Data Mining and Knowledge Discovery

What are the relations between the edge weights and the topology in real-world graphs? Given only the topology of a graph, how can we assign realistic weights to its edges based on the relations? Several trials have been done for edge-weight ...

Extreme multi-label classification aims to learn a classifier that annotates an instance with a relevant subset of labels from an extremely large label set. Many existing solutions embed the label matrix to a low-dimensional linear subspace, or ...

Relations among multiple entities are prevalent in many fields, and hypergraphs are widely used to represent such group relations. Hence, machine learning on hypergraphs has received considerable attention, and especially much effort has been made ...

Bayesian Confirmation Measures (BCMs) are used to assess the degree to which an evidence (or premise) E supports or contradicts an hypothesis (or conclusion) H, making use of prior probability $Pr(H)$, posterior probability $Pr(H|E)$ and of ...$$

Dimensionality reduction helps data analysts and machine learning designers to visualize in low dimension structures lying in high dimension. This is a basic but crucial operation, to discover relationship between variables, considering the ...${}^{}$

Query auto-completion (QAC) aims at suggesting plausible completions for a given query prefix. Traditionally, QAC systems have leveraged tries curated from historical query logs to suggest most popular completions. In this context, there are two ...$$$$

Group interactions are prevalent in a variety of areas. Many of them, including email exchanges, chemical reactions, and bitcoin transactions, are directional, and thus they are naturally modeled as directed hypergraphs, where each hyperarc ...

Hypergraphs are a powerful abstraction for modeling high-order relations, which are ubiquitous in many fields. A hypergraph consists of nodes and hyperedges (i.e., subsets of nodes); and there have been a number of attempts to extend the notion of ...$\mathit{}$$\mathbf{}\mathit{}\mathbf{}\mathit{}\mathbf{}$$\mathit{}$$\mathit{}$$\mathit{}$$\mathit{}$$\mathbf{}\mathit{}\mathbf{}\mathit{}\mathbf{}$$\mathit{}$$\mathit{}$$\mathbf{}\mathit{}\mathbf{}\mathit{}\mathbf{}$$\mathit{}$

Interactions that involve a group of people or objects are omnipresent in practice. Some examples include the list of recipients of an email, the group of co-authors of a publication, and the users participating in online discussion threads. These ...

Knowledge graphs (KGs) are becoming essential resources for many downstream applications. However, their incompleteness may limit their potential. Thus, continuous curation is needed to mitigate this problem. One of the strategies to address this ...

Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a ...

Matrix decomposition is a widely used tool in machine learning with many applications such as dimension reduction or visualization. In this paper we consider decomposing X, a matrix of size $n\times m$, to a product WS where we require that S, a matrix of ...$$$\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}$$\mathrm{}\mathrm{}$