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Bayesian Social Subgraph Generative Models: Social Network Twins using Belief Networks and Ego Behavior Models

2023 | HICSS | Citations: 0
Authors: Naidu, N.; El-Gayar, O.

Abstract: A key assumption of a Subgraph Generative Model (SUGM) for sparse networks is t ... Expand

Abstract: A key assumption of a Subgraph Generative Model (SUGM) for sparse networks is that a subgraph is independent of lower order subgraphs in a sparse network. This is not entirely true especially for nonsparse networks. Additionally, the generated networks lack the typical properties of a social network because of an assumption of random growth for nodes and edges. Finally, there is no concept for explicit ego choice or bias when connecting to dyadic or triadic relationships. We develop a novel graph generative model referred to as the Bayesian Social Subgraph Generative Model (BASSUGM). We ground the BASSUGM in a proposed sociological model and leverage Bayesian tools like belief networks. We introduce novel concepts like the networks' macro theme when combines with an ego's individuality realizes the ego's intent. We also demonstrate how the social network twin generated with BASSUGM outperforms SUGM for non-sparse, small, social, networks. Collapse

Semantic filters: graph theory

Topics: social network logistics management telephone

Methods: cluster analysis Bayesian network experiment hierarchical linear modeling computational algorithm

Theories: probability theory graph theory
Identifying Structural Holes for Sentiment Classification
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2022 | Information Systems Frontiers | Citations: 0
Authors: Xie, Zheng; Liu, Guannan; Qu, Jinming; Wu, Junjie; Li, Hong

Abstract: The prevalence of online user-generated content has attracted great interest in ... Expand

Abstract: The prevalence of online user-generated content has attracted great interest in textual sentiment analysis, which provides a low-cost yet effective way to discern consumers and markets. A mainstream of sentiment analysis is to construct a classification model with Bag-of-Words (BoW) features, but the large vocabulary base and skewed distribution of term frequency consistently pose research challenges, which is made even worse by the limited valid sentiment labels. In light of this, in this paper, we propose a novel method called Structural Holes based Sentiment Classifier (SHSC) for BoW-based sentiment classification. The key to SHSC is to reinforce the classification contribution of semantically rich words with clear-cut sentiment polarity. To this end, a word co-occurrence network is carefully constructed to represent both high and low frequency words. The work to find classification-inefficient words is then transformed into the identification of so-called bridge nodes that occupy the positions of structural holes in the network. Two interesting measures, i.e., information advantage rank and control advantage weight, are then designed elaborately for this purpose, which are based on the proposed sentiment-label propagation and short-path computation algorithms, respectively. SHSC finally feeds this information as the key regularizers into a simple regression model to guide parametric learning. Extensive experiments on real-world text datasets demonstrate the advantage of our SHSC model over competitive benchmarks, particularly when sentiment labels are scarce. The effectiveness of uncovering structural holes for sentiment classification is also carefully verified with some robustness checks and demonstration cases. Collapse

Semantic filters: graph theory

Topics: logistics management microblog Twitter Python Wikipedia

Methods: experiment machine learning Student's t-test support vector machine word embedding

Theories: graph theory
Disaggregating the Differential Impact of Healthcare IT in Complex Care Delivery: Insights from Field Research in Chronic Care
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2021 | Journal of the Association for Information Systems | Citations: 2
Authors: Aron, Ravi; Pathak, Praveen

Abstract: This study focuses on the impact of digitizing medical information on the effic ... Expand

Abstract: This study focuses on the impact of digitizing medical information on the efficiency and perceived quality of chronic care delivery at the individual physician level. This study extends the theory of task technology fit to activity systems consisting of highly interdependent tasks. We find that the outcomes of efficiency and quality gains are driven by the structure of interdependencies between tasks that physicians perform. While structured information plays a key role in enabling both decision-making and task execution, we find that physician-created semistructured information is also an important predictor of both efficiency and quality gains. We show that the structure of activity systems (task interdependencies) has a strong moderating influence on the factors that drive efficiency and quality gains. We find that digitization enables physicians to preprocess patients' records prior to their visit which in turn drives gains in both the efficiency and the perceived quality of care delivered. Collapse

Semantic filters: graph theory

Topics: task characteristic healthcare data decision making productivity service quality

Methods: theory development longitudinal research Student's t-test parametric test survey design

Theories: theory of task technology fit graph theory organizational theory
Towards sustainable collaborative networks for smart cities co-governance
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2021 | International Journal of Information Management | Citations: 0
Authors: Ben Yahia, Nesrine; Eljaoued, Wissem; Bellamine Ben Saoud, Narjès; Colomo-Palacios, Ricardo

Abstract: This paper addresses the concept of collaborative governance in the context of s ... Expand

