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

The effect of salience on Chinese pun comprehension : a visual world paradigm study

The present study adopted the printed-word visual world paradigm to investigate the salience effect on Chinese pun comprehension. In such an experiment, participants listen to a spoken sentence while looking at a visual display of four printed words (including a semantic competitor, a phonological competitor, and two unrelated distractors). Previous studies based on alphabetic languages have found robust phonological effects (participants fixated more at phonological competitors than distractors during the unfolding of the spoken target words), while controversy remains regarding the existence of a similar semantic effect. A recent Chinese study reported reliable semantic effects in two experiments using this paradigm, suggesting that Chinese participants could actively map the semantic input from the auditory modality with the semantic information retrieved from printed words. In light of their study, we designed an experiment with two conditions: a replication condition to test the validity of using the printed-word world paradigm in Chinese semantic research, and a pun condition to assess the role played by salience during pun comprehension. Indeed, global analyses have revealed robust semantic effects in both experimental conditions, where participants were found more attracted to the semantic competitors than to the distractors with the emergence of target words. More importantly, the local analyses from the pun condition have shown that the participants were more attracted to the semantic competitors related to the salient meaning of the ambiguous word in a pun than to those related to the less salient meanings within 200 ms after target word offset. This finding suggests that the salient meaning of the ambiguous word in a pun is activated and assessed faster than its less salient counterpart. The initial advantage observed in the present study is consistent with the prediction of the graded salience hypothesis rather than the direct access model

Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs

Real-world graphs generally have only one kind of tendency in their connections. These connections are either homophily-prone or heterophily-prone. While graphs with homophily-prone edges tend to connect nodes with the same class (i.e., intra-class nodes), heterophily-prone edges tend to build relationships between nodes with different classes (i.e., inter-class nodes). Existing GNNs only take the original graph during training. The problem with this approach is that it forgets to take into consideration the ``missing-half" structural information, that is, heterophily-prone topology for homophily-prone graphs and homophily-prone topology for heterophily-prone graphs. In our paper, we introduce Graph cOmplementAry Learning, namely GOAL, which consists of two components: graph complementation and complemented graph convolution. The first component finds the missing-half structural information for a given graph to complement it. The complemented graph has two sets of graphs including both homophily- and heterophily-prone topology. In the latter component, to handle complemented graphs, we design a new graph convolution from the perspective of optimisation. The experiment results show that GOAL consistently outperforms all baselines in eight real-world datasets.Comment: Accepted by ICML 202

The roles of familiarity and context in processing Chinese xiehouyu : an ERP study

This study conducts an ERP experiment to explore the online processing mechanism of Chinese xiehouyu, a subcategory of Chinese idiomatic expressions with a metaphorical two-part allegorical saying, regarded as a non-literal language construct. Using a 2 × 2 design, (high familiarity (HF)/low familiarity (LF)) × (literally-biasing context (LC)/metaphorically-biasing context (MC)), the researchers have obtained the following findings: (1) familiarity plays an important role in Chinese xiehouyu processing, i.e. the metaphorical meaning of a HF Chinese xiehouyu can be directly activated while that of a LF one has to be derived from its literal meaning first; (2) contextual information also weighs in the process, i.e. the metaphorical meaning of a Chinese xiehouyu can be promoted in MC condition but suppressed in LC condition; (3) the interactive effect of familiarity and contextual information can be explained by the career of metaphor hypothesis; and (4) the Standard Pragmatic Model (SPM) of non-literal languages can explain the processing of LF xiehouyu, and the Direct Access Model (DAM) may to some extent account for the mechanism of HF one but fails to explain the case of LF one, while the Graded Salience Hypothesis (GSH) can provide an acceptable explanation for the processing mechanism of Chinese xiehouyus of varied familiarity

Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence of heterophilic edges that connect nodes with distinct attributes. As a result, current methods are hard to generalize to heterophilic graphs where dissimilar nodes are widely connected, and also vulnerable to adversarial attacks. To address this issue, we propose a novel unsupervised Graph Representation learning method with Edge hEterophily discriminaTing (GREET) which learns representations by discriminating and leveraging homophilic edges and heterophilic edges. To distinguish two types of edges, we build an edge discriminator that infers edge homophily/heterophily from feature and structure information. We train the edge discriminator in an unsupervised way through minimizing the crafted pivot-anchored ranking loss, with randomly sampled node pairs acting as pivots. Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges. With an effective interplaying scheme, edge discriminating and representation learning can mutually boost each other during the training phase. We conducted extensive experiments on 14 benchmark datasets and multiple learning scenarios to demonstrate the superiority of GREET.Comment: 14 pages, 7 tables, 6 figures, accepted by AAAI 202

A Survey on Fairness-aware Recommender Systems

As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era. However, as people become more dependent on them, recent studies show that recommender systems potentially own unintentional impacts on society and individuals because of their unfairness (e.g., gender discrimination in job recommendations). To develop trustworthy services, it is crucial to devise fairness-aware recommender systems that can mitigate these bias issues. In this survey, we summarise existing methodologies and practices of fairness in recommender systems. Firstly, we present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, after introducing datasets and evaluation metrics applied to assess the fairness of recommender systems, we will delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications. Subsequently, we highlight the connection between fairness and other principles of trustworthy recommender systems, aiming to consider trustworthiness principles holistically while advocating for fairness. Finally, we summarize this review, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness, with the ultimate goal of fostering the development of fairness-aware recommender systems.Comment: 27 pages, 9 figure

On the use of an explicit chemical mechanism to dissect peroxy acetyl nitrate formation.

