egoSlider: Visual analysis of egocentric network evolution.

Note

This paper proposes a tool to visualize the dynamic and temporal information of ego-network. The primary goal of this tool is to support the study of the exploratory pattern for cross domains. For instance, how the ego-network change among time to the relationship with personal health? The contribution lay in three layers: 1) macroscopic: summarize the entire ego-network; 2) mesoscopic: overviewing particular individuals' ego-network evolution; 3) microscopic: displaying detailed temporal information of egos and their alters.

The visualization idea may come from different discipline, e.g. the sociology research may focus on more social interaction with developed social theory. It may be a great contribution to design such a tool to help them better facilitate, utilize and digest the generated data.

Reference

1. Wu, Yanhong, et al. "egoSlider: Visual analysis of egocentric network evolution." IEEE transactions on visualization and computer graphics 22.1 (2016): 260-269.

Reducing snapshots to points: A visual analytics approach to dynamic network exploration.

Note

This paper uses the dimensional reduction technique to reduce the complex, multi-dimensional graph into points as 2-dimension plot. It shows the pattern with a different cluster, the user can further explore the generated points to see the detail of the network.

This may help the user to understand the deep learning through neural network, the feature extraction process. But the challenge is still remaining how to explain/label the projection cluster. It is not guarantee to have a meaningful (or at least human understandable) pattern in each round of exploration.

Reference

1. van den Elzen, Stef, et al. "Reducing snapshots to points: A visual analytics approach to dynamic network exploration." IEEE transactions on visualization and computer graphics 22.1 (2016): 1-10.

CiteRivers: visual analytics of citation patterns

Note:

This visual tool is aim to help explore the citation network of the given publications (conference proceeding). It shows the citation word cloud, trend, diversity, author and publisher venue.

Points:

• Not clear of the scale of stream panel and the relation with spectral clustering. The benefit of using clustering techniques to show the publication in a river style is not clear. 
• An across stream citation analysis would be useful, i.e. to select more than one cell of the river. 
• The word meaning in the word cloud may be varied. E.g. the network is with multiple meaning across different research, even in the same domain. 
• The user case showed the citation pattern of given IEEE publications, but lack of the discussion of the found pattern. This may be the key value to the target users. 
• A user case that may be interesting: The given year publications are major based on which year's work? This could be a influence index for the past works (also the scholar). 

VIS15 preview: CiteRivers: Visual Analytics of Citation Patterns from VGTCommunity on Vimeo.


Reference:

1. Heimerl, Florian, et al. "CiteRivers: visual analytics of citation patterns." IEEE transactions on visualization and computer graphics 22.1 (2016): 190-199.

EgoNetCloud: Event-based egocentric dynamic network visualization

Note

A quality work on network visualization, this paper proposed a visual analytic tool to display the structure and temporal dynamics of an egocentric dynamic network [1,3].  It considered three important design factors in this work: 1) network simplification: to show all the links in the network graph is meaningless and over the information loading for users. A reasonable way to "prune" the node to highlight the important nodes is necessary. It firstly defined the weighting function by co-author number and ordering. Based on the weighting function, the authors tried four different approaches to pruning the node, to maximize the efficiency function, which maxes the weighting in the sub-graph.
2) temporal network: the temporal information present by horizon graph with an axis of time. It would be a simple task to identify the distribution over time; 3) graph layout: the layout designs with a 2D space. Due to the temporal relationship, the chart divides into several sub-graph that hard to fit by regular force-directed graph layout. They extend the stress model to calculate the ideal design [2].

Points: 1) the research methodology of visual analytic: from design, implantation, case study to user study. The user study design is a useful reference for my research; 2) considering the single publication as an event to form the egocentric network. It may supports to multiple use cases, e.g. urban computing, conference, news event, etc. This system is suitable to explore the relationship of a given dataset, for a temporal and egocentric related tasks; 3) the interaction of slider on time and weighting items is useful for a user to explore the content. It may potentially help a user to understand the deep relationship of the given person. This idea may also link to the explain function in the recommender system.

A worth to read citation [4].

Reference

1. Liu, Qingsong, et al. "EgoNetCloud: Event-based egocentric dynamic network visualization." Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on. IEEE, 2015.
2. Gansner, Emden R., Yehuda Koren, and Stephen North. "Graph drawing by stress majorization." International Symposium on Graph Drawing. Springer Berlin Heidelberg, 2004.
3. Shi, Lei, et al. "1.5 d egocentric dynamic network visualization." IEEE transactions on visualization and computer graphics 21.5 (2015): 624-637.
4. Zheng, Yixian, et al. "Visual Analytics in Urban Computing: An Overview." IEEE Transactions on Big Data 2.3 (2016): 276-296.