Motivation Scientists increasingly rely on intelligent information systems to help them in their
daily tasks, in particular for managing research objects, like publications or datasets. The …

Network embedding aims to map a complex network into a low-dimensional vector space
while maximally preserving the properties of the original network. An attributed network is a …

Recently, graph embedding models significantly improved the quality of graph machine
learning tasks, such as node classification and link prediction. In this work, we propose a …

Current cryptocurrencies, such as Bitcoin and Ethereum, enable anonymity by using public
keys to represent user accounts. On the other hand, inferring blockchain account types (ie …

Social networks have a plethora of applications, and analysis of these applications has been
gaining much interest from the research community. The high dimensionality of social …

Many real-world tasks solved by heterogeneous network embedding methods can be cast
as modeling the likelihood of a pairwise relationship between two nodes. For example, the …

We present a study on co-authorship network representation based on network embedding
together with additional information on topic modeling of research papers and new edge …

Background: An adverse drug event (ADE) is commonly defined as “an injury resulting from
medical intervention related to a drug.” Providing information related to ADEs and alerting …

Learning network representations is a fundamental task for many graph applications such as
link prediction, node classification, graph clustering, and graph visualization. Many real …

Node embeddings have become an ubiquitous technique for representing graph data in a
low dimensional space. Graph autoencoders, as one of the widely adapted deep models …