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Sensemaking is the cognitive process of extracting information, creating schemata from knowledge, making decisions from those schemata, and inferring conclusions. Human analysts are essential to exploring and quantifying this process, but they are limited by their inability to process the volume, variety, velocity, and veracity of data. Visualization tools are essential for helping this human-computer interaction. For example, analytical tools that use graphical linknode visualization can help sift through vast amounts of information. However, assisting the analyst in making connections with visual tools can be challenging if the information is not presented in an intuitive manner. Experimentally, it has been shown that analysts increase the number of hypotheses formed if they use visual analytic capabilities. Exploring multiple perspectives could increase the diversity of those hypotheses, potentially minimizing cognitive biases. In this paper, we discuss preliminary research results that indicate an improvement in sensemaking over the traditional link-node visualization tools by incorporating an annotation enhancement that differentiates links connecting nodes. This enhancement assists by providing a visual cue, which represents the perceived value of reported information. We conclude that this improved sensemaking occurs because of the removal of the limitations of mentally consolidating, weighing, and highlighting data. This study aims to investigate whether line thickness can be used as a valid representation of VoI., Comment: International Command and Control Research and Technology Symposium (ICCRTS - 2017), 9 pages, 3 figures, 3 tables, 1 equation

Graph pattern mining methods can extract informative and useful patterns from large-scale graphs and capture underlying principles through the overwhelmed information. Contrast analysis serves as a keystone in various fields and has demonstrated its effectiveness in mining valuable information. However, it has been long overlooked in graph pattern mining. Therefore, in this paper, we introduce the concept of contrast subgraph, that is, a subset of nodes that have significantly different edges or edge weights in two given graphs of the same node set. The major challenge comes from the gap between the contrast and the informativeness. Because of the widely existing noise edges in real-world graphs, the contrast may lead to subgraphs of pure noise. To avoid such meaningless subgraphs, we leverage the similarity as the cornerstone of the contrast. Specifically, we first identify a coherent core, which is a small subset of nodes with similar edge structures in the two graphs, and then induce contrast subgraphs from the coherent cores. Moreover, we design a general family of coherence and contrast metrics and derive a polynomial-time algorithm to efficiently extract contrast subgraphs. Extensive experiments verify the necessity of introducing coherent cores as well as the effectiveness and efficiency of our algorithm. Real-world applications demonstrate the tremendous potentials of contrast subgraph mining.

Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency parsing-based pattern discovery approach. However, the parsing results lose rich context around entities in the patterns, and the process is costly for a corpus of large scale. In this study, we propose a novel typed textual pattern structure, called meta pattern, which is extended to a frequent, informative, and precise subsequence pattern in certain context. We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise. Experiments demonstrate that our proposed framework discovers high-quality typed textual patterns efficiently from different genres of massive corpora and facilitates information extraction., Comment: 9 pages