Abstract: This paper addresses the concept of collaborative governance in the context of smart cities, with a focus on supporting and recommending performing organizational structures for sustainable collaborative networks (SCN). It highlights that governing a smart city is about promoting an effective environment of collaboration in the government and implying adaptive policy-making to construct new, internal and external human collaborations. Considering the smart governance as a collaborative network of government agencies and external stakeholders including citizens and a socio-technical system, we conduct in this paper an ethnographic mixed method by combining a qualitative method that studies actors’ collaboration and engagement in co-governance with a quantitative method that is based on graph theory to provide numerical analyses of organizational structures. While the qualitative method aims to discover organizational “smart factors” that affect the performance of SCN structures or configurations, the quantitative method aims to find “smart indicators” and metrics to evaluate these organizational factors. The result of this mixed method is an analytical recommender framework of the relevant SCN organizational structures in terms of robustness, flexibility and efficiency. Collapse

Semantic filters: graph theory

Topics: organization structure smart city information management emergency management hurricane

Methods: mixed method computational algorithm case study ethnography literature study

Theories: graph theory socio technical theory
Literature Reviewing: Addressing the Jingle and Jangle Fallacies and Jungle Conundrum Using Graph Theory and NLP

2021 | International Conference on Information Systems | Citations: 0
Authors: Song, Yuanyuan; Watson, Richard T.; Zhao, Xia

Abstract: Scientific advancements in all fields, including IS, are built on previous acco ... Expand

Abstract: Scientific advancements in all fields, including IS, are built on previous accomplishment. Identifying similar causal models is critical for synthesizing research. However, the growing knowledge repository and inconsistencies in existing literature (i.e., jingle and jangle fallacies) challenge humans' bounded rationality. Humans need supporting information systems to make a jungle of causal models amenable to analysis. This paper proposes using graph theory and natural language processing (NLP) methods to analyze knowledge networks and report similarity scores for causal models. This method builds on the first phase of the Theory Research Exchange (T-Rex) project, in which guidance on digitizing the core knowledge in publications is established. Digitizing core knowledge will provide an efficiency gain as illustrated in this paper and be a significant step forward for the knowledge economy. Collapse

Semantic filters: graph theory

Topics: privacy database system graph database internet technology information privacy concern

Methods: computational algorithm natural language processing survey literature study experiment

Theories: graph theory
Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm
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2020 | Business & Information Systems Engineering | Citations: 0
Authors: Filipiak, Dominik; Węcel, Krzysztof; Stróżyna, Milena; Michalak, Michał; Abramowicz, Witold

Abstract: The presented method reconstructs a network (a graph) from AIS data, which refle ... Expand

Abstract: The presented method reconstructs a network (a graph) from AIS data, which reflects vessel traffic and can be used for route planning. The approach consists of three main steps: maneuvering points detection, waypoints discovery, and edge construction. The maneuvering points detection uses the CUSUM method and reduces the amount of data for further processing. The genetic algorithm with spatial partitioning is used for waypoints discovery. Finally, edges connecting these waypoints form the final maritime traffic network. The approach aims at advancing the practice of maritime voyage planning, which is typically done manually by a ship’s navigation officer. The authors demonstrate the results of the implementation using Apache Spark, a popular distributed and parallel computing framework. The method is evaluated by comparing the results with an on-line voyage planning application. The evaluation shows that the approach has the capacity to generate a graph which resembles the real-world maritime traffic network. Collapse

Semantic filters: graph theory

Topics: Apache Spark parallel computing computer hardware

Methods: computational algorithm cumulative sum control chart experiment machine learning optimization model

Theories: graph theory
Visualizing Social Documents as Traces of Collaborative Activity in Enterprise Collaboration Platforms

2020 | HICSS | Citations: 7
Authors: Mosen, Julian; Williams, Susan P; Schubert, Petra

Abstract: Enterprise collaboration platforms are large-scale information infrastructures t ... Expand

Abstract: Enterprise collaboration platforms are large-scale information infrastructures that provide a wide range of tools and functionality to support collaborative work in organizations. These collaborative activities leave digital traces in the form of social documents, which can be analyzed to understand how employees work together to coordinate their joint work. In this paper, we present the findings of a research project to visualize the structure of social documents to prepare them for analysis as traces of collaborative activity. Using the representation of social documents defined in the Social Document Ontology (SocDOnt), we draw on concepts from graph theory to develop a method for the graphical visualization of social documents. Applying this method to analyze the social documents in an operational enterprise collaboration platform, we identify and display different types of social documents and define their characteristic structure. Our findings provide the necessary foundation for conducting computational ethnographies of collaborative work. Collapse

Semantic filters: graph theory

Topics: database system blog wiki knowledge representation digital dashboard

Methods: ontological modelling digital trace data ethnography design science design artifact

Theories: graph theory theory of affordance
Social media in times of crisis: Learning from Hurricane Harvey for the coronavirus disease 2019 pandemic response
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2020 | Journal of Information Technology | Citations: 39
Authors: Mirbabaie, Milad; Bunker, Deborah; Stieglitz, Stefan; Marx, Julian; Ehnis, Christian

Abstract: In recent times societal crises such as the coronavirus disease 2019 outbreak ha ... Expand

