Graph Neural Networks (GNNs) are an effective framework for representation learning of
graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector …

We study the problem of large-scale network embedding, which aims to learn latent
representations for network mining applications. Previous research shows that 1) popular …

Entity Resolution and Information Quality presents topics and definitions, and clarifies
confusing terminologies regarding entity resolution and information quality. It takes a very …

Label propagation is a powerful and flexible semi-supervised learning technique on graphs.
Neural networks, on the other hand, have proven track records in many supervised learning …

Purpose This study aims to predict hotel demand through text analysis by investigating
keyword series to increase demand predictions' precision. To do so, this paper presents a …

A graph embedding is a representation of graph vertices in a low-dimensional space, which
approximately preserves properties such as distances between nodes. Vertex sequence …

Research in computer-supported cooperative work has historically focused on behaviors of
individuals at scale, using frames of interpersonal interaction such as Goffman's theories of …

This paper presents the results of a study using Twitter lists to infer the characteristics of
users, especially about their interests. Gathering and structuring users' interest has been …

Low-dimensional embeddings, from classical spectral embeddings to modern neural-net-
inspired methods, are a cornerstone in the modeling and analysis of complex networks …

The skip-gram model for learning word embeddings (Mikolov et al. 2013) has been widely
popular, and DeepWalk (Perozzi et al. 2014), among other methods, has extended the …