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.
Authors introduce a novel algorithm based on multi-objective optimization to jointly position embedded documents and graph nodes in a 2-d landscape. 
First, the multi-objective optimization used in this paper is quite confusing. Since the overall error defined in (5) is a weighted summation of three objectives, it is not a real multi-objective optimization where multiple objectives are optimized simultaneously. 
Authors define a hypergraph to jointly model documents and entities. However, there lacks the novelty on the objectives defined in this paper. The optimization using gradient descent is also trivial. 
It is hard to understand why equations (2) and (3) share the same objectives but the two sets of samples have totally different meanings. Moreover, both objectives are summed uniformly as shown in the experimental setting. Please explain it in details.
For the quantitative evaluation, Table 1 shows the percentage of papers for each community per cluster. There are many numbers for a single clustering problem. It might be preferred to use a single measurement such as normalized mutual information or accuracy for clustering evaluation. In this way, it is easy to judge the compared methods.