Cross-domain graph anomaly detection(CD-GAD)is a promising task that leverages knowledge from a labelled source graph to guide anomaly detection on an unlabelled target graph.CD-GAD classifies anomalies as unique or c...Cross-domain graph anomaly detection(CD-GAD)is a promising task that leverages knowledge from a labelled source graph to guide anomaly detection on an unlabelled target graph.CD-GAD classifies anomalies as unique or common based on their presence in both the source and target graphs.However,existing models often fail to fully explore domain-unique knowledge of the target graph for detecting unique anomalies.Additionally,they tend to focus solely on node-level differences,overlooking structural-level differences that provide complementary information for common anomaly detection.To address these issues,we propose a novel method,Synthetic Graph Anomaly Detection via Graph Transfer and Graph Decouple(GTGD),which effectively detects common and unique anomalies in the target graph.Specifically,our approach ensures deeper learning of domain-unique knowledge by decoupling the reconstruction graphs of common and unique features.Moreover,we simulta-neously consider node-level and structural-level differences by transferring node and edge information from the source graph to the target graph,enabling comprehensive domain-common knowledge representation.Anomalies are detected using both common and unique features,with their synthetic score serving as the final result.Extensive experiments demonstrate the effectiveness of our approach,improving an average performance by 12.6%on the AUC-PR compared to state-of-the-art methods.展开更多
The exploration of new heterojunction materials is of great significance in reducing the cost of existing noble metal catalysts and thus realizing the large-scale application of electrocatalytic hydrolysis technology....The exploration of new heterojunction materials is of great significance in reducing the cost of existing noble metal catalysts and thus realizing the large-scale application of electrocatalytic hydrolysis technology.Herein,a novel CoP/CoMoP_(2) heterojunction was synthesized and served as a hydrogen evolution reaction(HER)electrocatalyst.The heterojunction has morphology of nanoporous structure,which is conducive to exposing more active sites and facilitating bubbles transport.The charge distribution is optimized by a strong interface interaction between CoP and CoMoP_(2).The catalyst’s conductivity and the adsorption properties of the intermediates have both been enhanced.CoP/CoMoP_(2) demonstrates excellent HER activity with an overpotential of 93.6 mV at 10 mA∙cm^(-2),which is competitive with the reported performance of analogous electrocatalysts.This work provides insights into the development of innovative phosphide-based heterojunction electrocatalysts.展开更多
基金supported by the National Nature Science Foundation of China,Grant/Award Numbers:62337001,62037001“Pioneer”and“Leading Goose”R&D Program of Zhejiang,Grant/Award Number:2022C03106.
文摘Cross-domain graph anomaly detection(CD-GAD)is a promising task that leverages knowledge from a labelled source graph to guide anomaly detection on an unlabelled target graph.CD-GAD classifies anomalies as unique or common based on their presence in both the source and target graphs.However,existing models often fail to fully explore domain-unique knowledge of the target graph for detecting unique anomalies.Additionally,they tend to focus solely on node-level differences,overlooking structural-level differences that provide complementary information for common anomaly detection.To address these issues,we propose a novel method,Synthetic Graph Anomaly Detection via Graph Transfer and Graph Decouple(GTGD),which effectively detects common and unique anomalies in the target graph.Specifically,our approach ensures deeper learning of domain-unique knowledge by decoupling the reconstruction graphs of common and unique features.Moreover,we simulta-neously consider node-level and structural-level differences by transferring node and edge information from the source graph to the target graph,enabling comprehensive domain-common knowledge representation.Anomalies are detected using both common and unique features,with their synthetic score serving as the final result.Extensive experiments demonstrate the effectiveness of our approach,improving an average performance by 12.6%on the AUC-PR compared to state-of-the-art methods.
基金supported by the National Natural Science Foundation of China(Nos.52271184,52072255)the Natural Science Foundation of Zhejiang Province,China(Nos.LY23E020001,LTY20E020001).
文摘The exploration of new heterojunction materials is of great significance in reducing the cost of existing noble metal catalysts and thus realizing the large-scale application of electrocatalytic hydrolysis technology.Herein,a novel CoP/CoMoP_(2) heterojunction was synthesized and served as a hydrogen evolution reaction(HER)electrocatalyst.The heterojunction has morphology of nanoporous structure,which is conducive to exposing more active sites and facilitating bubbles transport.The charge distribution is optimized by a strong interface interaction between CoP and CoMoP_(2).The catalyst’s conductivity and the adsorption properties of the intermediates have both been enhanced.CoP/CoMoP_(2) demonstrates excellent HER activity with an overpotential of 93.6 mV at 10 mA∙cm^(-2),which is competitive with the reported performance of analogous electrocatalysts.This work provides insights into the development of innovative phosphide-based heterojunction electrocatalysts.
