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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.
Jie Gao; Fei Hao; Zheng Pei; Geyong Min. Learning Concept Interestingness for Identifying Key Structures from Social Networks. IEEE Transactions on Network Science and Engineering 2021, PP, 1 -1.
AMA StyleJie Gao, Fei Hao, Zheng Pei, Geyong Min. Learning Concept Interestingness for Identifying Key Structures from Social Networks. IEEE Transactions on Network Science and Engineering. 2021; PP (99):1-1.
Chicago/Turabian StyleJie Gao; Fei Hao; Zheng Pei; Geyong Min. 2021. "Learning Concept Interestingness for Identifying Key Structures from Social Networks." IEEE Transactions on Network Science and Engineering PP, no. 99: 1-1.
The concept stability measure under the Formal Concept Analysis (FCA) theory is useful for improving the accuracy of structure identification of social networks. Nevertheless, the stability calculation is an NP-complete task which is the primary challenges in practical. Most existing studies have focused on the approximate estimate to calculate the stability. Therefore, we focus on introducing the Maximal Non-Generator-based Stability Calculation (MNG-SC) algorithm that directly deals with accurate stability calculation to pave the way for FCA’s application in structures identification of social networks. Specifically, a novel perspective of stability calculation by linking it to Maximal Non-Generator (MNG) is first provided. Then, the equivalence between maximal non-generator and lower neighbor concept is first proved, which greatly improves scalability and reduces computational complexity. The performed experiments show that the MNG-SC outperforms the pioneering approaches of the literature. Furthermore, a case study of identifying abnormal users in social networks is presented, which demonstrates the effectiveness and potential application of our algorithm.
Jie Gao; Fei Hao; Doo-Soon Park. On the Computation of Concept Stability Based on Maximal Non-Generator for Social Networking Services. Applied Sciences 2020, 10, 8618 .
AMA StyleJie Gao, Fei Hao, Doo-Soon Park. On the Computation of Concept Stability Based on Maximal Non-Generator for Social Networking Services. Applied Sciences. 2020; 10 (23):8618.
Chicago/Turabian StyleJie Gao; Fei Hao; Doo-Soon Park. 2020. "On the Computation of Concept Stability Based on Maximal Non-Generator for Social Networking Services." Applied Sciences 10, no. 23: 8618.