scholarly journals Leveraging Network Structure to Infer Missing Values in Relational Data

2007 ◽  
Author(s):  
B Gallagher ◽  
T Eliassi-Rad
Keyword(s):  
Network Structure ◽  
Missing Values ◽  
Relational Data

scholarly journals Discovery of genuine functional dependencies from relational data with missing values

2018 ◽  
Vol 11 (8) ◽  
pp. 880-892 ◽  
Author(s):  
Laure Berti-Équille ◽  
Hazar Harmouch ◽  
Felix Naumann ◽  
Noël Novelli ◽  
Saravanan Thirumuruganathan
Keyword(s):  
Missing Values ◽  
Relational Data ◽  
Functional Dependencies

scholarly journals Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data

2020 ◽  
Vol 110 (8) ◽  
pp. 2454-2484 ◽  
Author(s):  
Emily Breza ◽  
Arun G. Chandrasekhar ◽  
Tyler H. McCormick ◽  
Mengjie Pan
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Field Experiments ◽  
Network Formation ◽  
Relational Data ◽  
Network Data ◽  
Formation Model ◽  
Social Network Data ◽  
Level Statistics ◽  
Aggregated Relational Data

Social network data are often prohibitively expensive to collect, limiting empirical network research. We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD): responses to questions of the form “how many of your links have trait k ?” Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone. (JEL C81, C93, D85, Z13)

scholarly journals Learning Distributional Programs for Relational Autocompletion

2021 ◽  
pp. 1-34
Author(s):  
NITESH KUMAR ◽  
ONDŘEJ KUŽELKA ◽  
LUC DE RAEDT
Keyword(s):  
Missing Data ◽  
Expectation Maximization ◽  
Statistical Models ◽  
Missing Values ◽  
Probability Distributions ◽  
Rule Learning ◽  
Relational Data ◽  
Probabilistic Logic ◽  
Programming Framework ◽  
Distinguishing Features

Abstract Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses (DCs), which supports both discrete and continuous probability distributions. Within this framework, we introduce DiceML – an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data). To realize this, DiceML integrates statistical modeling and DCs with rule learning. The distinguishing features of DiceML are that it (1) tackles autocompletion in relational data, (2) learns DCs extended with statistical models, (3) deals with both discrete and continuous distributions, (4) can exploit background knowledge, and (5) uses an expectation–maximization-based (EM) algorithm to cope with missing data. The empirical results show the promise of the approach, even when there is missing data.

scholarly journals A Comparative Study on TIBA Imputation Methods in FCMdd-Based Linear Clustering with Relational Data

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Takeshi Yamamoto ◽  
Katsuhiro Honda ◽  
Akira Notsu ◽  
Hidetomo Ichihashi
Keyword(s):  
Comparative Study ◽  
Iterative Algorithm ◽  
Fuzzy Clustering ◽  
Incomplete Data ◽  
Numerical Experiments ◽  
Missing Values ◽  
Relational Data ◽  
Imputation Methods ◽  
Clustering Model ◽  
Linear Cluster

Relational fuzzy clustering has been developed for extracting intrinsic cluster structures of relational data and was extended to a linear fuzzy clustering model based on Fuzzyc-Medoids (FCMdd) concept, in which Fuzzyc-Means-(FCM-) like iterative algorithm was performed by defining linear cluster prototypes using two representative medoids for each line prototype. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values, and the applicability of several imputation methods is compared. In several numerical experiments, it is demonstrated that some pre-imputation strategies contribute to properly selecting representative medoids of each cluster.

scholarly journals GRASP: a Bayesian network structure learning method using adaptive sequential Monte Carlo

2019 ◽  
Author(s):  
Kaixian Yu ◽  
Zihan Cui ◽  
Xing Qiu ◽  
Jinfeng Zhang
Keyword(s):  
Monte Carlo ◽  
Network Structure ◽  
Biological Networks ◽  
Missing Values ◽  
Sequential Monte Carlo ◽  
Structure Learning ◽  
Heterogeneous Data ◽  
Optimal Network ◽  
Genomic Studies ◽  
Open Question

AbstractBayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex dependence structures. BNs can be used to infer complex biological networks using heterogeneous data from different sources with missing values. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC) based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the diversity of sampled networks which were further improved by a new stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.

