Fairness in Graph Mining: A Survey

Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploited in human-centered applications. Recently, algorithmic fairness has been extensively studied in graph-based applications. In contrast to algorithmic fairness on independent and identically distributed (i.i.d.) data, fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling techniques. In this survey, we provide a comprehensive and up-to-date introduction of existing literature under the context of fair graph mining. Specifically, we propose a novel taxonomy of fairness notions on graphs, which sheds light on their connections and differences. We further present an organized summary of existing techniques that promote fairness in graph mining. Finally, we summarize the widely used datasets in this emerging research field and provide insights on current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances

Adversarial Attacks on Fairness of Graph Neural Networks

Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can reduce the bias of predictions on any demographic group (e.g., female) in graph-based applications. Although these methods greatly improve the algorithmic fairness of GNNs, the fairness can be easily corrupted by carefully designed adversarial attacks. In this paper, we investigate the problem of adversarial attacks on fairness of GNNs and propose G-FairAttack, a general framework for attacking various types of fairness-aware GNNs in terms of fairness with an unnoticeable effect on prediction utility. In addition, we propose a fast computation technique to reduce the time complexity of G-FairAttack. The experimental study demonstrates that G-FairAttack successfully corrupts the fairness of different types of GNNs while keeping the attack unnoticeable. Our study on fairness attacks sheds light on potential vulnerabilities in fairness-aware GNNs and guides further research on the robustness of GNNs in terms of fairness. The open-source code is available at https://github.com/zhangbinchi/G-FairAttack.Comment: 32 pages, 5 figure

Interpreting Unfairness in Graph Neural Networks via Training Node Attribution

Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving graph analytical problems in various real-world applications. Nevertheless, GNNs could potentially render biased predictions towards certain demographic subgroups. Understanding how the bias in predictions arises is critical, as it guides the design of GNN debiasing mechanisms. However, most existing works overwhelmingly focus on GNN debiasing, but fall short on explaining how such bias is induced. In this paper, we study a novel problem of interpreting GNN unfairness through attributing it to the influence of training nodes. Specifically, we propose a novel strategy named Probabilistic Distribution Disparity (PDD) to measure the bias exhibited in GNNs, and develop an algorithm to efficiently estimate the influence of each training node on such bias. We verify the validity of PDD and the effectiveness of influence estimation through experiments on real-world datasets. Finally, we also demonstrate how the proposed framework could be used for debiasing GNNs. Open-source code can be found at https://github.com/yushundong/BIND

FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs

Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose to incorporate few-shot learning frameworks for fast adaptations to graph classes with limited labeled graphs. Specifically, these works propose to accumulate meta-knowledge across diverse meta-training tasks, and then generalize such meta-knowledge to the target task with a disjoint label set. However, existing methods generally ignore task correlations among meta-training tasks while treating them independently. Nevertheless, such task correlations can advance the model generalization to the target task for better classification performance. On the other hand, it remains non-trivial to utilize task correlations due to the complex components in a large number of meta-training tasks. To deal with this, we propose a novel few-shot learning framework FAITH that captures task correlations via constructing a hierarchical task graph at different granularities. Then we further design a loss-based sampling strategy to select tasks with more correlated classes. Moreover, a task-specific classifier is proposed to utilize the learned task correlations for few-shot classification. Extensive experiments on four prevalent few-shot graph classification datasets demonstrate the superiority of FAITH over other state-of-the-art baselines.Comment: IJCAI-ECAI 202

Accurate imaging in the processes of formation and inhibition of drug-induced liver injury by an activable fluorescent probe for ONOO−

Herein, an activable fluorescent probe for peroxynitrite (ONOO−), named NOP, was constructed for the accurate imaging in the processes of formation and inhibition of drug-induced liver injury induced by Acetaminophen (APAP). During the in-solution tests on the general optical properties, the probe showed advantages including good stability, wide pH adaption, high specificity and sensitivity in the monitoring of ONOO−. Subsequently, the probe was further applied in the model mice which used APAP to induce the injury and used inhibiting agents (GSH, Glu, NAC) to treat the induced injury. The construction of the liver injury model was confirmed by the pathological staining and the serum indexes including ALT, AST, ALP, TBIL as well as LDH. During the formation of the drug-induced liver injury, the fluorescence in the red channel enhanced in both time-dependent and dose-dependent manners. In inhibition tests, the inhibition of the liver injury exhibited the reduction of the fluorescence intensity. Therefore, NOP could achieve the accurate imaging in the processes of formation and inhibition of drug-induced liver injury. The information here might be helpful for the early diagnosis and the screening of potent treating candidates in liver injury cases

Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage

Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. However, they may inherit historical prejudices from training data, leading to discriminatory bias in predictions. Although some work has developed fair GNNs, most of them directly borrow fair representation learning techniques from non-graph domains without considering the potential problem of sensitive attribute leakage caused by feature propagation in GNNs. However, we empirically observe that feature propagation could vary the correlation of previously innocuous non-sensitive features to the sensitive ones. This can be viewed as a leakage of sensitive information which could further exacerbate discrimination in predictions. Thus, we design two feature masking strategies according to feature correlations to highlight the importance of considering feature propagation and correlation variation in alleviating discrimination. Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation. Given the learned fair views, we adaptively clamp weights of the encoder to avoid using sensitive-related features. Experiments on real-world datasets demonstrate that FairVGNN enjoys a better trade-off between model utility and fairness. Our code is publicly available at https://github.com/YuWVandy/FairVGNN

Research on Plastic Zone Evolution Law of Surrounding Rock of Gob-Side Entry Retaining under Typical Roof Conditions in Deep Mine

To solve the control problem of the surrounding rock of gob-side entry retaining under typical roof conditions in deep mines, we conduct theoretical analysis, numerical simulation, and actual measurements. Starting from the plastic zone of the surrounding rock, the serious damage area, the degree and scope of damage, and the dynamic evolution process of the surrounding rock of the gob-side entry retaining are systematically analyzed under four typical roof conditions in deep mines; the expansion and evolution laws of the plastic zone of the surrounding rock are expounded; and a key control technology is proposed. The results indicate that (1) the plastic failure of surrounding rock was concentrated mainly on the coal side and on the floor, especially in the filling body. The plastic zone of the surrounding rock of the gob-side entry retaining with the thick immediate roof was widely distributed and deep, but the plastic failure of the filling body was not obvious. The plastic failure of the surrounding rock of the gob-side entry retaining with the compound roof was mainly concentrated on the roof, filling body, and floor of the filling area. (2) According to the typical roof conditions of the deep gob-side entry retaining, the order of the degree of damage to the surrounding rock was as follows: thick immediate roof, compound roof, thin immediate roof, and thick-hard roof. (3) A “multisupport structure” control system is proposed for the gob-side entry retaining in a deep mine, including measures for enhancing the bearing performance of the anchorage system, increasing the strength of the cataclastic coal-rock mass, enhancing the bearing capacity of the filling body, and increasing the bearing capacity on the tunnel side. The proposed technology was applied to the deep gob-side entry retaining project in the east area of Panyi Mine, and it effectively fulfilled the reuse requirements of gob-side entry retaining in deep mines

The Reduction of Peripheral Blood CD4+ T Cell Indicates Persistent Organ Failure in Acute Pancreatitis.

ObjectiveFew data are available on the potential role of inflammatory mediators and T lymphocytes in persistent organ failure (POF) in acute pancreatitis (AP). We conducted a retrospective study to characterize their role in the progression of POF in AP.MethodsA total of 69 AP patients presented within 24 hours from symptom onset developing organ failure (OF) on admission were included in our study. There were 39 patients suffering from POF and 30 from transient OF (TOF). On the 1st, 3rd and 7th days after admission, blood samples were collected for biochemical concentration monitoring including serum IL-1β, IL-6, TNF-α and high-sensitivity C-reactive protein (hs-CRP). The proportions of peripheral CD4(+) and CD8(+) T lymphocytes were assessed based on flow cytometry simultaneously.ResultsPatients with POF showed a significantly higher value of IL-1β and hs-CRP on day 7 compared with the group of TOF (P ConclusionsThe reduction of peripheral blood CD4(+) T lymphocytes is associated with POF in AP, and may act as a potential predictor

Spatial heterogeneity of food web structure in a large shallow eutrophic lake (Lake Taihu, China): implications for eutrophication process and management

The knowledge of food web spatial heterogeneity is important for ecologists and lake managers to understand ecosystem complexity and lake management. Lake Taihu, a large shallow eutrophic lake in China, has two distinct zones: algae- and macrophyte-dominated. In this study, we assessed the spatial heterogeneity of food webs in the two lake zones using stable isotope analysis and isotope mixing model. The basal sources and consumers showed significant differences in delta C-13 and delta N-15 ratios between the two lake zones, except for the filter-feeding fishes. Overall, more delta C-13-depleted and delta N-15-enriched ratios were found in the algae- than the macrophyte-dominated zones for basal sources and consumers. Although the consumers in the algae-dominant zone had higher average delta N-15 values, the food web of the macrophyte-dominated zone had longer food chain length and more diverse trophic linkages. These spatial differences may have resulted from resource availability and environmental stress of Lake Taihu ecosystem. Factors associated with spatial trophic heterogeneity should be considered for the management and restoration of this shallow eutrophic lake