Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs

Text-attributed graphs (TAGs) are prevalent on the web and research over TAGs such as citation networks, e-commerce networks and social networks has attracted considerable attention in the web community. Recently, large language models (LLMs) have demonstrated exceptional capabilities across a wide range of tasks. However, the existing works focus on harnessing the potential of LLMs solely relying on prompts to convey graph structure information to LLMs, thus suffering from insufficient understanding of the complex structural relationships within TAGs. To address this problem, in this paper we present the Disentangled Graph-Text Learner (DGTL) model, which is able to enhance the reasoning and predicting capabilities of LLMs for TAGs. Our proposed DGTL model incorporates graph structure information through tailored disentangled graph neural network (GNN) layers, enabling LLMs to capture the intricate relationships hidden in text-attributed graphs from multiple structural factors. Furthermore, DGTL operates with frozen pre-trained LLMs, reducing computational costs and allowing much more flexibility in combining with different LLM models. Experimental evaluations demonstrate the effectiveness of the proposed DGTL model on achieving superior or comparable performance over state-of-the-art baselines. Additionally, we also demonstrate that our DGTL model can offer natural language explanations for predictions, thereby significantly enhancing model interpretability

NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search

Graph neural architecture search (GraphNAS) has recently aroused considerable attention in both academia and industry. However, two key challenges seriously hinder the further research of GraphNAS. First, since there is no consensus for the experimental setting, the empirical results in different research papers are often not comparable and even not reproducible, leading to unfair comparisons. Secondly, GraphNAS often needs extensive computations, which makes it highly inefficient and inaccessible to researchers without access to large-scale computation. To solve these challenges, we propose NAS-Bench-Graph, a tailored benchmark that supports unified, reproducible, and efficient evaluations for GraphNAS. Specifically, we construct a unified, expressive yet compact search space, covering 26,206 unique graph neural network (GNN) architectures and propose a principled evaluation protocol. To avoid unnecessary repetitive training, we have trained and evaluated all of these architectures on nine representative graph datasets, recording detailed metrics including train, validation, and test performance in each epoch, the latency, the number of parameters, etc. Based on our proposed benchmark, the performance of GNN architectures can be directly obtained by a look-up table without any further computation, which enables fair, fully reproducible, and efficient comparisons. To demonstrate its usage, we make in-depth analyses of our proposed NAS-Bench-Graph, revealing several interesting findings for GraphNAS. We also showcase how the benchmark can be easily compatible with GraphNAS open libraries such as AutoGL and NNI. To the best of our knowledge, our work is the first benchmark for graph neural architecture search

ImageNetVC: Zero-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories

Recently, Pretrained Language Models (PLMs) have been serving as general-purpose interfaces, posing a significant demand for comprehensive visual knowledge. However, it remains unclear how well current PLMs and their visually augmented counterparts (VaLMs) can master visual commonsense knowledge. To investigate this, we propose ImageNetVC, a fine-grained, human-annotated dataset specifically designed for zero-shot visual commonsense evaluation across 1,000 ImageNet categories. Utilizing ImageNetVC, we delve into the fundamental visual commonsense knowledge of both unimodal PLMs and VaLMs, uncovering the scaling law and the influence of the backbone model on VaLMs. Furthermore, we investigate the factors affecting the visual commonsense knowledge of large-scale models, providing insights into the development of language models enriched with visual commonsense knowledge. Our code and dataset are available at https://github.com/hemingkx/ImageNetVC

Tissue factor pathway inhibitor-2 induced hepatocellular carcinoma cell differentiation

AbstractTo investigate the effect of over-expression of tissue factor pathway inhibitor-2 (TFPI-2) on the differentiation of hepatocellular carcinoma (HCC) cells (Hep3B and HepG2). The TFPI-2 recombinant adenovirus (pAd-TFPI-2) was constructed using the pAdeasy-1 vector system. Transfected by pAd-TFPI-2, the cell proliferation of HCC cells was evaluated by CCK-8 assay, flow cytometry was used to detect cell apoptosis and CD133 expression. Real-time PCR and Western blot were used to detect the expression levels of markers of hepatocellular cancer stem cells (CSC) and hepatocytes. The over-expression of TFPI-2 significantly suppressed cell proliferation, induced apoptosis, and dramatically decreased the percentage of CD133 cells, which was considered as CSC in HCC. Real-time PCR and Western blot showed that the expression of markers of CSC in Hep3BcellsandHepG2 cells infected with pAd-TFPI-2 was markedly lower than those of the control group (P<0.05), while the expression of markers of hepatocytes was significantly increased (P<0.05). Hence, TFPI-2 could induce the differentiation of hepatocellular carcinoma cells into hepatocytes, and is expected to serve as a novel way for the treatment of HCC

