Dataset Information

An efficient semi-supervised community detection framework in social networks.

ABSTRACT: Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtained from domain knowledge in many applications. Thus, combining network topology with prior information to improve the community detection accuracy is promising. Previous methods mainly utilize the must-link constraints while cannot make full use of cannot-link constraints. In this paper, we propose a semi-supervised community detection framework which can effectively incorporate two types of pairwise constraints into the detection process. Particularly, must-link and cannot-link constraints are represented as positive and negative links, and we encode them by adding different graph regularization terms to penalize closeness of the nodes. Experiments on multiple real-world datasets show that the proposed framework significantly improves the accuracy of community detection.

SUBMITTER: Li Z

PROVIDER: S-EPMC5441628 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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An efficient semi-supervised community detection framework in social networks.

Li Zhen Z Gong Yong Y Pan Zhisong Z Hu Guyu G

PloS one 20170523 5

Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtained from domain knowledge in many applications. Thus, combining network topology with prior information ...[more]

PMID: 28542520

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Project description:Background and objectiveElectronic Health Record (EHR) data often include observation records that are unlikely to represent the "truth" about a patient at a given clinical encounter. Due to their high throughput, examples of such implausible observations are frequent in records of laboratory test results and vital signs. Outlier detection methods can offer low-cost solutions to flagging implausible EHR observations. This article evaluates the utility of a semi-supervised encoding approach (super-encoding) for constructing non-linear exemplar data distributions from EHR observation data and detecting non-conforming observations as outliers.MethodsTwo hypotheses are tested using experimental design and non-parametric hypothesis testing procedures: (1) adding demographic features (e.g., age, gender, race/ethnicity) can increase precision in outlier detection, (2) sampling small subsets of the large EHR data can increase outlier detection by reducing noise-to-signal ratio. The experiments involved applying 492 encoder configurations (involving different input features, architectures, sampling ratios, and error margins) to a set of 30 datasets EHR observations including laboratory tests and vital sign records extracted from the Research Patient Data Registry (RPDR) from Partners HealthCare.ResultsResults are obtained from (30 × 492) 14,760 encoders. The semi-supervised encoding approach (super-encoding) outperformed conventional autoencoders in outlier detection. Adding age of the patient at the observation (encounter) to the baseline encoder that only included observation value as the input feature slightly improved outlier detection. Top-nine performing encoders are introduced. The best outlier detection performance was from a semi-supervised encoder, with observation value as the single feature and a single hidden layer, built on one percent of the data and one percent reconstruction error. At least one encoder configurations had a Youden's J index higher than 0.9999 for all 30 observation types.ConclusionGiven the multiplicity of distributions for a single observation in EHR data (i.e., same observation represented with different names or units), as well as non-linearity of human observations, encoding offers huge promises for outlier detection in large-scale data repositories. https://github.com/hestiri/superencoder.

Dataset Information

An efficient semi-supervised community detection framework in social networks.

Publications

An efficient semi-supervised community detection framework in social networks.

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