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Extracting Actionable Knowledge from Social Networks using Structural Features

Nasrin Kalanat, Eynollah Khanjari, Alireza Khanshan
2020 IEEE Access  
INDEX TERMS Social networks mining, action mining, actionable knowledge discovery, structural features, change propagation, change-awareness.  ...  Actionable knowledge discovery is a field of study specifically developed for this matter. Existing methods rarely tackled the problem of extracting actionable knowledge from social networks.  ...  They benefit from a causal structure obtained from data and use causal inference to extract actions based on causality.  ... 
doi:10.1109/access.2020.2983146 fatcat:pzmygpvgbrcedi3utq72csb6vm

Causal Discovery for Manufacturing Domains [article]

Katerina Marazopoulou, Rumi Ghosh, Prasanth Lade, David Jensen
2016 arXiv   pre-print
This work demonstrates how data mining and knowledge discovery can be used for root cause analysis in the domain of manufacturing and connected industry.  ...  We apply causal structure learning techniques on real data collected from this line.  ...  Learning causal models from observational data In this section we briefly review how to recover the structure of Bayesian networks from data.  ... 
arXiv:1605.04056v2 fatcat:kqef7cwlqnh6fafjpivgq5jlwu

Levels of the social [chapter]

Daniel Little
2007 Philosophy of Anthropology and Sociology  
Can we assert causal connections from one level to another? Do high-level social structures have causal powers? Do they have effects on local behavior and local institutions?  ...  How do familiar objects of social science investigation like systems of norms, social networks, local social units, families, labor organizations, practices, organizations, institutions, and political  ...  Structuralism asserts that social structures have causal powers that are independent from the actions and states of mind of individuals; social causation is in some sense autonomous from the states and  ... 
doi:10.1016/b978-044451542-1/50010-6 fatcat:2bnmgqjmdfcsfdoxakscqgmewu

Discovering Social Networks Instantly: Moving Process Mining Computations to the Database and Data Entry Time [chapter]

Alifah Syamsiyah, Boudewijn F. van Dongen, Wil M. P. van der Aalst
2017 Lecture Notes in Business Information Processing  
Moreover, the database also has a role as an engine to compute the intermediate structure of social network during insertion data.  ...  In this paper, we focus on discovering social networks from event data.  ...  The data showed that social networks are structurally different from the web network.  ... 
doi:10.1007/978-3-319-59466-8_4 fatcat:b543kfqikvcmfeb4wzmfi7ikmi

Causality Learning: A New Perspective for Interpretable Machine Learning [article]

Guandong Xu, Tri Dung Duong, Qian Li, Shaowu Liu, Xianzhi Wang
2021 arXiv   pre-print
However, the traditional interpretable machine learning focuses on the association instead of the causality.  ...  This paper provides an overview of causal analysis with the fundamental background and key concepts, and then summarizes most recent causal approaches for interpretable machine learning.  ...  Using Bayesian Networks for Causal Inference QuantumBlack Labs TIGRAMITE Causal discovery for time series datasets PCMCI [63], Generally [64], CMIknn [65], Mediation class [66], [67] GNU General Public  ... 
arXiv:2006.16789v2 fatcat:ole3dvpnjnfkflldd6to4nrrwq

Proof and Uncertainty in Causal Claims

Martine Jayne Barons, Rachel L Wilkerson
2018 Exchanges  
We review differing techniques for determining cause in different disciplines using causal theories from psychology, medicine, and economics.  ...  From Hume to Granger, and Rubin to Pearl the history of science is full of examples of scientists testing new theories in an effort to uncover causal mechanisms.  ...  Acknowledgments Rachel Wilkerson is a Bridges funded PhD student (a Leverhulme Trust Doctoral Scholarships programme, funded by the Leverhulme Trust and the University of Warwick).  ... 
doi:10.31273/eirj.v5i2.238 fatcat:xjzeiclvzjgpvjfenjpxf2th5a

Theorisation In Critical Realist Is Research And Its Implications On Structure And Agency Interplay: A Morphogenetic Approach

Chidi G. Ononiwu, Irwin Brown
2013 European Conference on Information Systems  
Drawing on Archer's morphogenetic approach, derived from the critical realism (CR) philosophy, we discuss the relevance of understanding theory/theorisation from a critical realist perspective and its  ...  We then propose the morphogenetic model of emergent IT usage behaviour which questions the assumption of the duality of structure and agency, instead of conceptualising them separately.  ...  and technological contexts to constitute a network-in-use for everyday life banking activities of users, none of whom have complete knowledge of the entire network, and all of whom are conditioned by the  ... 
dblp:conf/ecis/OnoniwuB13 fatcat:xsygjpyz5batzbtib43vw4q7ay

Opportunity or dead end? Rethinking the study of entrepreneurial action without a concept of opportunity

