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The purpose of this paper is to construct a multi-relational network for an online sharing platform in the age of the sharing economy, to identify the factors impacting users’ product adoption behavior and to predict consumers’ purchases of user-generated products on the platform.

The study conducted multi-relational network analyses of five different sub-networks in identifying influential factors for e-book adoption. Meanwhile, the study adopted machine learning methods with different classification algorithms and feature sets to predict users’ purchasing behaviors.

The authors found that an individual’s adoption of a product was correlated with his or her purchasing habits and collaboration with others on the online sharing platform. Through the inclusion of network features, the authors were able to build a predictive model that forecasted consumers’ purchases of user-generated e-books with reasonable accuracy.

The interdisciplinary approach used in the study can serve as a good reference for identifying factors impacting the product adoption behavior of users in the online sharing platform, through employing different sociological and computational methods.

The outcome of the study has provided important managerial implications, especially for the design of social commerce platform in the age of the sharing economy.

The authors verified the social influence impacting consumers’ product adoption behavior and shed light on the value of collaboration in the age of the sharing economy.

The study was the first to identify user-generated e-book adoption on an online sharing platform from a multi-relational network perspective. The idea and the approach supplied a new method of behavioral analysis in the context of a sharing economy.

In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.

The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.

The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.

The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.

The aim of this paper is to propose a conceptual model of the field of informetrics. Specifically, the paper argues that informetrics comprises the study of entities in three dimensions: the social, documentary and epistemic dimensions containing respectively agents, documents, and concepts or cognitions.

The paper outlines a conceptual model, drawing on earlier work by Kochen, Leydesdorff, Borgman and others. Subsequently, each dimension and interdimensional relation is analyzed and discussed.

It is shown that not every study necessarily involves each of the three dimensions, but that the field as a whole cannot be reduced to one or two of them. Moreover, the dimensions should be kept separate but they are not completely independent. The paper discusses what kinds of relations exist between the dimensions. Special attention is given to the nature of the citation relation within this framework. The paper also considers the place of concepts like mapping, proximity and influence in the model.

This conceptual paper is a first step. Multi‐relational networks may be a key instrument to further the study of the interplay between the three dimensions.

The paper provides a framework to characterise informetric studies and makes the characteristics of the field explicit.

Much of the scholarship relating to educator learning in the context of school change centers on promising organizational structures that support educator knowledge-building and sharing. However, recent studies have found that educators' social networks also enhance learning of new practices. This study aims to explore how informal interactions support organizational learning in schools.

Applying structuration theory to concepts of organizational learning mechanisms, this paper proposes a framework for examining informal interactions and organizational learning. Employing an exploratory sequential mixed-methods design, this paper utilizes social network analysis of survey data and thematic analysis of interview data of a purposive sample of participants in a rural school district.

Within this rural district, organizational and social conditions supported recursive interactions where educators developed and shared knowledge of new instructional practices. Organizational resources and routines, and individuals' habits of mind mediated these recursive interactions, resulting in somewhat dependable knowledge-sharing spaces. Through these recursive interactions between individual agents acting within the opportunities and constraints of the normalized organizational expectations of each school, informal knowledge structures emerged.

This article applies structuration theory to examine organizational learning mechanisms in schools. This novel approach provides researchers with a new perspective on the organizational learning process—one that facilitates the exploration of the role of informal knowledge-building in this process.

Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest.

In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count.

The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data.

The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems.

Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts.

This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.

Unobtrusive measurement methodologies are critical to implementing intelligent tutoring systems (ITS) for teams. Such methodologies allow for continuous measurement of team states and processes while avoiding disruption of mission or training performance, and do not rely on post hoc feedback (including for the aggregation of data into measures or to develop insights from these real-time metrics). This chapter summarizes advances in unobtrusive measurement developed within Army research programs to illustrate the variety and potential that unobtrusive measurement approaches can provide for building ITS for teams. Challenges regarding the real-time aggregation of data and applications to current and future ITS for teams are also discussed.

The purpose of this article is to provide academicians and practitioners alike with a theory-based framework regarding horizontal collaboration in logistics. The proposed tool is based on an incremental perspective, according to two main dimensions: mutual trust among partners and the extent of the cooperation.

