An incremental method to detect communities in dynamic evolving social networks

Zhao, Zhongying; Zhang, Xuejian; Li, Chao; Chiclana, Francisco; Herrera‐Viedma, Enrique

doi:10.1016/j.knosys.2018.09.002

Cited by 111 publications

(49 citation statements)

References 28 publications

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“…We use the same method to calculate the matching degree, and the result is (4,4). (d) The results in the later iterations, which are (3,3), (2,2), (7,7), and (9,9).…”

Section: Problem Statementmentioning

confidence: 99%

“…Several researchers have conducted studies in this field relating to clustering [6], link prediction [7], information diffusion [1], and community detection [8,9], among others. However, it is difficult to apply such a representation to describe multilayer structures such as multiple OSNs, multiple transportation networks [10] as well as the dynamic networks [7,9]. The multilayer structures have a significant influence on the aspects of cascade [11,12], propagation [13,14,15,16], synchronization [17], and game [18,19,20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interlayer link prediction in multiplex social networks: An iterative degree penalty algorithm

Tang

Jiang

Chen

et al. 2020

Knowledge-Based Systems

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“…We use the same method to calculate the matching degree, and the result is (4,4). (d) The results in the later iterations, which are (3,3), (2,2), (7,7), and (9,9).…”

Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interlayer link prediction in multiplex social networks: An iterative degree penalty algorithm

Tang

Jiang

Chen

et al. 2020

Knowledge-Based Systems

View full text Add to dashboard Cite

show abstract

“…Secondly, we can use some additional information of items such as genres of movies [33] to improve the recommendation diversity. Thirdly, it's fun and useful to apply MFMAP to some special scenarios, such as social network [34][35] or context-aware recommendations [36].…”

Section: Discussionmentioning

confidence: 99%

MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System

Zhao¹,

Fu²,

Sun³

et al. 2019

KSII TIIS

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Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

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“…One of the common real-world networks in community detection is Zachary's karate club which is a real example of a social network of 34 members (nodes) in a karate club and usually used as a benchmark dataset to evaluate community detection algorithms as well [30]. One of the recent contributions was an incremental method to detect communities in dynamic evolving social networks which was motivated by how previous community detection methods were static [31].…”

Section: Related Workmentioning

confidence: 99%

Causality-based Social Media Analysis for Normal Users Credibility Assessment in a Political Crisis

Abouzeid

Granmo

Webersik

et al. 2019

2019 25th Conference of Open Innovations Association (FRUCT)

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Information trustworthiness assessment on political social media discussions is crucial to maintain the order of society, especially during emergent situations. The polarity nature of political topics and the echo chamber effect by social media platforms allow for a deceptive and a dividing environment. During a political crisis, a vast amount of information is being propagated on social media, that leads up to a high level of polarization and deception by the beneficial parties. The traditional approaches to tackling misinformation on social media usually lack a comprehensive problem definition due to its complication. This paper proposes a probabilistic graphical model as a theoretical view on the problem of normal users credibility on social media during a political crisis, where polarization and deception are keys properties. Such noisy signals dramatically influence any attempts for misinformation detection. Hence, we introduce a causal Bayesian network, inspired by the potential main entities that would be part of the process dynamics. Our methodology examines the problem solution in a causal manner which considers the task of misinformation detection as a question of cause and effect rather than just a classification task. Our causality-based approach provides a practical road map for some sub-problems in real-world scenarios such as individual polarization level, misinformation detection, and sensitivity analysis of the problem. Moreover, it facilitates intervention simulations which would unveil both positive and negative effects on the deception level over the network.

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An incremental method to detect communities in dynamic evolving social networks

Cited by 111 publications

References 28 publications

Interlayer link prediction in multiplex social networks: An iterative degree penalty algorithm

Interlayer link prediction in multiplex social networks: An iterative degree penalty algorithm

MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System

Causality-based Social Media Analysis for Normal Users Credibility Assessment in a Political Crisis

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