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Design and Implementation of Small Multiples Matrix-based Visualisation to Monitor and Compare Email Socio-organisational Relationships
One of the fundamental organisational questions is how organisations identify anomalies, monitor and compare email communications between staff-staff or staff-clients or staff-customers relationships on a daily basis. The tenacious and substantial relationships are built by the combination of timely replies, frequent engagement and deep interaction between the individuals. To watchdog this periodically, we need an interactive visualisation tool that can help organisational analysts to reconnect some lost relationships and/or strengthen an existing relationship or in some cases identify inside persons (anomalies). From our point of view, Social Intelligence (SI) in an organisation is a combination of self-, social- and organisational-awareness that will help in managing complex socio-organisational changes and can be interpreted in terms of socio-organisational communication efficacy (that is, one's confidence in one's ability to deal with social and organisational information). We considered a case study, an Enron Organisation Email Scandal, to understand the relationships of staff during various parts of the years and we conducted a workshop study with legal experts to gain insights on how they carry out investigation/analysis with respect to email relationships. The outcomes of the workshop helped us develop a novel small multiples matrix-based visualisation in collaboration with our industrial partner, Red Sift UK, to find anomalies, monitor and compare how email relationships change over time and how it defines the meaning of socio-organisational communication efficacy
Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs
The overview-driven visual analysis of large-scale dynamic graphs poses a
major challenge. We propose Multiscale Snapshots, a visual analytics approach
to analyze temporal summaries of dynamic graphs at multiple temporal scales.
First, we recursively generate temporal summaries to abstract overlapping
sequences of graphs into compact snapshots. Second, we apply graph embeddings
to the snapshots to learn low-dimensional representations of each sequence of
graphs to speed up specific analytical tasks (e.g., similarity search). Third,
we visualize the evolving data from a coarse to fine-granular snapshots to
semi-automatically analyze temporal states, trends, and outliers. The approach
enables to discover similar temporal summaries (e.g., recurring states),
reduces the temporal data to speed up automatic analysis, and to explore both
structural and temporal properties of a dynamic graph. We demonstrate the
usefulness of our approach by a quantitative evaluation and the application to
a real-world dataset.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to
appea
Graphs, Matrices, and the GraphBLAS: Seven Good Reasons
The analysis of graphs has become increasingly important to a wide range of
applications. Graph analysis presents a number of unique challenges in the
areas of (1) software complexity, (2) data complexity, (3) security, (4)
mathematical complexity, (5) theoretical analysis, (6) serial performance, and
(7) parallel performance. Implementing graph algorithms using matrix-based
approaches provides a number of promising solutions to these challenges. The
GraphBLAS standard (istc- bigdata.org/GraphBlas) is being developed to bring
the potential of matrix based graph algorithms to the broadest possible
audience. The GraphBLAS mathematically defines a core set of matrix-based graph
operations that can be used to implement a wide class of graph algorithms in a
wide range of programming environments. This paper provides an introduction to
the GraphBLAS and describes how the GraphBLAS can be used to address many of
the challenges associated with analysis of graphs.Comment: 10 pages; International Conference on Computational Science workshop
on the Applications of Matrix Computational Methods in the Analysis of Modern
Dat
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