scholarly journals Identifying Knowledge Anchors in a Data Graph

2016 ◽  
Author(s):  
Marwan Al-Tawil ◽  
Vania Dimitrova ◽  
Dhavalkumar Thakker ◽  
Brandon Bennett
Keyword(s):  
Data Graph

Research of Data Graph Mining Based on Telecommunication Customers

2013 ◽  
Vol 443 ◽  
pp. 402-406 ◽  
Author(s):  
Shang Gao ◽  
Mei Mei Li
Keyword(s):  
Data Mining ◽  
Graph Mining ◽  
Pattern Mining ◽  
Rapid Development ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Practical Significance ◽  
Research Progress ◽  
Graph Data ◽  
Data Graph

With the rapid development of the number of mobile phone users has accumulated a large number of graph data, graph data mining has gradually become a hot area of research. Traditional data such as clustering, classification, frequent pattern mining gradually extended to the field of graph data mining research. Introduced at this stage graph data mining technology research progress, summarizes the characteristics of the graphical data mining, practical significance, the main problem, and scenarios to discuss and forecast chart data, especially research on uncertain graph data become trends and hot spots.

scholarly journals A linked data graph approach to integration of immunological data

2019 ◽  
Author(s):  
Syed Ahmad Chan Bukhari ◽  
Jeff Mandell ◽  
Steven H. Kleinstein ◽  
Kei-Hoi Cheung
Keyword(s):  
Linked Data ◽  
Data Graph

scholarly journals A note on label propagation for semi-supervised learning

2015 ◽  
Vol 7 (1) ◽  
pp. 18-30
Author(s):  
Zalán Bodó ◽  
Lehel Csató
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Unlabeled Data ◽  
Label Propagation ◽  
Learning Method ◽  
The Past ◽  
Data Graph

Abstract Semi-supervised learning has become an important and thoroughly studied subdomain of machine learning in the past few years, because gathering large unlabeled data is almost costless, and the costly human labeling process can be minimized by semi-supervision. Label propagation is a transductive semi-supervised learning method that operates on the—most of the time undirected—data graph. It was introduced in [8] and since many variants were proposed. However, the base algorithm has two variants: the first variant presented in [8] and its slightly modified version used afterwards, e.g. in [7]. This paper presents and compares the two algorithms—both theoretically and experimentally—and also tries to make a recommendation which variant to use.

scholarly journals Executing Dynamic Data-Graph Computations Deterministically Using Chromatic Scheduling

2016 ◽  
Vol 3 (1) ◽  
pp. 1-31 ◽  
Author(s):  
Tim Kaler ◽  
William Hasenplaugh ◽  
Tao B. Schardl ◽  
Charles E. Leiserson
Keyword(s):  
Dynamic Data ◽  
Data Graph

The Effect of Configural Graphs on Concurrent and Retrospective Performance

1996 ◽  
Vol 40 (23) ◽  
pp. 1160-1164
Author(s):  
Karel Hurts
Keyword(s):  
Data Structure ◽  
Task Performance ◽  
Search Time ◽  
Graph Structure ◽  
Reversal Effect ◽  
High Data ◽  
Graph Type ◽  
Data Graph ◽  
Structure Complexity ◽  
Integrated Tasks

Following up on a previous study showing the performance on integrated tasks for non-configural graphs to be superior to that for configural graphs if the memory for the graph is tested (retrospective or memory-based conditions), this paper further contrasts retrospective and concurrent (display-based) task performance. This was done by experimentally investigating the effect of various configural and non-configural static graphs on integrated task performance (requiring the consideration of lower-level graph information as well as higher-level graph information), using both retrospective and concurrent conditions. Subjects were asked to answer a question about each graph, which was phrased in terms of the domain of the data and which could not be easily anticipated. Graphs also differed in the amount of fit between graph structure and data structure (data-graph compatibility). The results confirmed the expectation that the reversal effect (inferior performance for configural graphs) is only found under memory-based conditions. Both display-based and memory-based performance were better for the configural graphs with high data-graph compatibility, although only significantly so for display-based search time. The two separable types of graphs could only be compared with respect to the amount of time needed to memorize the graphs: longer times were found for the graph type with low data-graph compatibility. However, the latter effect may also be due to a difference in data structure complexity, as this factor was confounded with data-graph compatibility in the two separable graph types. Although more research is needed to disambiguate some of the present results and to make other and better comparisons, the results of this study still show the importance of structural and semantic factors in determining the effectiveness of configurality in statistical graphs.

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