This study used a “synthesising” approach to gauge potential contributions previously spread across different streams of research and disciplines that are now integrated into the framework. We conduct a deep literature review to characterise the horizontal collaboration phenomenon along two levels of analysis. In doing so, we examined relevant literature in the field of horizontal cooperation in logistics to critically appraise aims of, impediments to and existing models for horizontal collaboration. Additionally, we reviewed seminal literature of four organisational theories to assess their potential to contribute to the theoretical foundations of the growing topic of horizontal collaboration. Transaction Cost Economics, Social Exchange, Resource Dependence and Social Dilemma represent the theoretical foundations to cast light to how to design and implement inter-organisational horizontal initiatives.

The proposed tool organises horizontal collaborations within three steps for each of the two levels of classification: trust and extent of the cooperation. The organisational theories reviewed play different roles to help in different stages of the horizontal collaboration. Additionally, for each combination of trust/extent of the cooperation coherent pairs of aims of the collaboration and assets that are to be shared are defined.

The article represents the first attempt to analyse horizontal collaboration from within the discipline itself and from the wider field of SCM through other well-established theoretical lenses. The proposed tool has shed some light into the black box of (un)successful horizontal collaboration, but it is theory based – which represents its main limitations – thus, requiring further testing of the research streams suggested in the paper.

The article not only gives insights into theoretical challenges of horizontal collaborations that needs further investigation but is also useful to companies involved in horizontal collaborations by helping define coherent assets that are to be shared to achieve specific goals. In its more theoretical underpinning, the framework can also inspire the partnership philosophy and help sketch a collaborative evolutionary path.

The lack of a theoretically robust landmark that could help understand, design and implement horizontal collaborations has been defined as a major theoretical and practical shortcoming. The article represents the first contribution aimed at filling that gap.

This paper advocates the use of graph database platforms to investigate networks of illicit companies identified in money laundering schemes. It explains the setup of the data structure to investigate a network of illicit companies identified in cases of money laundering schemes and presents its key application in practice. Grounded in the technology acceptance model (TAM), this paper aims to present key operationalisations and theoretical considerations for effectively driving and facilitating its wider adoption among a range of stakeholders focused on anti-money laundering solutions.

This paper explores the benefits of adopting graph databases and critiques their limitations by drawing on primary data collection processes that have been undertaken to derive a network topology. Such representation on a graph database platform provides the opportunity to uncover hidden relationships critical for combatting illicit activities such as money laundering.

The move to adopt a graph database for storing information related to corporate entities will aid investigators, journalists and other stakeholders in the identification of hidden links among entities to deter activities of corruption and money laundering.

This paper does not display the nodal data as it is framed as a background to how graph databases can be used in practice.

To the best of the authors’ knowledge, no studies in the past have considered companies from multiple cases in the same graph network and attempted to investigate the links between them. The advocation for such an approach has significant implications for future studies.

The purpose of this paper is to apply link prediction to community mining and to clarify the role of link prediction in improving the performance of social network analysis.

In this study, the 2009 version of Enron e-mail data set provided by Carnegie Mellon University was selected as the research object first, and bibliometric analysis method and citation analysis method were adopted to compare the differences between various studies. Second, based on the impact of various interpersonal relationships, the link model was adopted to analyze the relationship among people. Finally, the factorization of the matrix was further adopted to obtain the characteristics of the research object, so as to predict the unknown relationship.

The experimental results show that the prediction results obtained by considering multiple relationships are more accurate than those obtained by considering only one relationship.

Due to the limited number of objects in the data set, the link prediction method has not been tested on the large-scale data set, and the validity and correctness of the method need to be further verified with larger data. In addition, the research on algorithm complexity and algorithm optimization, including the storage of sparse matrix, also need to be further studied. At the same time, in the case of extremely sparse data, the accuracy of the link prediction method will decline a lot, and further research and discussion should be carried out on the sparse data.

The focus of this research is on link prediction in social network analysis. The traditional prediction model is based on a certain relationship between the objects to predict and analyze, but in real life, the relationship between people is diverse, and different relationships are interactive. Therefore, in this study, the graph model is used to express different kinds of relations, and the influence between different kinds of relations is considered in the actual prediction process. Finally, experiments on real data sets prove the effectiveness and accuracy of this method. In addition, link prediction, as an important part of social network analysis, is also of great significance for other applications of social network analysis. This study attempts to prove that link prediction is helpful to the improvement of performance analysis of social network by applying link prediction to community mining.

This study adopts a variety of methods, such as link prediction, data mining, literature analysis and citation analysis. The research direction is relatively new, and the experimental results obtained have a certain degree of credibility, which is of certain reference value for the following related research.