research-article

CMINet: a Graph Learning Framework for Content-aware Multi-channel Influence Diffusion

Authors:

Wang-Chien Lee,

Ming-Syan ChenAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 545 - 555

https://doi.org/10.1145/3543507.3583465

Published: 30 April 2023 Publication History

Abstract

The phenomena of influence diffusion on social networks have received tremendous research interests in the past decade. While most prior works mainly focus on predicting the total influence spread on a single network, a marketing campaign that exploits influence diffusion often involves multiple channels with various information disseminated on different media. In this paper, we introduce a new influence estimation problem, namely Content-aware Multi-channel Influence Diffusion (CMID), and accordingly propose CMINet to predict newly influenced users, given a set of seed users with different multimedia contents. In CMINet, we first introduce DiffGNN to encode the influencing power of users (nodes) and Influence-aware Optimal Transport (IOT) to align the embeddings to address the distribution shift across different diffusion channels. Then, we transform CMID into a node classification problem and propose Social-based Multimedia Feature Extractor (SMFE) and Content-aware Multi-channel Influence Propagation (CMIP) to jointly learn the user preferences on multimedia contents and predict the susceptibility of users. Furthermore, we prove that CMINet preserves monotonicity and submodularity, thus enabling (1 − 1/e)-approximate solutions for influence maximization. Experimental results manifest that CMINet outperforms eleven baselines on three public datasets.

References

[1]

Bijaya Adhikari, Yao Zhang, Aditya Bharadwaj, and B Aditya Prakash. 2017. Condensing temporal networks using propagation. In Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, 417–425.

[2]

Sinan Aral and Dylan Walker. 2012. Identifying influential and susceptible members of social networks. Science 337, 6092 (2012), 337–341.

[3]

Cigdem Aslay, Nicola Barbieri, Francesco Bonchi, and Ricardo Baeza-Yates. 2014. Online Topic-aware Influence Maximization Queries. In EDBT.

[4]

Eytan Bakshy, Itamar Rosenn, Cameron Marlow, and Lada Adamic. 2012. The role of social networks in information diffusion. In WebConf.

[5]

Nicola Barbieri, Francesco Bonchi, and Giuseppe Manco. 2013. Topic-aware social influence propagation models. Knowledge and information systems 37, 3 (2013), 555–584.

[6]

Taotao Cai, Jianxin Li, Ajmal S Mian, Timos Sellis, Jeffrey Xu Yu, 2020. Target-aware holistic influence maximization in spatial social networks. IEEE Transactions on Knowledge and Data Engineering (2020).

[7]

Qi Cao, Huawei Shen, Keting Cen, Wentao Ouyang, and Xueqi Cheng. 2017. Deephawkes: Bridging the gap between prediction and understanding of information cascades. In CKIM. 1149–1158.

Digital Library

[8]

Meeyoung Cha, Alan Mislove, and Krishna P Gummadi. 2009. A measurement-driven analysis of information propagation in the flickr social network. In WebConf.

[9]

Hsi-Wen Chen, Hong-Han Shuai, De-Nian Yang, Wang-Chien Lee, Chuan Shi, S Yu Philip, and Ming-Syan Chen. 2021. Structure-Aware Parameter-Free Group Query via Heterogeneous Information Network Transformer. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2075–2080.

[10]

Liqun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, and Jingjing Liu. 2020. Graph optimal transport for cross-domain alignment. In International Conference on Machine Learning. PMLR, 1542–1553.

[11]

Shuo Chen, Ju Fan, Guoliang Li, Jianhua Feng, Kian-lee Tan, and Jinhui Tang. 2015. Online topic-aware influence maximization. Proceedings of the VLDB Endowment (2015).

Digital Library

[12]

Wei Chen, Tian Lin, and Cheng Yang. 2016. Real-time topic-aware influence maximization using preprocessing. Computational social networks 3, 1 (2016), 1–19.

[13]

Wei Chen, Yifei Yuan, and Li Zhang. 2010. Scalable influence maximization in social networks under the linear threshold model. In ICDM.

[14]

Yi-Cheng Chen, Wen-Yuan Zhu, Wen-Chih Peng, Wang-Chien Lee, and Suh-Yin Lee. 2014. CIM: community-based influence maximization in social networks. ACM Transactions on Intelligent Systems and Technology (TIST) 5, 2 (2014), 1–31.

Digital Library

[15]

Nicolas Courty, Rémi Flamary, Amaury Habrard, and Alain Rakotomamonjy. 2017. Joint distribution optimal transportation for domain adaptation. In NeurIPS.

[16]

John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization.Journal of machine learning research 12, 7 (2011).

[17]

Shanshan Feng, Gao Cong, Arijit Khan, Xiucheng Li, Yong Liu, and Yeow Meng Chee. 2018. Inf2vec: Latent representation model for social influence embedding. In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 941–952.

[18]

Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 249–256.

[19]

Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS.

[20]

David Kempe, Jon Kleinberg, and Éva Tardos. 2003. Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. 137–146.

Digital Library

[21]

Jin-Hwa Kim, Jaehyun Jun, and Byoung-Tak Zhang. 2018. Bilinear attention networks. Advances in Neural Information Processing Systems 31 (2018).

[22]

Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.

[23]

Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann. 2018. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In International Conference on Learning Representations.

[24]

Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann. 2019. Diffusion improves graph learning. In NeurIPS.

[25]

Philip A Knight. 2008. The Sinkhorn–Knopp algorithm: convergence and applications. SIAM J. Matrix Anal. Appl. (2008).

[26]

Jihoon Ko, Kyuhan Lee, Kijung Shin, and Noseong Park. 2020. MONSTOR: an inductive approach for estimating and maximizing influence over unseen networks. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 204–211.

