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.
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
The experimental results show that the prediction results obtained by considering multiple relationships are more accurate than those obtained by considering only one relationship.
Research limitations/implications
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.
Practical implications
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.
Originality/value
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.
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Andriani Kusumawati, Humam Santosa Utomo, Suharyono Suharyono and Sunarti Sunarti
The purpose of this paper is to examine the effect of sustainability on word-of-mouth (WoM) intention and revisit intention, with environmental awareness as a moderator. This…
Abstract
Purpose
The purpose of this paper is to examine the effect of sustainability on word-of-mouth (WoM) intention and revisit intention, with environmental awareness as a moderator. This study was carried out in one of the tourist destinations in Indonesia, namely, Bali.
Design/methodology/approach
The population in this study was foreign tourists visiting Bali. This study uses non-probability sampling with a purposive sampling technique and uses inferential statistics. Inferential statistics was used to test the sample data on the effect of sustainability on WoM intention and revisit intention. The statistical tool used is warp-partial least square.
Findings
Effect of sustainability on WoM intention is that the higher perception of foreign tourists in the sustainability assessment will increase the WoM intention of foreign tourists. Contrarily, lower perception of foreign tourists on sustainability assessment will lower the WoM intention of foreign tourists. Effects of sustainability on revisit intention is that the higher perception of foreign tourists in the sustainability assessment will increase the revisit intention of foreign tourists. Contrarily, the lower perception of foreign tourists in sustainability assessment will lower the revisit intention of foreign tourists. Environmental awareness moderating the effects of sustainability on revisit intention is that the higher the environmental awareness of foreign tourists visiting Bali, the stronger the influence of sustainability on revisit intention. Contrarily, the lower environmental awareness of foreign tourists visiting Bali will lower the effect of sustainability on revisit intention.
Originality/value
Destination sustainability research from the perspective of tourists has not been conducted up to the behavior intention, and research is still limited to tourist satisfaction. Research that connects destination sustainability with trust, WoM intention and revisit intention has not been found yet. WoM intention and revisit intention provide a clearer picture than behavioral intention; therefore, this study focuses on WoM intention and revisit intention variables. Destination sustainability research has not been combined with destination quality as an exogenous variable that is able to predict more precisely tourist satisfaction and behavioral intention. Research has not been found on environmental awareness in relation to the sustainability variable and behavior intention. The studies that have been carried out only focus on the effect of environmental awareness on the behavior intention (Gao et al., 2016), and the research has not yet linked it to sustainability.
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Binghai Zhou, Xiujuan Li and Yuxian Zhang
This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been…
Abstract
Purpose
This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been formulated to minimize the number of EVs and the maximum handling time by specifying the EVs and sequence of all the delivery tasks.
Design/methodology/approach
First, a mathematical programming model of point-to-point part feeding scheduling problem (PTPPFSP) with EVs is presented. Because the PTPPFSP is NP-hard, an improved multi-objective cuckoo search (IMCS) algorithm is developed with novel search strategies, possessing the self-adaptive Levy flights, the Gaussian mutation and elite selection strategy to strengthen the algorithm’s optimization performance. In addition, two local search operators are designed for deep optimization. The effectiveness of the IMCS algorithm is verified by dealing with the PTPPFSP in different problem scales.
Findings
Numerical experiments are used to demonstrate how the IMCS algorithm serves as an efficient method to solve the PTPPFSP with EVs. The effectiveness and feasibility of the IMCS algorithm are validated by approximate Pareto fronts obtained from the instances of different problem scales. The computational results show that the IMCS algorithm can achieve better performance than the other high-performing algorithms in terms of solution quality, convergence and diversity.
Research limitations/implications
This study is applicable without regard to the breakdown of EVs. The current research contributes to the scheduling of in-plant logistics for automotive assembly lines, and it could be modified to cope with similar part feeding scheduling problems characterized by just-in-time (JIT) delivery.
Originality/value
Both limited electricity capacity and no earliness and tardiness constraints are considered, and the scheduling problem is solved satisfactorily and innovatively for an efficient JIT part feeding with EVs applied to in-plant logistics.
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Anish Khobragade, Shashikant Ghumbre and Vinod Pachghare
MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity…
Abstract
Purpose
MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.
Design/methodology/approach
D3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.
Findings
Experimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.
Research limitations/implications
Despite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.
Practical implications
Link prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.
Originality/value
The representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.
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Hui Jin and Zheng Wang
To reveal the effective ways for leaders to motivate employees' innovative behaviour in complex environmental situations, the leadership rapport orientation is subdivided into two…
Abstract
Purpose
To reveal the effective ways for leaders to motivate employees' innovative behaviour in complex environmental situations, the leadership rapport orientation is subdivided into two types of values-based/instrumental rapport orientation. The mechanism of supervisor developmental feedback in mediating between leadership rapport orientation and employees' innovative behaviour and the moderating effect of ambidextrous environments is explored. This paper aims to discuss the aforementioned objective.
Design/methodology/approach
Leadership rapport orientation is divided into value-based and instrumental rapport orientation to reveal effective ways for leaders to motivate employees' innovative behaviour in complex environmental situations.
Findings
The results show that the values-based (instrumental) rapport orientation of leaders impacts employees' innovative behaviour positively (negatively).
Originality/value
Leaders' values-based/instrumental rapport orientation indirectly influences employees' innovative behaviour through supervisor developmental feedback, which positively moderates the relationship between the values-based or instrumental rapport orientation of leaders and employees' innovative behaviour and further moderates the partially mediating role of supervisor developmental feedback between leaders' values-based/instrumental rapport orientation and employees' innovative behaviour.