Hetero2^2Net: Heterophily-aware Representation Learning on Heterogenerous Graphs

Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of common graph neural networks (GNNs) in handling homogeneous graphs with heterophily, little work has been conducted on investigating the heterophily properties in the context of heterogeneous graphs. To bridge this research gap, we identify the heterophily in heterogeneous graphs using metapaths and propose two practical metrics to quantitatively describe the levels of heterophily. Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily. To address the challenge, we present Hetero$^2$Net, a heterophily-aware HGNN that incorporates both masked metapath prediction and masked label prediction tasks to effectively and flexibly handle both homophilic and heterophilic heterogeneous graphs. We evaluate the performance of Hetero$^2$Net on five real-world heterogeneous graph benchmarks with varying levels of heterophily. The results demonstrate that Hetero$^2$Net outperforms strong baselines in the semi-supervised node classification task, providing valuable insights into effectively handling more complex heterogeneous graphs.Comment: Preprin

LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning

Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications, such as social networks and e-commerce, involve temporal graphs where nodes and edges are dynamically evolving. Temporal graph neural networks (TGNNs) have progressively emerged as an extension of GNNs to address time-evolving graphs and have gradually become a trending research topic in both academics and industry. Advancing research and application in such an emerging field necessitates the development of new tools to compose TGNN models and unify their different schemes for dealing with temporal graphs. In this work, we introduce LasTGL, an industrial framework that integrates unified and extensible implementations of common temporal graph learning algorithms for various advanced tasks. The purpose of LasTGL is to provide the essential building blocks for solving temporal graph learning tasks, focusing on the guiding principles of user-friendliness and quick prototyping on which PyTorch is based. In particular, LasTGL provides comprehensive temporal graph datasets, TGNN models and utilities along with well-documented tutorials, making it suitable for both absolute beginners and expert deep learning practitioners alike.Comment: Preprint; Work in progres

Analyses of thermal performance and pressure drop in a plate heat exchanger filled with ferrofluids under a magnetic field

This paper experimentally investigates the effect of various magnetic fields on the performance of a plate heat exchanger filled with ferrofluids. Spherical nanoparticles Fe3O4 with an average diameter of 20 nm are dispersed into DI-water to synthesize the ferrofluid. Thermal performance and flow characteristics of the ferrofluid with 0.1% particle concentration are investigated based on various arrangements of magnets outside the plate heat exchanger. Effects of magnetic field strength and distribution are thoroughly studied concerning the performance of the heat exchanger with various ferrofluid flow rates. Results indicate that with a vertical arrangement of two magnets side by side outside the sidewalls, 21.8% increase in average Nusselt number and 10.0% reduction in average pressure drop are achieved compared to the cases without a magnetic field. Novel configurations of magnets are first discussed in a plate heat exchanger. Ferrofluid flow control is achieved under a measurable magnetic field strength and different flow rates. It is well known that enhancement of thermal performance in the plate heat exchanger is accompanied with a reduction of resistance loss. Deposition of magnetic particles and blockage in the channel of the plate heat exchanger will also be weakened based on results from this research

Assembly of carbon nanotubes on a nanoporous gold electrode for acetylcholinesterase biosensor design

An electrochemical sensing platform based on assembly of carbon nanotubes on a nanoporous gold electrode is described for highly sensitive detection of organophosphate pesticides. The nanoporous gold film (NPG) electrode is fabricated by an alloying/dealloying process, which possess high electroactive surface area and is an excellent substrate for sensor design. The NPG functionalized with cysteamine allows the immobilization of carbon nanotubes on the electrode with the self-assembly technique. The carboxylated carbon nanotubes are further linkered with acetylcholinesterase (AChE) for amperometric sensing of pesticides. The immobilized AChE, as a model, shows excellent activity to its substrate and allows a quantitative measurement of organophosphate pesticides. Under the optimal experimental conditions, the inhibition of malathion is proportional to its concentration in the range of 0.001-0.5 mu g mL(-1) with a detection limit of 0.5 ng mL(-1). The proposed method shows good reproducibility and high stability, which provides a new avenue for electrochemical biosensor design. (C) 2014 Elsevier B.V. All rights reserved

Towards Learning to Discover Money Laundering Sub-network in Massive Transaction Network

Anti-money laundering (AML) systems play a critical role in safeguarding global economy. As money laundering is considered as one of the top group crimes, there is a crucial need to discover money laundering sub-network behind a particular money laundering transaction for a robust AML system. However, existing rule-based methods for money laundering sub-network discovery is heavily based on domain knowledge and may lag behind the modus operandi of launderers. Therefore, in this work, we first address the money laundering sub-network discovery problem with a neural network based approach, and propose an AML framework AMAP equipped with an adaptive sub-network proposer. In particular, we design an adaptive sub-network proposer guided by a supervised contrastive loss to discriminate money laundering transactions from massive benign transactions. We conduct extensive experiments on real-word datasets in AliPay of Ant Group. The result demonstrates the effectiveness of our AMAP in both money laundering transaction detection and money laundering sub-network discovering. The learned framework which yields money laundering sub-network from massive transaction network leads to a more comprehensive risk coverage and a deeper insight to money laundering strategies

