Online Spammer Detection using User-Neighbor Relationship

2018 ◽  
Author(s):  
Sihyun Jeong ◽  
Chong-kwon Kim
Keyword(s):  
Neighbor Relationship

scholarly journals The geometric formulas of the Lewis’s law and Aboav-Weaire’s law in two dimensions based on ellipse packing

2019 ◽  
Author(s):  
Kai Xu
Keyword(s):  
Two Dimensions ◽  
Empirical Formulas ◽  
Von Neumann ◽  
Upper Limit ◽  
Edge Distribution ◽  
Cell Topology ◽  
Neighbor Relationship ◽  
New Formula ◽  
Geometric Formula ◽  
Ellipse Packing

The two-dimensional (2D) Lewis’s law and Aboav-Weaire’s law are two simple formulas derived from empirical observations. Numerous attempts have been made to improve the empirical formulas. In this study, we simulated a series of Voronoi diagrams by randomly disordered the seed locations of a regular hexagonal 2D Voronoi diagram, and analyzed the cell topology based on ellipse packing. Then, we derived and verified the improved formulas for Lewis’s law and Aboav-Weaire’s law. Specifically, we found that the upper limit of the second moment of edge number is 3. In addition, we derived the geometric formula of the von Neumann-Mullins’s law based on the new formula of the Aboav-Weaire’s law. Our results suggested that the cell area, local neighbor relationship, and cell growth rate are closely linked to each other, and mainly shaped by the effect of deformation from circle to ellipse and less influenced by the global edge distribution.

scholarly journals Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine Learning

Biomolecules ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1605
Author(s):  
Christian Feldmann ◽  
Dimitar Yonchev ◽  
Jürgen Bajorath
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Structural Features ◽  
Biological Screening ◽  
Large Scale Analysis ◽  
Structural Relationships ◽  
Single Target ◽  
Neighbor Relationship ◽  
Target Activity

Predicting compounds with single- and multi-target activity and exploring origins of compound specificity and promiscuity is of high interest for chemical biology and drug discovery. We present a large-scale analysis of compound promiscuity including two major components. First, high-confidence datasets of compounds with multi- and corresponding single-target activity were extracted from biological screening data. Positive and negative assay results were taken into account and data completeness was ensured. Second, these datasets were investigated using diagnostic machine learning to systematically distinguish between compounds with multi- and single-target activity. Models built on the basis of chemical structure consistently produced meaningful predictions. These findings provided evidence for the presence of structural features differentiating promiscuous and non-promiscuous compounds. Machine learning under varying conditions using modified datasets revealed a strong influence of nearest neighbor relationship on the predictions. Many multi-target compounds were found to be more similar to other multi-target compounds than single-target compounds and vice versa, which resulted in consistently accurate predictions. The results of our study confirm the presence of structural relationships that differentiate promiscuous and non-promiscuous compounds.

scholarly journals The geometric formulas of the Lewis’s law and Aboav-Weaire’s law in two dimensions based on ellipse packing

2019 ◽  
Author(s):  
Kai Xu
Keyword(s):  
Two Dimensions ◽  
Empirical Formulas ◽  
Von Neumann ◽  
Upper Limit ◽  
Edge Distribution ◽  
Cell Topology ◽  
Neighbor Relationship ◽  
New Formula ◽  
Geometric Formula ◽  
Ellipse Packing

The two-dimensional (2D) Lewis’s law and Aboav-Weaire’s law are two simple formulas derived from empirical observations. Numerous attempts have been made to improve the empirical formulas. In this study, we simulated a series of Voronoi diagrams by randomly disordered the seed locations of a regular hexagonal 2D Voronoi diagram, and analyzed the cell topology based on ellipse packing. Then, we derived and verified the improved formulas for Lewis’s law and Aboav-Weaire’s law. Specifically, we found that the upper limit of the second moment of edge number is 3. In addition, we derived the geometric formula of the von Neumann-Mullins’s law based on the new formula of the Aboav-Weaire’s law. Our results suggested that the cell area, local neighbor relationship, and cell growth rate are closely linked to each other, and mainly shaped by the effect of deformation from circle to ellipse and less influenced by the global edge distribution.

A GRAPH-BASED APPROACH TO DETECT ABNORMAL SPATIAL POINTS AND REGIONS

2011 ◽  
Vol 20 (04) ◽  
pp. 721-751 ◽  
Author(s):  
CHANG-TIEN LU ◽  
RAIMUNDO F. DOS SANTOS ◽  
XUTONG LIU ◽  
YUFENG KOU
Keyword(s):  
Time Complexity ◽  
Nearest Neighbor ◽  
Census Data ◽  
High Weight ◽  
Transportation Systems ◽  
Detection Methods ◽  
K Nearest Neighbor ◽  
Isolated Points ◽  
Neighbor Relationship ◽  
Spatial Outliers

Spatial outliers are the spatial objects whose nonspatial attribute values are quite different from those of their spatial neighbors. Identification of spatial outliers is an important task for data mining researchers and geographers. A number of algorithms have been developed to detect spatial anomalies in meteorological images, transportation systems, and contagious disease data. In this paper, we propose a set of graph-based algorithms to identify spatial outliers. Our method first constructs a graph based on k-nearest neighbor relationship in spatial domain, assigns the differences of nonspatial attribute as edge weights, and continuously cuts high-weight edges to identify isolated points or regions that are much dissimilar to their neighboring objects. The proposed algorithms have three major advantages compared with other existing spatial outlier detection methods: accurate in detecting both point and region outliers, capable of avoiding false outliers, and capable of computing the local outlierness of an object within subgraphs. We present time complexity of the algorithms, and show experiments conducted on US housing and Census data to demonstrate the effectiveness of the proposed approaches.

scholarly journals Locality Adaptive Discriminant Analysis

2017 ◽  
Author(s):  
Xuelong Li ◽  
Mulin Chen ◽  
Feiping Nie ◽  
Qi Wang
Keyword(s):  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Real World ◽  
Original Data ◽  
Additional Parameter ◽  
Distributed Data ◽  
Local Data ◽  
Linear Discriminant ◽  
Benchmark Datasets ◽  
Neighbor Relationship

Linear Discriminant Analysis (LDA) is a popular technique for supervised dimensionality reduction, and its performance is satisfying when dealing with Gaussian distributed data. However, the neglect of local data structure makes LDA inapplicable to many real-world situations. So some works focus on the discriminant analysis between neighbor points, which can be easily affected by the noise in the original data space. In this paper, we propose a new supervised dimensionality reduction method, Locality Adaptive Discriminant Analysis (LADA), to lean a representative subspace of the data. Compared to LDA and its variants, the proposed method has three salient advantages: (1) it finds the principle projection directions without imposing any assumption on the data distribution; (2) it’s able to exploit the local manifold structure of data in the desired subspace; (3) it exploits the points’ neighbor relationship automatically without introducing any additional parameter to be tuned. Performance on synthetic datasets and real-world benchmark datasets demonstrate the superiority of the proposed method.

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