Titlebook: Handbuch des Umweltschutzes und der Umweltschutztechnik; Band 2: Produktions- Heinz Brauer Book 1996 Springer-Verlag Berlin Heidelberg 1996

Mystic

诱导

R. Jansen,P. Külpmannntities and relation types. Most existing methods only concentrate on knowledge triples, ignoring logic rules which contain rich background knowledge. Although there has been some work aiming at leveraging both knowledge triples and logic rules, they ignore the transitivity and asymmetry of logic ru

Antagonism

圆木可阻碍

artless

F. Mosers, graph neural networks (GNNs) have been successfully applied in many embedding-based EA methods. However, existing GNN-based methods either suffer from the structural heterogeneity issue that especially appears in the real KG distributions or ignore the heterogeneous representation learning for un

美色花钱

obtained from illegal activities. Although recent approaches based on Graph Neural Networks (GNNs) have shown remarkable achievements in fraud detection, investigating cryptocurrency transaction networks is subject to the following challenges: 1) There is a lack of useful node features as cryptocur

Cardiac

H. J. Haepp,W. Pollmanng imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining multi-media/multi-dimensional data; application; mining grap978-3-030-47425-6978-3-030-47426-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

OMIT

nique to capture the general pattern underlying the data, thus guaranteeing the model robustness under some data missing circumstances. Extensive experiments on three widely used citation network datasets show that our proposed method has achieved or matched state-of-the-art results on link predicti

Glycogen

E. Gock,J. Kähler,V. Vogtormal data. Second, we adopt Spatial-Temporal Transformer with distinct attention modules to detect diverse anomalies. Extensive experiments on five real-world datasets are conducted, the results show that our method is superior to existing state-of-the-art approaches.

Derogate