Learning Concept Interestingness for Identifying Key Structures From Social Networks

Abstract

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.

Overlapping Community Detection Based on Weak Equiconcept

“…Gao et al [32] suggested the employment of graph topological metrics for node ranking and neighborhood inflation for seed selection. Lastly, Gao et al [22] utilized formal context methods for the selection of local key structures.…”

“…In addition, two key operations were defined for extracting the common attributes/public objects for a given set of objects/attributes. In recent years, many scholars have successfully integrated FCA with specific tasks such as data mining [15], machine learning [16], [17], [18], knowledge discovery [19], [20], cloud computing [21], complex network [22], [23], [24], [25], and so on.…”

“…For instance, Hao et al [23], [24] combined graph networks with FCA, conducting a thorough analysis of social networks and proposing a k-clique detection method applied to community discovery. Gao et al [22] established an equivalence relationship between FCA and critical structures in graph networks, introducing a method for detecting key structures. Moreover, Yang et al [25] extended static networks to dynamic ones, reinforcing the integration of FCA with networks.…”

Non-redundant implicational base of formal context with constraints using SAT

“…In computer science, some studies were successful to apply formal concept analysis for solving some problems in many sub-domains, e.g ., datamining ( Aragón, Medina & Ramírez-Poussa, 2022 ; Hao et al, 2023 ), machine learning ( Janostik, Konecny & Krajča, 2022 ), data science ( Bazin et al, 2022 ), intelligent system ( Shao et al, 2023 ), information retrieval ( Ojeda-Hernández, López-Rodríguez & Mora, 2023 ; Khattak et al, 2021 ), natural language processing ( Marín et al, 2021 ; Jain, Seeja & Jindal, 2020 ), decision support system ( Wei et al, 2020 ), recommendation system ( Liu et al, 2022 ), semantic web ( Jindal, Seeja & Jain, 2020 ), cloud computing ( Khemili, Hajlaoui & Omri, 2022 ), data structure ( Ferré & Cellier, 2020 ), mobile application ( Kwon et al, 2021 ), software engineering ( Carbonnel et al, 2020 ), and robotic ( Zhang et al, 2023 ). In addition, some successful studies to apply formal concept analysis were in other domains, e.g ., engineering ( Rocco, Hernandez-Perdomo & Mun, 2020 ), mathematics ( Jäkel & Schmidt, 2022 ; Rocco, Hernandez-Perdomo & Mun, 2020 ), biology ( Gély et al, 2022 ), psychology ( Belohlavek & Mikula, 2022 ), medicine ( Md Saleh, Ab Ghani & Jilani, 2022 ), business ( Wajnberg et al, 2018 ; Ravi, Ravi & Prasad, 2017 ; Acharjya & Das, 2017 ), and social science ( Lang & Yao, 2023 ; Hao et al, 2021 ; Gao et al, 2021 ).…”

CNFRD: A Few-Shot Rumor Detection Framework via Capsule Network for COVID-19

“…Tey help people gather, communicate, and share their common interests [4]. Multiple social network platforms, such as Facebook, Twitter, Sina Weibo, and a few e-mail systems [5], allow users to access various services simultaneously [6,7]. Te use of online platforms is exponentially amplifed, which increases the risk of information leakage and opens the door to several cybercrimes due to the large amount of data and information available on these online platforms [8,9].…”