Current and Future Challenges in Mining Large Networks

Holder, Lawrence B.; Caceres, Rajmonda S.; Gleich, David F.; Riedy, Jason; Khan, Maleq; Chawla, Nitesh V.; Kumar, Ravi; Wu, Yinghui; Klymko, Christine; Eliassi-Rad, Tina; Prakash, Amit

doi:10.1145/2980765.2980770

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…Large data refers to a collection of data that can not be managed, acquired, processed, and serviced in a short time by traditional hardware and software tools and computer technology [1][2][3]. Large data types included unstructured data, structured data and semi-structured data.…”

Section: Introductionmentioning

confidence: 99%

Parallel algorithm research of graph search and depth learning based on data mining

Wang¹

2017

Proceedings of the 2016 2nd International Conference on Materials Engineering and Information Technology Applications (MEITA 20

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Abstract. At present, information technology presents exponential growth characteristics, it have entered the era of large data. Data is a strategic resource as important as self-heating resources and human resources, which implied huge economic value. How to effectively organize and deal with large data will play a huge role in the socio-economic development. The graph search and depth learning algorithms play a more and more important role in the processing of large data because of their strong ability of network analysis and feature recognition and classification. In this paper, we propose a parallel optimization method based on locality principle, synchronization cost reduction and load balancing to solve the problem of width-first search. Finally, this paper combines all the methods together, and proposes a width-first search method using heuristic search. The experimental results showed that the width-first search algorithm with parallel optimization has good acceleration effect.

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Section: Introductionmentioning

confidence: 99%

Parallel algorithm research of graph search and depth learning based on data mining

Wang¹

2017

Proceedings of the 2016 2nd International Conference on Materials Engineering and Information Technology Applications (MEITA 20

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A Note on Artificial Intelligence and Statistics

Morik

2019

Studies in Classification, Data Analysis, and Knowledge Organization

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A Statistical Performance Analysis of Graph Clustering Algorithms

Miasnikof

Shestopaloff

Bonner

et al. 2018

Lecture Notes in Computer Science

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Measuring graph clustering quality remains an open problem. To address it, we introduce quality measures based on comparisons of intra-and inter-cluster densities, an accompanying statistical test of the significance of their differences and a step-by-step routine for clustering quality assessment. Our null hypothesis does not rely on any generative model for the graph, unlike modularity which uses the configuration model as a null model. Our measures are shown to meet the axioms of a good clustering quality function, unlike the very commonly used modularity measure. They also have an intuitive graph-theoretic interpretation, a formal statistical interpretation and can be easily tested for significance. Our work is centered on the idea that well clustered graphs will display a significantly larger intra-cluster density than inter-cluster density. We develop tests to validate the existence of such a cluster structure. We empirically explore the behavior of our measures under a number of stress test scenarios and compare their behavior to the commonly used modularity and conductance measures. Empirical stress test results confirm that our measures compare very favorably to the established ones. In particular, they are shown to be more responsive to graph structure and less sensitive to sample size and breakdowns during numerical implementation and less sensitive to uncertainty in connectivity. These features are especially important in the context of larger data sets or when the data may contain errors in the connectivity patterns.

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Current and Future Challenges in Mining Large Networks

Cited by 5 publications

References 30 publications

Parallel algorithm research of graph search and depth learning based on data mining

Parallel algorithm research of graph search and depth learning based on data mining

A Note on Artificial Intelligence and Statistics

A Statistical Performance Analysis of Graph Clustering Algorithms

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