently, large language models (LLMs) have demonstrated superior capabilities in understanding and zero-shot learning on textual data, promising significant advances for many text-related domains. In the graph domain, various real-world scenarios also involve textual data, where tasks and node features can be described by text. These text-attributed graphs (TAGs) have broad applications in social media, recommendation systems, etc. Thus, this paper explores how to utilize LLMs to model TAGs. Previous methods for TAG modeling are based on million-scale LMs. When scaled up to billion-scale LLMs, they face huge challenges in computational costs. Additionally, they also ignore the zero-shot inference capabilities of LLMs. Therefore, we propose GraphAdapter, which uses a graph neural network (GNN) as an efficient adapter in collaboration with LLMs to tackle TAGs. In terms of efficiency, the GNN adapter introduces only a few trainable parameters and can be trained with low computation costs. The entire framework is trained using auto-regression on node text (next token prediction). Once trained, GraphAdapter can be seamlessly fine-tuned with task-specific prompts for various downstream tasks. Through extensive experiments across multiple real-world TAGs, GraphAdapter based on Llama 2 gains an average improvement of approximately 5\% in terms of node classification. Furthermore, GraphAdapter can also adapt to other language models, including RoBERTa, GPT-2. The promising results demonstrate that GNNs can serve as effective adapters for LLMs in TAG modeling.Comment: Accepted by WWW'2

A Comparative Analysis of the Effective and Local Slip Lengths for Liquid Flows Over a Trapped Nanobubble

The gas–liquid interfaces distributed on a superhydrophobic (SHP) surface promote the effective slip and might result in significant drag reduction desirable in many applications. While the slippage of water past gas–liquid interfaces on structured SHP surfaces has attracted wide attention, the slip behavior at gas–liquid interfaces trapped by the wettability step still remains unclear. Using molecular dynamics simulations, we first demonstrated that the three-phase contact line can be pinned on a smooth substrate of mixed wettability. We then numerically investigated slip flows over smooth surfaces with flattened gas bubbles trapped by the wettability step. It was found that the local slip length is relatively large at the gas–liquid interface and its spatial distribution becomes asymmetric due to shear-induced deformation of the attached bubble, while the effective slip length remains nearly constant. With increasing gas areal fraction, the local and effective slip lengths become larger, especially in the case of a stripe-like continuous gas–liquid interface where the interface curvature in the flow direction is absent

A comparative analysis of the effective and local slip lengths for liquid flows over a trapped nanobubble

The gas–liquid interfaces distributed on a superhydrophobic (SHP) surface promote the effective slip and might result in significant drag reduction desirable in many applications. While the slippage of water past gas–liquid interfaces on structured SHP surfaces has attracted wide attention, the slip behavior at gas–liquid interfaces trapped by the wettability step still remains unclear. Using molecular dynamics simulations, we first demonstrated that the three-phase contact line can be pinned on a smooth substrate of mixed wettability. We then numerically investigated slip flows over smooth surfaces with flattened gas bubbles trapped by the wettability step. It was found that the local slip length is relatively large at the gas–liquid interface and its spatial distribution becomes asymmetric due to shear-induced deformation of the attached bubble, while the effective slip length remains nearly constant. With increasing gas areal fraction, the local and effective slip lengths become larger, especially in the case of a stripe-like continuous gas–liquid interface where the interface curvature in the flow direction is absent

Substance P Modulates Colitis-Asscociated Fibrosis

Substance P (SP) and the neurokinin-1 receptor (NK-1R) are involved in the development of colitis and mucosal healing after colonic inflammation. We studied whether SP modulates colonic fibrosis by using a chronic model of trinitrobenzenesulfonic acid (TNBS)-induced colitis in wild-type (WT) and NK-1R-deficient (NK-1R KD) mice. We found increased mRNA expression levels of collagen, vimentin, and the fibrogenic factors transforming growth factor β1 and insulin-like growth factor 1 in the chronically inflamed colons of WT mice treated with repeated intracolonic TNBS administrations. Fibrosis in TNBS-treated mice was also evident immunohistochemically by collagen deposition in the colon. Treatment of TNBS-exposed WT mice with the NK-1R antagonist CJ-12255 reduced colonic inflammation, colonic fibrosis, fibroblast accumulation, and expression levels of the fibrogenic factors. NK-1R knockout mice chronically exposed to TNBS had similar colonic inflammation compared with WT, but reduced colonic fibrosis, fibroblast accumulation, and expression levels of fibrogenic factors. Immunohistochemical staining also showed co-localization of NK-1R with fibroblasts in inflamed colons of mice and in colonic mucosa of patients with Crohn’s disease. Exposure of human colonic CCD-18Co fibroblasts to SP (10 nmol/L) increased cell migration. SP stimulated collagen synthesis in CCD-18Co fibroblasts in the presence of transforming growth factor β1 and insulin-like growth factor 1, and this effect was reduced by Akt inhibition. Thus, SP, via NK-1R, promotes intestinal fibrogenesis after chronic colitis by stimulating fibrotic responses in fibroblasts