Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents. Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks. Furthermore, social networks can be extracted from email corpora, tweets, or social media. When it comes to visualising these large corpora, either the textual content or the network graph are used. In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure. To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape. We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.

Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents. Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-co-occurrence networks. Furthermore, social networks can be extracted from email corpora, tweets, or social media. When it comes to visualising these large corpora, traditionally either the textual content or the network graph are used. We propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents’ content but also the relationships expressed by the inherent network structure in a two-dimensional landscape. We illustrate the effectiveness of our approach with an exploration interface for different real world datasets.

Nowadays, more and more large datasets exhibit an intrinsic graph structure. While there exist special graph databases to handle ever increasing amounts of nodes and edges, visualising this data becomes infeasible quickly with growing data. In addition, looking at its structure is not sufficient to get an overview of a graph dataset. Indeed, visualising additional information about nodes or edges without cluttering the screen is essential. In this paper, we propose an interactive visualisation for social networks that positions individuals (nodes) on a two-dimensional canvas such that communities defined by social links (edges) are easily recognisable. Furthermore, we visualise topical relatedness between individuals by analysing information about social links, in our case email communication. To this end, we utilise document embeddings, which project the content of an email message into a high dimensional semantic space and graph embeddings, which project nodes in a network graph into a latent space reflecting their relatedness.