Social Influence Maximization in Hypergraphs

Antelmi, Alessia; Cordasco, Gennaro; Spagnuolo, Carmine; Szufel, Przemysław

doi:10.3390/e23070796

Cited by 24 publications

(13 citation statements)

References 42 publications

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“…Once its strategy achieves convergence, the agent can be used to dismantle real world networks. • SubTSSH [1] This method is designed for the problem of selecting target nodes set in hypernetworks. Through iteratively conduct nodes remeval and influence propagation, it can be used for hypernetwork dismantling.…”

Section: Experimental Datasets and Setupmentioning

confidence: 99%

Betweenness Approximation for Hypernetwork Dismantling with Hypergraph Neural Network

Yan¹,

Xie²,

Zhang³

2022

Preprint

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The purpose of Network Dismantling (ND) is to find an optimal set of nodes and removing these nodes can greatly decrease the network connectivity. However, current dismantling methods are mainly focus on traditional simple network which only consider the pairwise interaction between two nodes, while the hypernetwork, which can model higher order groupwise relation among arbitrary nodes, is more suitable for modeling real world. Due to the structural difference between simple network and hypernetwork, current dismantling methods cannot be directly applied to hypernetwork dismantling. Although some centrality measures in hypernetwork can be used for hypernetwork dismantling, they face the problem of banlancing effect and efficiency. Therefore, in this paper, we propose a novel HyperNetwork Dismantling methods based on hypergraph neural network, called HND. Specifically, our method first generates plenty of node ranking samples with the help of synthetic hypernetwork generator. Then, a node betweenness approximation model in hypernetwork is built based on hypergraph neural network. And this model is trained on those ranking samples generated in previous step until convergence. Finally, the well-trained model is utilized to approximate the nodes' betweenness in real world hypernetworks and further used for dismantling. To confirm the effectiveness of our method, we conduct extensive experiments on five real world hypernetworks. The experimental results demostrate that the HND outperforms various baselines.

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Section: Experimental Datasets and Setupmentioning

confidence: 99%

Betweenness Approximation for Hypernetwork Dismantling with Hypergraph Neural Network

Yan¹,

Xie²,

Zhang³

2022

Preprint

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“…The importance of a friend [3] evaluates the influence of a friend while visiting the POI. Numerous studies [81][82][83][84][85][86] indicate that social relations are beneficial for the recommender systems, and the use of social factors to reinforce traditional recommendation systems has been investigated, both in memory-based methods [87,88] and in model-based techniques [89][90][91]. Attention to social influence and friend impression in POI selection has improved the recommendations of traditional recommender systems.…”

Section: • Social Influence and Importance Of Friends' Behaviormentioning

confidence: 99%

“…Thus, feature engineering has been one of the key research topics for a long time, and also, feature extraction operations are based on its application type and require noteworthy human efforts. For instance, in the field of machine vision, several various types of features have been introduced and evaluated; these include histogram oriented gradients (HOG) [91], scale-invariant feature transform (SIFT) [83], and bag of words (BOW) [89]. The same conditions exist in other fields, such as natural language processing (NLP) and speech recognition.…”

Section: Deep-learning Techniques In Poi Recommender Systemsmentioning

confidence: 99%

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

2022

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In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work.

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“…For example, centrality methods combined with some selection strategies can be directly used to identify multiple influencers [ 9 , 15 , 38 , 39 ]. Other methods for IM problems include greedy algorithms [ 35 , 40 ], community-based algorithms [ 37 , 41 ], optimization-based meta-heuristic algorithms [ 1 , 42 ], and so on [ 43 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Local-Forest Method for Superspreaders Identification in Online Social Networks

Hao

Tang

Liu

et al. 2022

Entropy

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Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation models, such as epidemic-like models, which rarely consider the Push-Republish mechanism with attenuation characteristic, the unique and widely-existing spreading mechanism in online social media. To address this issue, we []proposefirst adopt the Push-Republish (PR) model []to describe the spreading processes in online social networksas the underlying spreading process to check the performance of identification methods. Then, we find that the performance of classical identification methods significantly []reducesdecreases in the PR model compared to epidemic-like models, especially when identifying the top 10% of superspreaders. Furthermore, inspired by the local tree-like structure caused by the PR model, we propose a new identification method, namely the Local-Forest (LF) method, and conduct extensive experiments in four real large networks to evaluate it. Results highlight that the Local-Forest method has the best performance in accurately identifying superspreaders compared with the classical methods.

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Social Influence Maximization in Hypergraphs

Cited by 24 publications

References 42 publications

Betweenness Approximation for Hypernetwork Dismantling with Hypergraph Neural Network

Betweenness Approximation for Hypernetwork Dismantling with Hypergraph Neural Network

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

Local-Forest Method for Superspreaders Identification in Online Social Networks

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