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Title: MODiR: Multi-Objective Dimensionality Reduction for Joint Data Visualisation

Paper content:
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.
Substantial amounts of data is produced in our modern information society each day.
A large portion of it comes from the communication on social media platforms, within chat applications, or via emails.
This data exhibits dualtiy in the sense that they can be represented as text and graph.
The metadata provides an inherent graph structure given by the social network between correspondents and the exchanged messages constitute the textual content.
In addition, there are many other datasets that exhibit these two facets.
Some of them are found in bibliometrics, for example in collections of research publications as co-author and citation networks.
When it comes to analyse these types of datasets, usually either the content or the graph structure is neglected.
In data exploration scenarios the goal of getting an overview of the datasets at hand is insurmountable with current tools.
The sheer amount of data prohibits simple visualisations of networks or meaningful keyword-driven summaries of the textual content.
Data-driven journalism (Coddington, 2015) often has to deal with leaked, unstructured, very heterogeneous data, e.g. in the context of the Panama Papers, where journalists needed to untangle and order huge amounts of information, search entities, and visualise found patterns (Chabin, 2017) .
Similar datasets are of interest in the context of computational forensics (Franke & Srihari, 2007) .
Auditing firms and law enforcement need to sift through huge amounts of data to gather evidence of criminal activity, often involving communication networks and documents (Karthik et al., 2008) .
Users investigating such data want to be able to quickly gain an overview of its entirety, since the large amount of heterogeneous data renders experts' investigations by hand infeasible.
Computer-aided exploration tools can support their work to identify irregularities, inappropriate content, or suspicious patterns.
Current tools 1 lack sufficient semantic support, for example by incorporating document embeddings (Mikolov et al., 2013) and the ability to combine text and network information intuitively.
We propose MODiR, a scalable multi-objective dimensionality reduction algorithm, and show how it can be used to generate an overview of entire text datasets with inherent network information in a single interactive visualisation.
Special graph databases enable the efficient storage of large relationship networks and provide interfaces to query or analyse the data.
However, without prior knowledge, it is practically impossible to gain an overview or quick insights into global network structures.
Although traditional node-link visualisations of a graph can provide this overview, all semantic information from associated textual content is lost completely.
Technically, our goal is to combine network layouts with dimensionality reduction of highdimensional semantic embedding spaces.
Giving an overview over latent structures and topics in one visualisation may significantly improve the exploration of a corpus by users unfamiliar with the domain and terminology.
This means, we have to integrate multiple aspects of the data, especially graph and text, into a single visualisation.
The challenge is to provide an intuitive, two-dimensional representation of both the graph and the text, while balancing potentially contradicting objectives of these representations.
In contrast to existing dimensionality reduction methods, such as tSNE (Maaten & Hinton, 2008) , MODiR uses a novel approach to transform high-dimensional data into two dimensions while optimising multiple constraints simultaneously to ensure an optimal layout of semantic information extracted from text and the associated network.
To minimise the computational complexity that would come from a naive combination of network drawing and dimensionality reduction algorithms, we formally use the notion of a hypergraph.
In this way, we are able to move repeated expensive computations from the iterative document-centred optimisation to a preprocessing step that constructs the hypergraph.
We use real-world datasets from different domains to demonstrate the effectiveness and flexibility of our approach.
MODiR-generated representations are compared to a series of baselines and state-of-the-art dimensionality reduction methods.
We further show that our integrated view of these datasets exhibiting duality is superior to approaches focusing on text-only or network-only information when computing the visualisation.
In this paper we discussed how to jointly visualise text and network data with all its aspects on a single canvas.
Therefore we identified three principles that should be balanced by a visualisation algorithm.
From those we derived formal objectives that are used by a gradient descend algorithm.
We have shown how to use that to generate landscapes which consist of a base-layer, where the embedded unstructured texts are positioned such that their closeness in the document landscape reflects semantic similarity.
Secondly, the landscape consists of a graph layer onto which the inherent network is drawn such that well connected nodes are close to one another.
Lastly, both aspects can be balanced so that nodes are close to the documents they are associated with while preserving the graph-induced neighbourhood.
We proposed MODiR, a novel multi-objective dimensionality reduction algorithm which iteratively optimises the document and network layout to generate insightful visualisations using the objectives mentioned above.
In comparison with baseline approaches, this multi-objective approach provided best balanced overall results as measured by various metrics.
In particular, we have shown that MODiR outperforms state-of-the-art algorithms, such as tSNE.
We also implemented an initial prototype for an intuitive and interactive exploration of multiple datasets.
(Ammar et al., 2018) with over 45 million articles.
Both corpora cover a range of different scientific fields.
Semantic Scholar for example integrates multiple data sources like DBLP and PubMed and mostly covers computer science, neuroscience, and biomedical research.
Unlike DBLP however, S2 and AM not only contain bibliographic metadata, such as authors, date, venue, citations, but also abstracts to most articles, that we use to train document embeddings using the Doc2Vec model in Gensim 10 .
Similar to Carvallari et al. (Cavallari et al., 2017) remove articles with missing information and limit to six communities that are aggregated by venues as listed in Table 3 .
This way we reduce the size and also remove clearly unrelated computer science articles and biomedical studies.
For in depth comparisons we reduce the S2 dataset to 24 hand-picked authors, their co-authors, and their papers (S2b).
Note, that the characteristics of the networks differ greatly as the ratio between documents, nodes, and edges in Table 2 shows.
In an email corpus, a larger number of documents is attributed to fewer nodes and the distribution has a high variance (some people write few emails, some a lot).
In the academic corpora on the other hand, the number of documents per author is relatively low and similar throughout.
Especially different is the news corpus, that contains one entity that is linked to all other entities and to all documents.

assistant: text: Dimensionality reduction algorithm to visualise text with network information, for example an email corpus or co-authorships.