A Method for Estimating Driving Factors of Illicit Trade Using Node Embeddings and Clustering
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DOI: 10.1007/978-3-030-49076-8_22
Abstract: The trade on illegal goods and services, also known as illicit trade, is expected to drain 4.2 trillion dollars from the world economy and put 5.4 million jobs at risk by 2022. These estimates reflect… read more here.
Keywords: illicit trade; node embeddings; embeddings clustering; method estimating ... See more keywords
Towards Understanding the Instability of Network Embedding
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DOI: 10.1109/tkde.2020.2989512
Abstract: Network embedding algorithms learn a mapping from the discrete representation of nodes to continuous vector spaces that preserve the proximities of nodes. The techniques have a wide range of applications in various downstream tasks such… read more here.
Keywords: understanding instability; network; node embeddings; network embedding ... See more keywords
Learning Adaptive Node Embeddings Across Graphs
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DOI: 10.1109/tkde.2022.3160211
Abstract: Recently, learning embeddings of nodes in graphs has attracted increasing research attention. There are two main kinds of graph embedding methods, i.e., transductive embedding methods and inductive embedding methods. The former focuses on directly optimizing… read more here.
Keywords: across graphs; node embeddings; adaptive node; graph ... See more keywords