Being able to recommend links between users in online social networks is important for
users to connect with like-minded individuals as well as for the platforms themselves and …

Protein–protein interaction (PPI) networks consist of the physical and/or functional
interactions between the proteins of an organism, and they form the basis for the field of …

Manufacturing companies often lack visibility of the procurement interdependencies
between the suppliers within their supply network. However, knowledge of these …

Network embedding methods map a network's nodes to vectors in an embedding space, in
such a way that these representations are useful for estimating some notion of similarity or …

While graphs capture pairwise relations between entities, hypergraphs deal with higher-
order ones, thereby ensuring losslessness. However, in hyperlink (ie, higher-order link) …

In this paper we present EvalNE, a Python toolbox for evaluating network embedding
methods on link prediction tasks. Link prediction is one of the most popular choices for …

Recent years have seen rapid progress in network representation learning, which removes
the need for burdensome feature engineering and facilitates downstream network-based …

Usual networks lossily (if not incorrectly) represent higher-order relations, which calls for
complex structures such as hypergraphs to be used instead. Akin to the link prediction …

Link prediction is an area of social network research that tries to predict future links using a
social network structure. This paper proposes a novel link prediction method (FLP-DLA) that …

Link prediction involves assessing the likelihood of connections between node pairs based
on various structural properties. The effectiveness of link predictors can be influenced by …