Graph Meets LLMs: Towards Large Graph Models

Large models have emerged as the most recent groundbreaking achievements in artificial intelligence, and particularly machine learning. However, when it comes to graphs, large models have not achieved the same level of success as in other fields, such as natural language processing and computer vision. In order to promote applying large models for graphs forward, we present a perspective paper to discuss the challenges and opportunities associated with developing large graph models. First, we discuss the desired characteristics of large graph models. Then, we present detailed discussions from three key perspectives: representation basis, graph data, and graph models. In each category, we provide a brief overview of recent advances and highlight the remaining challenges together with our visions. Finally, we discuss valuable applications of large graph models. We believe this perspective can encourage further investigations into large graph models, ultimately pushing us one step closer towards artificial general intelligence (AGI). We are the first to comprehensively study large graph models, to the best of our knowledge.Comment: Accepted by NeurIPS 2023 New Frontiers in Graph Learning Workshop. Comments are welcom

LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs?

In an era marked by the increasing adoption of Large Language Models (LLMs) for various tasks, there is a growing focus on exploring LLMs' capabilities in handling web data, particularly graph data. Dynamic graphs, which capture temporal network evolution patterns, are ubiquitous in real-world web data. Evaluating LLMs' competence in understanding spatial-temporal information on dynamic graphs is essential for their adoption in web applications, which remains unexplored in the literature. In this paper, we bridge the gap via proposing to evaluate LLMs' spatial-temporal understanding abilities on dynamic graphs, to the best of our knowledge, for the first time. Specifically, we propose the LLM4DyG benchmark, which includes nine specially designed tasks considering the capability evaluation of LLMs from both temporal and spatial dimensions. Then, we conduct extensive experiments to analyze the impacts of different data generators, data statistics, prompting techniques, and LLMs on the model performance. Finally, we propose Disentangled Spatial-Temporal Thoughts (DST2) for LLMs on dynamic graphs to enhance LLMs' spatial-temporal understanding abilities. Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks. The data and codes will be open-sourced at publication time

Stability and sensitivity characteristic analysis for the hydropower unit considering the sloping roof tailrace tunnel and coupling effect of the power grid

This paper focuses on the stability and dynamic characteristics of the coupled system of nonlinear hydraulic turbine regulating system (HTRS) and power grid (PG). By establishing a nonlinear mathematical model considering the downstream surge chamber and sloping roof tailrace tunnel, the coupling effect and influence mechanism between the hydropower station and power grid are revealed. First, with regard to the coupled system, HTRS considering downstream surge chamber and sloping roof tailrace tunnel and PG model is established. Then, dynamic performance of the coupled system is investigated based on the nonlinear mathematical model as well as Hopf bifurcation theory and validated by numerical simulation. Meanwhile, the impact mechanism of HTRS and PG is revealed by investigating dynamic characteristics. In addition, stability is studied by using eigenvalue method according to the Jacobian matrix of the coupled system. Finally, parameter sensitivity is investigated to quantify parameter effects on system performance. The experimental results indicate that bifurcation line divides the whole proportional–integral adjustment coefficient plane into two parts and the region at the bottom of bifurcation line is stability region. HTRS and PG possess a coupling effect on stable domain and dynamic properties of the coupled system. The variation of HTRS parameters is most significant for the coupled system, especially for the inertia time constant of the hydraulic turbine unit and penstock flow inertia time constant

Memory-Inspired Temporal Prompt Interaction for Text-Image Classification

In recent years, large-scale pre-trained multimodal models (LMM) generally emerge to integrate the vision and language modalities, achieving considerable success in various natural language processing and computer vision tasks. The growing size of LMMs, however, results in a significant computational cost for fine-tuning these models for downstream tasks. Hence, prompt-based interaction strategy is studied to align modalities more efficiently. In this contex, we propose a novel prompt-based multimodal interaction strategy inspired by human memory strategy, namely Memory-Inspired Temporal Prompt Interaction (MITP). Our proposed method involves in two stages as in human memory strategy: the acquiring stage, and the consolidation and activation stage. We utilize temporal prompts on intermediate layers to imitate the acquiring stage, leverage similarity-based prompt interaction to imitate memory consolidation, and employ prompt generation strategy to imitate memory activation. The main strength of our paper is that we interact the prompt vectors on intermediate layers to leverage sufficient information exchange between modalities, with compressed trainable parameters and memory usage. We achieve competitive results on several datasets with relatively small memory usage and 2.0M of trainable parameters (about 1% of the pre-trained foundation model)