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A Neural Influence Diffusion Model for Social Recommendation [article]

Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang
2019 arXiv   pre-print
In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation.  ...  We argue that, for each user of a social platform, her potential embedding is influenced by her trusted users.  ...  CONCLUSIONS In this paper, we proposed a DiffNet neural model for social recommendation.  ... 
arXiv:1904.10322v1 fatcat:hyo2gnvjznejheoc63hojpor3q

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation [article]

Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, Meng Wang
2021 arXiv   pre-print
Recently, we propose a preliminary work of a neural influence diffusion network (i.e., DiffNet) for social recommendation (Diffnet), which models the recursive social diffusion process to capture the higher-order  ...  In this paper, we propose DiffNet++, an improved algorithm of DiffNet that models the neural influence diffusion and interest diffusion in a unified framework.  ...  CONCLUSIONS AND FUTURE WORK In this paper, we presented a neural social and interest diffusion based model, i.e., DiffNet++, for social recommendation.  ... 
arXiv:2002.00844v4 fatcat:c3ru4lxozfef7hovzgwojrxqt4

SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation [article]

Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang
2019 arXiv   pre-print
To this end, in this paper, we propose an effective graph convolutional neural network based model for social recommendation.  ...  Based on a classical CF model, the key idea of our proposed model is that we borrow the strengths of GCNs to capture how users' preferences are influenced by the social diffusion process in social networks  ...  Model Architecture In this part, we build a SocialGCN model that depicts the influence of propagation on social networks for social recommendation.  ... 
arXiv:1811.02815v2 fatcat:rg7ak7rywfbjxlljcfdrdcvhsa

A New Supervised Epidemic Model for Intelligent Viral Content Classification

Abdulkerim Şenoğlu, Uraz Yavanoğlu, Suat Özdemir
2016 International Journal of Intelligent Systems and Applications in Engineering  
The outputs of the proposed model are shown to be useful for the provenance problem and the diffusion prediction systems in both physical and social networks.  ...  We collected epidemically diffused data from Twitter with supervision to create a ranking system that forms the base of our diffusion model.  ...  Yang, Jaewon, and Jure Leskovec developed a Linear Influence Model that predicts global influence of a node in the network on the rate of diffusion.  ... 
doi:10.18201/ijisae.2016specialissue-146977 fatcat:faceb3vucneyrie4s5xbyrcilm

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation [article]

Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang
2021 arXiv   pre-print
In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this field to facilitate researchers  ...  Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.  ...  For instance, DiffNet++ is proposed to jointly model the interest diffusion from user-item bipartite graph and the influence diffusion from the user-user social graph for user modeling in social recommendation  ... 
arXiv:2104.13030v3 fatcat:7bzwaxcarrgbhe36teik2rhl6e

Step Out of Your Comfort Zone: More Inclusive Content Recommendation for Networked Systems [article]

Jiaxin Wu, Supawit Chockchowwat
2021 arXiv   pre-print
To counteract the bias, we will consider information dissemination across network as a metric to assess the recommendation for contents e.g. new connections and news feed.  ...  One crucial task on the social network is to recommend new content based on special characteristics of the graph structure.  ...  And the influence diffusion layer models the dynamic diffusion process for the user's latent preference.  ... 
arXiv:2106.10408v1 fatcat:tccfan6a6zgmthfciwg2q3lrmm

Hierarchical Social Recommendation Model Based on a Graph Neural Network

Zhongqin Bi, Lina Jing, Meijing Shan, Shuming Dou, Shiyang Wang, Xiaoxian Yang
2021 Wireless Communications and Mobile Computing  
Therefore, integrating social information into recommendation systems is of profound importance. We present an efficient network model for social recommendation.  ...  With the continuous accumulation of social network data, social recommendation has become a widely used recommendation method.  ...  DiffNet [44] is a hierarchical influence propagation structure for the simulation of the recursive dynamic diffusion process in social recommendations.  ... 
doi:10.1155/2021/9107718 fatcat:s4ovvdloyjgfdi7wwvvytfbox4

DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction [article]

Ruijie Wang, Zijie Huang, Shengzhong Liu, Huajie Shao, Dongxin Liu, Jinyang Li, Tianshi Wang, Dachun Sun, Shuochao Yao, Tarek Abdelzaher
2021 arXiv   pre-print
This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media.  ...  Moreover, it has the lowest run-time compared with recurrent neural network based models.  ...  the dynamic adjcency matrix and node attributes, and predicts the link existence. • GraphRec [6]: a novel graph neural network framework for social recommendations, which coherently models interactions  ... 
arXiv:2106.03251v1 fatcat:forh47cfcnhb3dlk44wq4mqr2u

Who is next: rising star prediction via diffusion of user interest in social networks [article]