Peroxy acetyl nitrate (PAN) is a key component of photochemical smog and plays an important role in atmospheric chemistry. Though it has been known that PAN is produced via reactions of nitrogen oxides (NOx) with some volatile organic compounds (VOCs), it is difficult to quantify the contributions of individual precursor species. Here we use an explicit photochemical model--Master Chemical Mechanism (MCM) model--to dissect PAN formation and identify principal precursors, by analyzing measurements made in Beijing in summer 2008. PAN production was sensitive to both NOx and VOCs. Isoprene was the predominant VOC precursor at suburb with biogenic impact, whilst anthropogenic hydrocarbons dominated at downtown. PAN production was attributable to a relatively small class of compounds including NOx, xylenes, trimethylbenzenes, trans/cis-2-butenes, toluene, and propene. MCM can advance understanding of PAN photochemistry to a species level, and provide more relevant recommendations for mitigating photochemical pollution in large cities

Biological control agents colonize litchi fruit during storage and stimulate physiological responses to delay pericarp browning

IntroductionLitchi is an economically important fruit in subtropical countries, but pericarp browning can limit its shelf life outside of controlled storage conditions. Effective and sustainable biological control strategies are needed to protect fruit against postharvest browning.Results and DiscussionIn this study, we show that the four bacterial strains Bacillus licheniformis HS10, B. amyloliquefaciens LI24 and PP19, and Exiguobacterium acetylicum SI17 can delay fruit browning in both laboratory trials (LTs) and field plus laboratory trials (FLTs). Strains HS10, LI24, PP19 and SI17 showed 47.74%, 35.39%, 33.58% and 32.53% browning-inhibitory efficacy respectively at 180 h in LT. Litchi sarcocarp interior sourced isolate SI17 showed 74.05% inhibit-brown efficacy at 216 h in FLTs, performing better in FLT than in LT. Furthermore, strains PP19 and SI17 colonized the fruit pericarp and increased total phenolic and anthocyanin contents but decreased peroxidase and polyphenol oxidase activity. This is the first report of E. acetylicum (SI17) and B. licheniformis (HS10) strains acting as biological control agents (BCAs) to delay postharvest browning in litchi fruit. We conclude that PP19 and SI17 are promising BCAs against fruit browning, and their application could be effective for prolonging the shelf life of harvested litchi fruit

Nitrite production from urine for sulfide control in sewers

Most commonly used methods for sewer sulfide control involves dosing chemical agents to wastewater, which incurs high operational costs. Here, we propose and demonstrate a cost-effective and environmentally attractive approach to sewer sulfide control through urine separation and its subsequent conversion to nitrite prior to intermittent dosage to sewers. Urine collected from a male toilet urinal was fed to laboratory-scale sequencing batch reactors. The reactors stably converted roughly 50% of the nitrogen in urine to nitrite, with high abundance (at 17.46%) of known ammonia-oxidizing bacteria (AOB) of the genus Nitrosomonas, and absence (below detection level) of typical nitrite-oxidizing bacteria of the genus Nitrospira, according to 454 pyrosequencing analysis. The stable nitrite production was achieved at both relatively high (1.0–2.0 mg/L) and low (0.2–0.3 mg/L) dissolved oxygen concentrations. Dosing tests in laboratory-scale sewer systems confirmed the sulfide control effectiveness of free nitrous acid generated from urine. Life cycle assessment indicated that, compared with commodity chemicals, nitrite/free nitrous acid (FNA) production from urine for sulfide control in sewers would lower the operational costs by approximately 2/3 and greenhouse gas (GHG) emissions by 1/3 in 20 years

CW dual-frequency MOPA laser with frequency separation of 45 GHz

A CW dual-frequency master oscillator power amplifier (MOPA) laser system with dozens of gigahertz (GHz) frequency separation is presented. The MOPA system consists of a monolithic microchip seed laser and a double-end pumped traveling wave power amplifier. The short length of seed laser cavity guarantees the seed signal with a large frequency separation (above 53 GHz) but low output power (below 247.8 mW). By adding a long and low-doped active medium laser amplifier stage, a significant increase in laser power and an improvement in beam quality are obtained. After fine temperature tuning of seed laser cavity for spectra matching , a 2.40 W dual-frequency laser signal with 45 GHz frequency separation is achieved