John Kitching, Julia Rouse
2016 International Small Business Journal  
and consequences of entrepreneurial action -one that needs no concept of opportunity.  ...  and cultural causal powers.  ...  of entrepreneurial action, they adopt, implicitly, a layered social ontology that distinguishes the causal powers of structures and cultures, from actual practices and experiences.  ... 
doi:10.1177/0266242616652211 fatcat:m6r3ntl2qfe6fh4avonlhue7jm

Knowledge discovery from observational data for process control using causal Bayesian networks

Jing Li, Jianjun Shi
2007 IIE Transactions  
and the support from OG Technologies, Inc.  ...  Acknowledgements The authors gratefully acknowledge the financial support of the NSF Engineering Research Center for Reconfigurable Machining Systems (NSF grant EEC95-92125) at the University of Michigan  ...  Incorporating manufacturing domain knowledge with causal discovery Causal discovery from observational data consists of two steps: learning the structure (i.e., the DAG) and learning the parameters (i.e  ... 
doi:10.1080/07408170600899532 fatcat:6sx566byfnahtggflzbv3kipmq

Embedding entrepreneurial regional innovation ecosystems: reflecting on the role of effectual entrepreneurial discovery processes

L. Nieth, P. Benneworth, D. Charles, L. Fonseca, C. Rodrigues, M. Salomaa, M. Stienstra
2018 European Planning Studies  
In this paper, we argue there is an issue arising from the way these agency activation strategies are supposed to develop long-term plans, as partners' mind-sets may be too causal and lack the flexibility  ...  We use a qualitative case study approach comparing entrepreneurial discovery processes in three less successful regions, namely Twente (Netherlands), Aveiro (Portugal), and Lincolnshire (UK), drawing on  ...  In a recent study on path creation in Denmark, it was concluded that the renewal of paths is a result of joint contributions through "social action by knowledgeable pioneering individuals, universities  ... 
doi:10.1080/09654313.2018.1530144 fatcat:ds7va6w2wnb2rewbjgqpjrxy5i

Causality: Objectives and Assessment

Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf
2010 Journal of machine learning research  
The NIPS 2008 workshop on causality provided a forum for researchers from different horizons to share their view on causal modeling and address the difficult question of assessing causal models.  ...  There has been a vivid debate on properly separating the notion of causality from particular models such as graphical models, which have been dominating the field in the past few years.  ...  Acknowledgments This project is an activity of the Causality Workbench supported by the Pascal network of excellence funded by the European Commission and by the U.S.  ... 
dblp:journals/jmlr/GuyonJS10 fatcat:4qd2hojrfrceloronsn4snjruu

Causal Modeling of Twitter Activity During COVID-19 [article]

Oguzhan Gencoglu, Mathias Gruber
2020 medRxiv   pre-print
We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.  ...  In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g. number of infections and deaths) and Twitter activity as well  ...  Our results show that the proposed structure discovery method can successfully capture the epidemiological domain knowledge.  ... 
doi:10.1101/2020.05.16.20103903 fatcat:hpywgp3w6zhejhrsmf3eam5dgm

Causal Modeling of Twitter Activity during COVID-19

Oguzhan Gencoglu, Mathias Gruber
2020 Computation  
We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.  ...  In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g., number of infections and deaths) and Twitter activity as well  ...  Our results show that the proposed structure discovery method can successfully capture the epidemiological domain knowledge.  ... 
doi:10.3390/computation8040085 fatcat:amuefp4yanalngqhbdfgacxqjy

Causal Feature Selection [chapter]

Isabelle Guyon, Constantin Aliferis, Andr´e Elissee.
2007 Computational Methods of Feature Selection  
We examine situations in which the knowledge of causal relationships benefits feature selection.  ...  Conversely, we highlight the benefits that causal discovery may draw from recent developments in feature selection theory and algorithms.  ...  From the point of view of causal discovery and feature selection, learning the structure of the graph is the subtask of interest.  ... 
doi:10.1201/9781584888796.ch4 fatcat:dui3jd46qjdfbfeyoi4fyx7tdy

Using Unsupervised Learning to Help Discover the Causal Graph [article]

Seamus Brady
2020 arXiv   pre-print
It is found that AitiaExplorer meets these requirements and is a useful exploratory causal analysis tool that automatically selects subsets of important features from a dataset and creates causal graph  ...  The software outlined in this paper, AitiaExplorer, is an exploratory causal analysis tool which uses unsupervised learning for feature selection in order to expedite causal discovery.  ...  They attempt to predict the causal structure of Gene Regulatory Networks (GRNs) using the covariance values in the genetic data alongside existing background knowledge of the genetic data priors to feed  ... 
arXiv:2009.10790v1 fatcat:ccvyamim6jbo5lxa5lj253sk4i
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