Xuan Yang, Yang Yang, Jintao Su, Yifei Sun, Shen Fan, Zhongyao Wang, Jun Zhang, Jingmin Chen
2022 arXiv   pre-print
To address above challenges, in this paper, we observe that the presence of rising stars is closely correlated with the early diffusion of user interest in social networks, which is validated in the case  ...  Thus, we propose a novel framework, RiseNet, to incorporate the user interest diffusion process with the item dynamic features to effectively predict rising stars.  ...  More specifically, we first calculate two weights respectively: item attention for modeling dynamic item information's influence; and relationship attention for modeling user relationship' influence.  ... 
arXiv:2203.14807v2 fatcat:ah4i5bdct5hxfdzqvjazh5ce2u

Social Recommendation System Based on Hypergraph Attention Network

Zhongxiu Xia, Weiyu Zhang, Ziqiang Weng, Syed Hassan Ahmed
2021 Computational Intelligence and Neuroscience  
We propose a model that applies the hypergraph attention network to the social recommendation system (HASRE) to solve this problem.  ...  However, because the influence of the users' friends is different, we use the graph attention mechanism to capture the users' attention to different friends and adaptively model selection information for  ...  DiffNet++ [11] models influence diffusion and interest diffusion of neural networks in a unified framework based on DiffNet [12] .  ... 
doi:10.1155/2021/7716214 fatcat:mkbbpptjmrhetesyajq3vxpj4e

Dual Side Deep Context-aware Modulation for Social Recommendation [article]

Bairan Fu and Wenming Zhang and Guangneng Hu and Xinyu Dai and Shujian Huang and Jiajun Chen
2021 arXiv   pre-print
Specifically, we first proposed a novel graph neural network to model the social relation and collaborative relation, and on top of high-order relations, a dual side deep context-aware modulation is introduced  ...  Social recommendation is effective in improving the recommendation performance by leveraging social relations from online social networking platforms.  ...  ACKNOWLEDGMENTS We want to express gratitude to the anonymous reviewers for their hard work. This work is funded by NSFC 61976114 and NSFC 61936012.  ... 
arXiv:2103.08976v1 fatcat:svlxdyqawjcadpgwnai2enwkji

Knowledge-aware Coupled Graph Neural Network for Social Recommendation [article]

Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, Yanfang Ye
2021 arXiv   pre-print
While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider  ...  To tackle the above challenges, this work proposes a Knowledge-aware Coupled Graph Neural Network (KCGN) that jointly injects the inter-dependent knowledge across items and users into the recommendation  ...  Acknowledgments We thank the anonymous reviewers for their constructive feedback and comments.  ... 
arXiv:2110.03987v1 fatcat:ra6xspadufe5dfjgntb5mfxlli

Discovering influencers for marketing in the blogosphere

Yung-Ming Li, Cheng-Yang Lai, Ching-Wen Chen
2011 Information Sciences  
Introduction With the advent of online social networking, word-of-mouth (or viral) marketing is increasingly being recognized as a crucial strategy in social influence and marketing domains.  ...  recommendations to their friends [32] .  ...  The MATLAB neural network toolbox is applied to construct a three-layer ANN for training and testing the appropriate neural network model.  ... 
doi:10.1016/j.ins.2011.07.023 fatcat:chhwpkmycbauhp4fcjwnz63fei

Information Cascades Prediction With Graph Attention

Zhihao Chen, Jingjing Wei, Shaobin Liang, Tiecheng Cai, Xiangwen Liao
2021 Frontiers in Physics  
Then, for temporal feature, a recurrent neural network is built to learn their structural context in several different time intervals based on timestamp with a time-decay attention.  ...  To that end, in this paper, we propose a recurrent neural network model with graph attention mechanism, which constructs a seq2seq framework to learn the spatial-temporal cascade features.  ...  [35] proposed to a social recommendation via a dynamic graph method. They encoded the long-short term preferences for users in a session based on RNN.  ... 
doi:10.3389/fphy.2021.739202 fatcat:i372tbbuw5h2hgoxptenkvj6ym

Identifying bloggers with marketing influence in the blogosphere

Yung-Ming Li, Cheng-Yang Lai, Ching-Wen Chen
2009 Proceedings of the 11th International Conference on Electronic Commerce - ICEC '09  
Finding influential bloggers will not only allow us to better understand interesting activities happening in a social network, but also present unique opportunities for sales and advertisements.  ...  In this paper, we address a novel problem of finding influential bloggers with marketing value in the blogosphere by proposing a MIV (Marketing Influential Value) model.  ...  Back-propagation Neural Network Artificial neural network (ANN) is a model which is utilize mathematical or computational model based on biological neural networks.  ... 
doi:10.1145/1593254.1593307 dblp:conf/ACMicec/LiLC09 fatcat:mbbml7nahjalllijbl3w6mimiq
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