Integrating Graphs with Large Language Models: Methods and Prospects

Large language models (LLMs) such as GPT-4 have emerged as frontrunners, showcasing unparalleled prowess in diverse applications, including answering queries, code generation, and more. Parallelly, graph-structured data, an intrinsic data type, is pervasive in real-world scenarios. Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest. This paper bifurcates such integrations into two predominant categories. The first leverages LLMs for graph learning, where LLMs can not only augment existing graph algorithms but also stand as prediction models for various graph tasks. Conversely, the second category underscores the pivotal role of graphs in advancing LLMs. Mirroring human cognition, we solve complex tasks by adopting graphs in either reasoning or collaboration. Integrating with such structures can significantly boost the performance of LLMs in various complicated tasks. We also discuss and propose open questions for integrating LLMs with graph-structured data for the future direction of the field

An Empirical Analysis of the Relationship between Stock Price of Resource Industry and Price of Staple Products

Abstract The price of staple commodities, represented by oil and copper, has rewritten the record many times over the past several years. It is experiencing a ���¢��������super spike���¢�������� which insiders forecast will last 10 years. According to a survey on 150 private banks, hedge funds, trust funds and pension funds made by Barclays, one third of the respondents held the stocks of staple commodities, 89% of institutions intended to purchase such stocks by 2008, and more than one third of institutions planned to raise the proportion of staple commodity stocks to over 10%. In 1998, Jim Rogers, a global investment guru, founded the Rogers Raw Materials Index, which was renamed the Rogers International Commodity Index (RICI) afterwards. The index covers 38 commodities, including silk fabrics, soybean, gold and oil. During recent seven years, the RICI had soared more than 200%, compared with the US Treasury Bond Index which climbed merely 72% and the US Stock Index with only an 18% rise. The survey shows that the value of staple commodity stocks does have a close relationship with the price of staple commodities

The Impact of Corporate Governance and Audit Quality on Earnings Management: Evidence from the Chinese Information Technology Industry.

This study integrates the characteristics of corporate governance and audit quality, and explores the impact of the two on earnings management. Since there are few existing literature concerning the rapid development of China's information technology industry in recent years, this study chooses Chinese information industry as the research background. In addition, this study selects listed companies in the information technology industry from 2017 to 2019 as samples. According to the modified Jones model, the absolute value of discretionary accruals is selected as the dependent variable to describe the degree of company earnings management. The research results show that there is a significant negative correlation between board size, director compensation, Big4 and 8 auditors and earnings management. On the other hand, gender diversity and audit fees have a significant and positive relationship with earnings management. In view of the board meeting, and the audit committee do not show its relevance to earnings management

PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection

Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in various domains such as medicine, social networks, and e-commerce. However, challenges have arisen due to the diversity of anomalies and the dearth of labeled data. Existing methodologies - reconstruction-based and contrastive learning - while effective, often suffer from efficiency issues, stemming from their complex objectives and elaborate modules. To improve the efficiency of GAD, we introduce a simple method termed PREprocessing and Matching (PREM for short). Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities. Comprising two modules - a pre-processing module and an ego-neighbor matching module - PREM eliminates the necessity for message-passing propagation during training, and employs a simple contrastive loss, leading to considerable reductions in training time and memory usage. Moreover, through rigorous evaluations of five real-world datasets, our method demonstrated robustness and effectiveness. Notably, when validated on the ACM dataset, PREM achieved a 5% improvement in AUC, a 9-fold increase in training speed, and sharply reduce memory usage compared to the most efficient baseline.Comment: Accepted by IEEE International Conference of Data Mining 2023 (ICDM 2023

Photocatalytic Activity of MOF-derived Cu2O/Cu/C/Ag Porous Composites

Cu2O/Cu/C/Ag porous composite was synthesized by heat-treatment and wet-chemical method using a typical metal-organic framework (Cu-BTC) as  precursor. The samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS) and  ultraviolet-visible spectroscopy (UV-vis). The results showed that the originalstructure of Cu-BTC was retained by high temperature calcination in nitrogen atmosphere. Uniform doping of Cu, C and Ag provided a triple trapping of photogenerated electron hole pairs and the Cu2O/Cu/C/Ag exhibited an enhanced photocatalytic activity for degradation of Congo Red under visible light irradiation. Heat-treatment of the MOFs with high temperature is afacile and effective way for preparation of photocatalytic composite with desirable properties.Keywords: Photocatalyst, cuprous oxide, dye degradation, Cu-BTC

Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion

Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increase of graph sparsity. This problem is also exacerbated because of the widespread existence of sparse KGs in practical applications. To alleviate this challenge, we present a novel framework, LR-GCN, that is able to automatically capture valuable long-range dependency among entities to supplement insufficient structure features and distill logical reasoning knowledge for sparse KGC. The proposed approach comprises two main components: a GNN-based predictor and a reasoning path distiller. The reasoning path distiller explores high-order graph structures such as reasoning paths and encodes them as rich-semantic edges, explicitly compositing long-range dependencies into the predictor. This step also plays an essential role in densifying KGs, effectively alleviating the sparse issue. Furthermore, the path distiller further distills logical reasoning knowledge from these mined reasoning paths into the predictor. These two components are jointly optimized using a well-designed variational EM algorithm. Extensive experiments and analyses on four sparse benchmarks demonstrate the effectiveness of our proposed method.Comment: 12 pages, 5 figure