English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Graph convolutional networks (GCNs) is a class of artificial neural networksfor processing data that can be represented as graphs. Since financialtransactions can naturally be constructed as graphs, GCNs are widely applied inthe financial industry, especially for financial fraud detection. In thispaper, we focus on fraud detection on cryptocurrency truct networks. In theliterature, most works focus on static networks. Whereas in this study, weconsider the evolving nature of cryptocurrency networks, and use localstructural as well as the balance theory to guide the training process. Morespecifically, we compute motif matrices to capture the local topologicalinformation, then use them in the GCN aggregation process. The generatedembedding at each snapshot is a weighted average of embeddings within a timewindow, where the weights are learnable parameters. Since the trust networks issigned on each edge, balance theory is used to guide the training process.Experimental results on bitcoin-alpha and bitcoin-otc datasets show that theproposed model outperforms those in the literature.

Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks