Defining and detecting k-bridges in a social network: The Yelp case, and more

Corradini, Enrico; Nocera, Antonino; Ursino, Domenico; Virgili, Luca

doi:10.1016/j.knosys.2020.105721

Cited by 13 publications

(12 citation statements)

References 35 publications

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“…Bridge connects an entity (e.g., a customer, social network user [58], business, or service) to numerous different sub-networks (e.g., customer reviews about a business or service), allowing the entity to be made known to them. An enhanced approach called k-bridge [59] is proposed to relate an entity to overlapping sub-networks, which is useful in AIM.…”

Section: Customer Loyaltymentioning

confidence: 99%

“…The peculiarities of k-bridges (i.e., users) have been applied to define crawling strategies to seek for new and updated contents in social networks, which is a significant process in the pre-processor of the AIM framework [57]. Bridges have also been applied to provide recommendations on: (a) businesses and products to prospects; (b) other users whom a user can interact with; (c) and suggestions for text used in writing new reviews [59]. These are significant processes in the main processor of the AIM framework.…”

Section: Customer Loyaltymentioning

confidence: 99%

“…In [59], k-bridge is applied to find the right target segments and individuals in order to increase market share. Using k-bridge, different sub-networks (e.g., businesses and services) are linked to provide diffusion points.…”

Section: Customer Loyaltymentioning

confidence: 99%

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Artificial Intelligence Marketing (AIM) for Enhancing Customer Relationships

2021

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Based on the literature, we present an artificial intelligence marketing (AIM) framework that enables autonomous machines to receive big data and information, use artificial intelligence (AI) to create knowledge, and then disseminate and apply the knowledge to enhance customer relationships in a knowledge-based environment. To develop the AIM framework, we bring together and curate a wide range of relevant literatures including real-life examples and cases, and then understand how these literatures contribute to the framework in this research topic. We explain the AIM framework from the interdisciplinary perspective, which is an important role of both the artificial intelligence and marketing academia. The AIM framework includes three main components, including the pre-processor, the main processor, and the memory storage. The main processor, which is the key component, uses AI to process structured data processed by pre-processor in order to make real-time decisions and reasonings. The AI approach is characterized by its hypothetical abilities, learning paradigms, and operation modes with human. The strategic use of the developed AIM framework based on the literature to enhance customer relationships, including customer trust, satisfaction, commitment, engagement, and loyalty, is presented. Finally, future potential investigations are presented to drive forward this interdisciplinary research topic.

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Section: Customer Loyaltymentioning

confidence: 99%

Section: Customer Loyaltymentioning

confidence: 99%

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Artificial Intelligence Marketing (AIM) for Enhancing Customer Relationships

2021

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“…For detecting bridging structures, a great deal of recent approaches [9], [36]- [38] have been proposed. For example, Buccafurri et al [9] have deeply studied bridges are the most basic structure of a social networking scenario and argued that most understanding of structural characteristics of networks is based on the adequate knowledge of bridges.…”

Section: A Key Structures Detectionmentioning

confidence: 99%

“…Besides, identifying bridging structures is helpful to analyze information diffusion between communities and then better serve social networking applications. This is because bridges or bridging nodes commonly located in the borders of communities are able to enable information exchange among different communities [9]. Specifically, the bridge represents the essential structure of a social networking scenario where users join different OSNs even though they are not familiar with each other but still can interact.…”

Section: Introductionmentioning

confidence: 99%

Learning Concept Interestingness for Identifying Key Structures From Social Networks

Gao

Hao

Pei

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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Identifying key structures from social networks that aims to discover hidden patterns and extract valuable information is an essential task in the network analysis realm. These different structure detection tasks can be integrated naturally owing to the topological nature of key structures. However, identifying key network structures in most studies has been performed independently, leading to huge computational overheads. To address this challenge, this paper proposes a novel approach for handling key structures identification tasks simultaneously under the unified Formal Concept Analysis (FCA) framework. Specifically, we first implement the FCA-based representation of a social network and then generate the fine-grained knowledge representation, namely concept. Then, an efficient concept interestingness calculation algorithm suitable for social network scenarios is proposed. Next, we then leverage concept interestingness to quantify the hidden relations between concepts and network structures. Finally, an efficient algorithm for jointly key structures detection is developed based on constructed mapping relations. Extensive experiments conducted on real-world networks demonstrate that the efficiency and effectiveness of our proposed approach.

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Close Contact Detection in Social Networks via Possible Attribute Analysis

Huang

2022

Rough Sets

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Defining and detecting k-bridges in a social network: The Yelp case, and more

Cited by 13 publications

References 35 publications

Artificial Intelligence Marketing (AIM) for Enhancing Customer Relationships

Artificial Intelligence Marketing (AIM) for Enhancing Customer Relationships

Learning Concept Interestingness for Identifying Key Structures From Social Networks

Close Contact Detection in Social Networks via Possible Attribute Analysis

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