GUARD: Graph Universal Adversarial Defense

Graph convolutional networks (GCNs) have been shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in security-critical scenarios. To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks. However, current defense approaches are typically designed to prevent GCNs from untargeted adversarial attacks and focus on overall performance, making it challenging to protect important local nodes from more powerful targeted adversarial attacks. Additionally, a trade-off between robustness and performance is often made in existing research. Such limitations highlight the need for developing an effective and efficient approach that can defend local nodes against targeted attacks, without compromising the overall performance of GCNs. In this work, we present a simple yet effective method, named Graph Universal Adversarial Defense (GUARD). Unlike previous works, GUARD protects each individual node from attacks with a universal defensive patch, which is generated once and can be applied to any node (node-agnostic) in a graph. GUARD is fast, straightforward to implement without any change to network architecture nor any additional parameters, and is broadly applicable to any GCNs. Extensive experiments on four benchmark datasets demonstrate that GUARD significantly improves robustness for several established GCNs against multiple adversarial attacks and outperforms state-of-the-art defense methods by large margins.Comment: Accepted by CIKM 2023. Code is publicly available at https://github.com/EdisonLeeeee/GUAR

Electrochemical Evaluation of the Mechanism of Acetylcholinesterase Inhibition Based on an Electrodeposited Thin Film

An interface embedded gold nanoparticles in sol-gel thin film was constructed by one-step electrochemical deposition. Acetylcholinesterase (AChE) was physically absorbed onto to the interface to form a thin enzymatic layer. The proposed thin enzymatic layer, having kinetics similar to that of the enzyme in solution, provides an ideal sensing platform to electrochemically evaluate the chemical mechanism of enzyme inhibition. Lineweaver-Burk plot and surface plasmon resonance confirmed that the inhibition of AChE by malathion followed an irreversible mechanism and was a mixed type of competitive and noncompetitive. On the contrary, the degrees of inhibition by Pb2+ and Fe3+ were independent of the incubation time and the AChE concentrations, showing the reversibility of the inhibition. Furthermore, UV-vis absorption spectra indicated that the AChE mediated the hydrolysis of acetylthiocholine to yield a reducing agent thiocholine that reduced Fe3+ to Fe2+ and Fe2+ presented an effect of activation. To meet the demand of the biosensor design, we further investigated the relationship between inhibition percentage and both incubation time and inhibitor concentration. The enzyme's sensitivity to solvent effects and reactivation of the biosensor were also evaluated. It is anticipated that a rapid evaluation of the chemical mechanism of AChE inhibition could paves the way to rationally design biosensors and new compounds, as candidates for the treatment of Alzheimer's disease and pesticides.An interface embedded gold nanoparticles in sol-gel thin film was constructed by one-step electrochemical deposition. Acetylcholinesterase (AChE) was physically absorbed onto to the interface to form a thin enzymatic layer. The proposed thin enzymatic layer, having kinetics similar to that of the enzyme in solution, provides an ideal sensing platform to electrochemically evaluate the chemical mechanism of enzyme inhibition. Lineweaver-Burk plot and surface plasmon resonance confirmed that the inhibition of AChE by malathion followed an irreversible mechanism and was a mixed type of competitive and noncompetitive. On the contrary, the degrees of inhibition by Pb2+ and Fe3+ were independent of the incubation time and the AChE concentrations, showing the reversibility of the inhibition. Furthermore, UV-vis absorption spectra indicated that the AChE mediated the hydrolysis of acetylthiocholine to yield a reducing agent thiocholine that reduced Fe3+ to Fe2+ and Fe2+ presented an effect of activation. To meet the demand of the biosensor design, we further investigated the relationship between inhibition percentage and both incubation time and inhibitor concentration. The enzyme's sensitivity to solvent effects and reactivation of the biosensor were also evaluated. It is anticipated that a rapid evaluation of the chemical mechanism of AChE inhibition could paves the way to rationally design biosensors and new compounds, as candidates for the treatment of Alzheimer's disease and pesticides

CdTe nanocrystal-based electrochemical biosensor for the recognition of neutravidin by anodic stripping voltammetry at electrodeposited bismuth film

CdTe quantum dots (QDs)-based electrochemical sensor for recognition of neutravidin, as a model protein, using anodic stripping voltammetry at electrodeposited bismuth film is presented. This biosensor involves the immobilization of the captured QDs conjugates which was dissolved with 1 M HCl solution to release cadmium ions and metal components were quantified by anodic stripping voltammetry after a 3-min accumulation at -1.2 V on bismuth-film electrode (BiFE) of the biotin, served as recognition element, onto the gold surface in connection with a cysteamine self-assembled monolayer. The modification procedure was characterized by electrochemical impedance spectroscopy and atomic force microscopy. We exploit QDs as labels for amplifying signal output and monitoring the extent of competition process between CdTe-labeled neutravidin and the target neutravidin for the limited binding sites on biotin. As expected for the competitive mechanism, the recognition event thus yields distinct cadmium stripping voltammetric current peak, whose response decreases upon increasing the level of target neutravidin concentrations. Under optimal conditions, the voltammetric response is highly linear over the range of 0.5-100 ng L-1 neutravidin and the limit of detection is estimated to be 0.3 ng L-1 (5 nM). Unlike earlier two-step sandwich bioassays, the present protocol relies on a one-step competitive assay, which is more accurate and sensitive, showing great promise for rapid, simple and cost-effective analysis of protein. (C) 2008 Elsevier B.V. All rights reserved.National Natural Science Foundation of China [20705010, 20775064, 20735002, 20772038]; Research Fund for the Doctoral Program of Higher Education of China [20070511015]; Program for Distinguished Young Scientist of Hubei Province [2007ABB017