Applications of link prediction in social networks: A review

Daud, Nur Nasuha; Hamid, Siti Hafizah Ab; Saadoon, Muntadher; Sahran, Firdaus; Anuar, Nor Badrul

doi:10.1016/j.jnca.2020.102716

Cited by 201 publications

(65 citation statements)

References 50 publications

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“…Whereas, the problem of link type differ since the prediction methods foTr multi-object type links may require special attention and the deployment of different methods. To obtain more detailed information regarding the commonly used approaches for link prediction problem in weighted, bipartite, temporal, dynamic, signed, and heterogeneous networks, please visit [72,[157][158][159][160][161], respectively.…”

Section: Discussionmentioning

confidence: 99%

Review on Learning and Extracting Graph Features for Link Prediction

Mutlu

Oghaz

Rajabi

et al. 2020

MAKE

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Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future links as well as employed for reconstruction networks, recommender systems, privacy control, etc. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used in order to study link prediction. We conclude this study with a discussion of recent developments and future research directions.

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Section: Discussionmentioning

confidence: 99%

Review on Learning and Extracting Graph Features for Link Prediction

Mutlu

Oghaz

Rajabi

et al. 2020

MAKE

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“…Several reviews on link prediction were published to discuss the algorithms, state-of-the-art applications, challenges, and future directions of link prediction research. 37 Another innovative method is the affinity propagation (AP)-based method. The AP-based method depicts good results for community detection in social networks.…”

Section: Communities Detection By Futuristic Approachmentioning

confidence: 99%

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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Community detection in social networks is one of the advertising methods in electronic marketing. One of the approaches to find communities in large social networks is to use greedy methods, because these methods perform very fast. Greedy methods are generally designed based on local decisions; thus, inappropriate local decisions may result in an improper global solution. The use of a greedy improved index with a futuristic approach can, to some extent, prevent inappropriate local choices. Our proposed method determines the influential nodes in the social network based on the followers and following and new futuristic greedy index. It classifies the nodes based on the influential nodes by the density-based clustering algorithm with a new distance function. The proposed method can improve clustering precision to detect communities by the futuristic greedy approach. We implemented the proposed algorithm with the map-reduce technique in the Hadoop structure. Experimental results in datasets show that the average of the rand index of clusters was accomplished by 99.32% in the proposed method. In addition, these results illustrate that there is a reduction in execution time by the proposed algorithm.

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“…With the development of information technology, the data on the Internet has grown exponentially, and how to effectively provide relevant information to users in need is facing great challenges [1][2][3][4] in recent years. To this end, various information sharing systems have been spawned, and online social networks are undoubtedly one of the most popular Internet products in the last decade [5][6][7], which provides the basic conditions for maintaining social relationships, such as discovering users with similar interests and hobbies, and acquiring information and knowledge shared by other users.…”

Section: Introductionmentioning

confidence: 99%

A hybrid recommendation model in social media based on deep emotion analysis and multi-source view fusion

et al. 2020

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The recommendation system is an effective means to solve the information overload problem that exists in social networks, which is also one of the most common applications of big data technology. Thus, the matrix decomposition recommendation model based on scoring data has been extensively studied and applied in recent years, but the data sparsity problem affects the recommendation quality of the model. To this end, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion which makes a personalized recommendation with user-post interaction ratings, implicit feedback and auxiliary information in a hybrid recommendation system. Specifically, the HITS algorithm is used to process the data set, which can filter out the users and posts with high influence and eliminate most of the low-quality users and posts. Secondly, the calculation method of measuring the similarity of candidate posts and the method of calculating K nearest neighbors are designed, which solves the problem that the text description information of post content in the recommendation system is difficult to mine and utilize. Then, the cooperative training strategy is used to achieve the fusion of two recommended views, which eliminates the data distribution deviation added to the training data pool in the iterative training. Finally, the performance of the DMHR algorithm proposed in this paper is compared with other state-of-art algorithms based on the Twitter dataset. The experimental results show that the DMHR algorithm has significant improvements in score prediction and recommendation performance.

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Applications of link prediction in social networks: A review

Cited by 201 publications

References 50 publications

Review on Learning and Extracting Graph Features for Link Prediction

Review on Learning and Extracting Graph Features for Link Prediction

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

A hybrid recommendation model in social media based on deep emotion analysis and multi-source view fusion

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