Network analysis or social network analysis (SNA) is a data science methodology that captures the social structure of a system through the use of network and graph theory. It characterizes networked structures in terms of nodes and the ties or edges (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network analysis include social media networks, friendship and acquaintance networks, kinship, disease transmission, organizational structure, international trade and sexual relationships. In principle, networks are either observable as a natural phenomenon or created by identification and operationalization of implicit relationships. These networks normally have the same type of nodes with similar attribute profile and data characteristics.
However, in many real-world settings, the networks consist of not one but several different types, or modes, of nodes. Multi-modal (social) networks are those nodes belong to different types or entity classes. They are heterogeneous networks that are not constrained to homogeneous network formations. Examples include co-authorship networks that contain not just authors, but also
the venues they attend and the journals they publish in; organizational charts that contain employees as well as the departments they belong to; and information retrieval processes that involve both databases and the people who access them; medical databases where patients may interact with physicians and may be associated with standard set of disease codes and treatment procedure codes. Bi-partite networks are a special case of multi-modal networks with 2 types of nodes, which are the most popular after the unimodal networks that are the de facto standard in (social) network analysis.
Uni-modal or homogeneous network realizations have been done in case of online social networks (interpersonal), biological networks (gene-gene interactions) or economic networks (international strategic relationships). Community detection, network characterization using metrics and integration with other data science methodologies are approaches commonly adopted for simple unimodal networks, but formalization in multi-modal networks is still in its early development stages.
A standard approach to analyzing multimodal networks has been to transform them into unimodal social networks either through projection or through separation. For example, in a study analyzing user subscription in online brand pages in facebook; the ties between brand pages and its subscribers are transformed (or projected) to ties among brands with edge weights given by the number of common users active in a pair of brand pages.
I) Multimodal network visualization:
Ghani et al. (2013) have summarized the various visualization strategies for multi-modal network visualization as follows:
1) Compound Network Visualization: Treat the multimodal graph as a unimodal graph and color the node types differentially.
2) Eliminating modes through projection: Several graph visualization packages offer features to collapse nodes of one node type to edges between nodes of another node type; particularly in case of bipartite networks.
3) Linked network visualization: A third approach is to use multiple views, each of which renders a
different mode of the graph separately (linked network visualization). Between-mode ties are visualized using visual links or brushing (when nodes are selected in one view, corresponding nodes in another view are highlighted).
One of the approaches in the review is an extension of parallel node-link bands (PNLB) for bipartite networks that was customized and implemened a tool named MMGraph.
Figure 1: Parallel node-link bands visualization of a researchers-projects dataset done by Ghani et al. (2013)
The authors also defined some metrics such as multimodal degree centrality, betweenness and closeness centrality for nodes in one mode with reference to its interactions with nodes of other modes.
II) Multi-modal network analysis:
Bauer et al. (2013) analyze diseases, drugs, medical devices and procedures and demonstrate advantages of network-based approaches as compared to traditional approaches such as expert inputs based propensity score matching.
They constructed a homogeneous network for group foramtion and then represented it in a multimodal network as shown in figure 2.
Figure 2: Drugs, disease, device, procedure are different node types in the multimodal network developed by Bauer et al. (2013)
By effective use of the width, length of node dimensions, edge thickness, node type coloring, the researchers were effectively able to identify the node entities clustering in the cilostazol treatment concept quite distinctly from the control group of patients.
Multimodal networks have tremendous potential for theoretical developments as well as practical applications. The world of networks is not always a simplistic representation of similar entities. Theoretical development in uni-modal, undirected, unweighted and attribute-less nodes in a network will have to transform into a complete comprehensive representation of the multi-faceted eco-system that we are currently experience. Visualization alone can aid decision-making as indicated by the two use-cases above, but problem-solvers should not restrain themselves.
Instead, we should look at it like a pandora's box which is just being opened in the last few years and with tremendous potential of changing the ways that we perceive social networks.
References:
1) Ghani, S., Kwon, B. C., Lee, S., Yi, J. S., & Elmqvist, N. (2013). Visual analytics for multimodal social network analysis: A design study with social scientists. Visualization and Computer Graphics, IEEE Transactions on, 19(12), 2032-2041.
2) Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social networks, 19(3), 243-269.
3) Bauer-Mehren, A., LePendu, P., Iyer, S. V., Harpaz, R., Leeper, N. J., & Shah, N. H. (2013). Network analysis of unstructured EHR data for clinical research. AMIA Summits on Translational Science Proceedings, 2013, 14.