Digital Library

[27]

Hsu-Chao Lai, Jui-Yi Tsai, Hong-Han Shuai, Jiun-Long Huang, Wang-Chien Lee, and De-Nian Yang. 2020. Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 665–674.

Digital Library

[28]

Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In International conference on machine learning. PMLR, 1188–1196.

[29]

Cheng Li, Jiaqi Ma, Xiaoxiao Guo, and Qiaozhu Mei. 2017. Deepcas: An end-to-end predictor of information cascades. In WebConf.

Digital Library

[30]

Jianxin Li, Taotao Cai, Ajmal Mian, Rong-Hua Li, Timos Sellis, and Jeffrey Xu Yu. 2018. Holistic influence maximization for targeted advertisements in spatial social networks. In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 1340–1343.

[31]

Yuchen Li, Ju Fan, George Ovchinnikov, and Panagiotis Karras. 2019. Maximizing multifaceted network influence. In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 446–457.

[32]

Petro Liashchynskyi and Pavlo Liashchynskyi. 2019. Grid search, random search, genetic algorithm: A big comparison for NAS. arXiv preprint arXiv:1912.06059 (2019).

[33]

Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3431–3440.

[34]

Mayank Meghawat, Satyendra Yadav, Debanjan Mahata, Yifang Yin, Rajiv Ratn Shah, and Roger Zimmermann. 2018. A multimodal approach to predict social media popularity. In 2018 IEEE conference on multimedia information processing and retrieval (MIPR). IEEE, 190–195.

[35]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 701–710.

Digital Library

[36]

Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. 2018. Deepinf: Social influence prediction with deep learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2110–2119.

Digital Library

[37]

Ajitesh Srivastava, Charalampos Chelmis, and Viktor K Prasanna. 2015. Social influence computation and maximization in signed networks with competing cascades. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. 41–48.

Digital Library

[38]

Fangshuang Tang, Qi Liu, Hengshu Zhu, Enhong Chen, and Feida Zhu. 2014. Diversified social influence maximization. In IEEE ASONAM.

[39]

Shan Tian, Songsong Mo, Liwei Wang, and Zhiyong Peng. 2020. Deep reinforcement learning-based approach to tackle topic-aware influence maximization. Data Science and Engineering 5, 1 (2020), 1–11.

[40]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).

[41]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations.

[42]

Hao Wang, Cheng Yang, and Chuan Shi. 2021. Neural Information Diffusion Prediction with Topic-Aware Attention Network. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1899–1908.

Digital Library

[43]

Jianshu Weng, Ee-Peng Lim, Jing Jiang, and Qi He. 2010. Twitterrank: finding topic-sensitive influential twitterers. In Proceedings of the third ACM international conference on Web search and data mining. 261–270.

Digital Library

[44]

Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Qiushi Huang, Jintao Li, and Tao Mei. 2017. Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks. In IJCAI.

[45]

Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International conference on machine learning. PMLR, 6861–6871.

[46]

Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, and Guihai Chen. 2019. Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In WebConf. 2091–2102.

[47]

Xudong Wu, Luoyi Fu, Shuaiqi Wang, Bo Jiang, Xinbing Wang, and Guihai Chen. 2021. Collective Influence Maximization in Mobile Social Networks. IEEE Transactions on Mobile Computing (2021).

[48]

Wenwen Xia, Yuchen Li, Jun Wu, and Shenghong Li. 2021. DeepIS: Susceptibility Estimation on Social Networks. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 761–769.

Digital Library

[49]

Yu Xie, Yuanqiao Zhang, Maoguo Gong, Zedong Tang, and Chao Han. 2020. Mgat: Multi-view graph attention networks. Neural Networks 132 (2020), 180–189.

[50]

Bingbing Xu, Junjie Huang, Liang Hou, Huawei Shen, Jinhua Gao, and Xueqi Cheng. 2020. Label-consistency based graph neural networks for semi-supervised node classification. In ACM SIGIR.

[51]

Chuan Zhou, Peng Zhang, Wenyu Zang, and Li Guo. 2015. On the upper bounds of spread for greedy algorithms in social network influence maximization. IEEE Transactions on Knowledge and Data Engineering 27, 10 (2015), 2770–2783.

Digital Library

[52]

Honglei Zhuang, Yihan Sun, Jie Tang, Jialin Zhang, and Xiaoming Sun. 2013. Influence maximization in dynamic social networks. In 2013 IEEE 13th International Conference on Data Mining. IEEE, 1313–1318.

Cited By

Li HXia CWang TLin WJiang CChen CZhao Y(2024)Multi-Signal Fusion of Social Diffusion Graph with Bi-Directional Semantic ConsistencyICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448010(5450-5454)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448010
Li YGao HGao YGuo JWu W(2023)A Survey on Influence Maximization: From an ML-Based Combinatorial OptimizationACM Transactions on Knowledge Discovery from Data10.1145/360455917:9(1-50)Online publication date: 18-Jul-2023
https://dl.acm.org/doi/10.1145/3604559

Index Terms

CMINet: a Graph Learning Framework for Content-aware Multi-channel Influence Diffusion
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks

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cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 ACM.

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Published: 30 April 2023

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WWW '23: The ACM Web Conference 2023

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Cited By

Li HXia CWang TLin WJiang CChen CZhao Y(2024)Multi-Signal Fusion of Social Diffusion Graph with Bi-Directional Semantic ConsistencyICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448010(5450-5454)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448010
Li YGao HGao YGuo JWu W(2023)A Survey on Influence Maximization: From an ML-Based Combinatorial OptimizationACM Transactions on Knowledge Discovery from Data10.1145/360455917:9(1-50)Online publication date: 18-Jul-2023
https://dl.acm.org/doi/10.1145/3604559

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