An alter-centric perspective on employee innovation: The importance of alters’ creative self-efficacy and network structure.

2017 ◽  
Vol 102 (9) ◽  
pp. 1360-1374 ◽  
Author(s):  
Travis J. Grosser ◽  
Vijaya Venkataramani ◽  
Giuseppe (Joe) Labianca
Keyword(s):  
Network Structure ◽  
Self Efficacy ◽  
Creative Self

Türkiye'de iller arası göçlerin nodexl ile sosyal ağ analizi

2017 ◽  
Vol 4 (1) ◽  
pp. 82-109 ◽  
Author(s):  
Mustafa Yakar ◽  
Fatma Sert Eteman
Keyword(s):  
Network Structure ◽  
Population Data ◽  
Single Mode ◽  
Turkish Population ◽  
Place Of Birth ◽  
Place Of Residence ◽  
Migration Flow ◽  
Population Structures ◽  
The Republic ◽  
Provincial Scale

Türkiye'de 20.yy'ın ortasından itibaren başlayan iç göçler zamanla kurulan göçmen ağları ile süreklilik kazanmış ve ülke içinde nüfusun kır-kent dağılımını değiştirecek boyutlara erişmiştir. Araştırma, göçün doğum yeri verisinden hareketle ikamet edilen yerdeki nüfus miktarına göre alınan ve verilen göç akışının büyüklüğünü iller ölçeğinde yönlü ağlar kullanılarak analiz edilmesini amaçlamaktadır. Araştırmada, TÜİK tarafından yayınlanmış olan 2015 yılına ait, iller ölçeğinde doğum yerine göre ikamet yeri verisi kullanılmıştır. Göçün kaynak ve hedef sahaları arasındaki akışını incelemek için NodeXL ile oluşturulan tek modlu, yönlü ve ağırlıklandırılmış göç ağının istatistiksel olarak tam ağ yapısına sahip olduğu görülmüştür. Ağ grafiklerinden ve istatistiklerinden göç hareketinin doğudan batıya doğru gerçekleştiği ve İstanbul’ un ülkenin tamamına hâkim bir görünüme sahip olduğu anlaşılmaktadır. Türkiye nüfusunun cumhuriyet tarihi içinde geçirdiği iç göç süreçleriyle birlikte ülke içinde kurulmuş ve oldukça karmaşık bir görünüme sahip ağ yapısının olduğu ileri sürülebilir. Kurulan ağlar göçlerin devamını sağladığı gibi, göçün yöneldiği merkezlerde daha heterojen nüfus yapılarının ortaya çıkmasına yol açmıştır.ABSTRACT IN ENGLISHSocial Network Analysis of Migration Inter Provinces In Turkey with Nodexl The internal migrations which started in Turkey in the middle of the 20th century have gained permanency with the migration networks that were established at the time and reached dimensions which have the potential to change the rural-urban distribution of the population within the country.  The study aims to analyze the magnitude of the incoming and outgoing migration flow at the provincial scale based on the population data for place of birth according to place of residence by using directional networks. Place of residence according to place of birth at the provincial scale data for 2015 published by TÜİK was used in the study. A single mode, directional and weighted migration network created with NodeXL to examine the migration flows between the source and target has a statistically complete network structure. The network graphs and statistics show that the migrations have taken place from east to west and Istanbul has a view as dominant of the country. It can be argued that internal network structure of Turkish population has  a very complex view because of internal migration in the history of the republic. The established networks have enabled the continuation of migration and have manifested as the emergence of more heterogeneous population structures in centers where migration had been directed.

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