We present NodeXL, an extendible toolkit for network overview, discovery and exploration implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL data analysis and visualization features with a social media data sample drawn from an enterprise intranet social network. A sequence of NodeXL operations from data import to computation of network statistics and refinement of network visualization through sorting, filtering, and clustering functions is described. These operations reveal sociologically relevant differences in the patterns of interconnection among employee participants in the social media space. The tool and method can be broadly applied.

As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records.

A common goal in graph visualization research is the design of novel techniques for displaying an overview of an entire graph. However, there are many situations where such an overview is not relevant or practical for users, as analyzing the global structure may not be related to the main task of the users that have semi-specific information needs. Furthermore, users accessing large graph databases through an online connection or users running on less powerful (mobile) hardware simply do not have the resources needed to compute these overviews. In this paper, we advocate an interaction model that allows users to remotely browse the immediate context graph around a specific node of interest. We show how Furnas' original degree of interest function can be adapted from trees to graphs and how we can use this metric to extract useful contextual subgraphs, control the complexity of the generated visualization and direct users to interesting datapoints in the context. We demonstrate the effectiveness of our approach with an exploration of a dense online database containing over 3 million legal citations.

Start entering some encoder sentence (enter triggers request)... our tool helps to find errors in seq2seq models using visual analysis methods .Enc words: Attention: topK:our tool helps to find errors in seq2seq models using visual analysis methods .unser werkzeug hil , fehler in modellen zu finden , die mit visuellen analysen .unser werkzeug hil , fehler in modellen zu finden , die mit visuellen analysen der .unsere instrument dabei dabei fehlern zu der modelle anzuwenden entdecken mit mit mittels visueller von das tool hilfreich dazu abwei chungen bei den einzusetzen suchen die indem visuell der belegen werden unserem hilfsmittel ist zu für form , mit verschaffen mittels mittels visuellen vi suali si erung auswertung geprägt zu wir werkzeuge helfen es etwas auf die anhand für geben und um von analyse zu lernen pivot change: word attn compare: sentence swap: unser unsere werkzeug instrument tool hil , dabei es dazu fehler , fehler in zu fehler zu in finden in finden modellen modelle in in zu zu modellen finden finden modellen zu modellen , , , finden die die , mit mittels mit visuellen visueller visueller visuellen analysen analysen der von der von visuellen . visuellen analyse analysen analyse . zu . . show: edges nodes our tool helps to find errors in seq2seq models using visual analysis methods . show: src tgt highlight: -1 0 +1 and around the world , satellites and warning systems are saving lives in areas such as bangladesh . these are the two pictures taken of garment factories in province and garment factories in india . i would love to talk about my astronomy , but i suspect that the number of people who are interested in transfer in atmospheres and polarization of light in jupiter 's upper atmosphere are the number of people who 'd fit in a bus shelter . if a neutrino had a brain , which it evolved in ancestors , it would say that rocks really do consist of empty space . i would love to talk about my astronomy , but i suspect that the number of people who are interested in transfer in atmospheres and polarization of light in jupiter 's upper atmosphere are the number of people who 'd fit in a bus shelter . most of those individuals had spent most of their lives in hospitals . this is a long time ago . that 's year by year . this comes from our friends at i 'll get an esl class in learning " it 's raining , it 's pouring . " you send one blessed email to whomever you 're thinking of at she for jobs down in province in the south . science columnist lee describes a remarkable project at divide , antarctica , where a hardy team are drilling into ice to extract vital data on our changing climate . that technology will be used on animals . i work in homes , largely . so for example , there was one study that was done in a population of jews in new yor...

Predictive modeling techniques are increasingly being used by data scientists to understand the probability of predicted outcomes. However, for data that is high-dimensional, a critical step in predictive modeling is determining which features should be included in the models. Feature selection algorithms are often used to remove non-informative features from models. However, there are many different classes of feature selection algorithms. Deciding which one to use is problematic as the algorithmic output is often not amenable to user interpretation. This limits the ability for users to utilize their domain expertise during the modeling process. To improve on this limitation, we developed INFUSE, a novel visual analytics system designed to help analysts understand how predictive features are being ranked across feature selection algorithms, cross-validation folds, and classifiers. We demonstrate how our system can lead to important insights in a case study involving clinical researchers predicting patient outcomes from electronic medical records.