Abstract: In recent times societal crises such as the coronavirus disease 2019 outbreak have given rise to a tension between formal ‘command and control’ and informal social media activated self-organising information and communication systems that are utilised for crisis management decision-making. Social media distrust affects the dissemination of disaster information as it entails shifts in media perception and participation but also changes in the way individuals and organisations make sense of information in critical situations. So far, a little considered notion in this domain is the concept of sense-giving. Originating from organisational theory, it is used to explain the mechanisms behind intentional information provision that fosters collective meaning creation. In our study, we seek to understand the potential impact of sense-giving from Twitter crisis communication generated during the Hurricane Harvey disaster event. Social network and content analyses performed with a dataset of 9,414,463 tweets yielded insights into how sense-giving occurs during a large-scale disaster event. Theoretically, we specified (1) perpetual sense-giving, which relies primarily on topical authority and frequency; as well as (2) intermittent sense-giving, which occurs from high value of message content and leverage of popularity, that is, retweets. Our findings emphasise the importance of information-rich actors in communication networks and the leverage of their influence in crises such as coronavirus disease 2019 to reduce social media distrust and facilitate sense-making. Collapse

Semantic filters: graph theory

Topics: Twitter hurricane social media sensemaking pandemic

Methods: case study qualitative content analysis computational algorithm literature study social network analysis

Theories: graph theory
A Hybrid Genetic Algorithm for Software Architecture Re-Modularization
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2020 | Information Systems Frontiers | Citations: 0
Authors: Mu, Lifeng; Sugumaran, Vijayan; Wang, Fangyuan

Abstract: Software architectures have become highly heterogeneous and difficult to maintai ... Expand

Abstract: Software architectures have become highly heterogeneous and difficult to maintain due to software evolution and continuous change. Therefore, a software system usually must be restructured in terms of modules containing relatively dependent components to address the system complexity. However, it is challenging to re-modularize systems automatically to improve their maintainability. In this paper, we present a new mathematical programming model for the software re-modularization problem. In contrast to previous research, a novel metric based on the principle of complexity balance is introduced to address the issue of over-cohesiveness. In addition, a hybrid genetic algorithm (HGA) is designed to automatically determine high-quality re-modularization solutions. In the proposed HGA, a heuristic based on edge contraction and vectorization techniques is designed first to generate feature-rich solutions and subsequently implant these solutions as seeds into the initial population. Finally, a customized genetic algorithm (GA) is employed to improve the solution quality. Two sets of test problems are employed to evaluate the performance of the HGA. The first set includes sixteen real-world instances and the second set contains 900 large-scale simulated data. The proposed HGA is compared with two widely adopted algorithms, i.e., the multi-start hill-climbing algorithm (HCA) and the genetic algorithms with group number encoding (GNE). Experimental and statistical results demonstrate that in most cases, the HGA can guarantee better quality solutions than HCA and GNE. Collapse

Semantic filters: graph theory

Topics: systems development enterprise architecture agile software development systems design architectural quality

Methods: computational algorithm experiment Student's t-test parametric test simulation

Theories: graph theory
Developing a Proof-of-Concept Practices Context Model

2020 | European Conference On Information Systems | Citations: 0
Authors: Neto, Antonio Jose Rodrigues; Borges, Maria Manuel; Roque, Licinio

Abstract: In this study, we worked in collaboration with ninety-seven Proof-of-Concept (P ... Expand

Abstract: In this study, we worked in collaboration with ninety-seven Proof-of-Concept (PoC) practitioners in the development and execution of thirty different activities to map PoC practices and their relationships. Adopting a practitioner and designer mindsets, we used a Design Science Research approach to model PoC practices and their interactions, apply and study the model influence in practitioners understanding of context. We identified how practitioners incorporated model concepts and discovered interactions between practices, fostering their co-evolution. As practitioners, we framed practices in the context of activity systems, grounded in sociotechnical phenomena and used Activity Theory to substantiate our reflections on PoC practices and map mediators flowing between them. The paper contributes a conceptual-relational PoC practices context model, as a language contribution to improve reflection on practice, and to further enable the study the role of emergent practices in system design. Collapse

Semantic filters: graph theory

Topics: innovation management systems design enterprise architecture knowledge creation systems development

Methods: experiment design artifact design science field study experimental design

Theories: activity theory co-evolution theory graph theory

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Found 78 articles

Bayesian Social Subgraph Generative Models: Social Network Twins using Belief Networks and Ego Behavior Models

Identifying Structural Holes for Sentiment Classification

Disaggregating the Differential Impact of Healthcare IT in Complex Care Delivery: Insights from Field Research in Chronic Care

Towards sustainable collaborative networks for smart cities co-governance

Literature Reviewing: Addressing the Jingle and Jangle Fallacies and Jungle Conundrum Using Graph Theory and NLP

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

Visualizing Social Documents as Traces of Collaborative Activity in Enterprise Collaboration Platforms

Social media in times of crisis: Learning from Hurricane Harvey for the coronavirus disease 2019 pandemic response

A Hybrid Genetic Algorithm for Software Architecture Re-Modularization

Developing a Proof-of-Concept Practices Context Model