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README

Influence Maximization and Learning papers

Awesome

*Image from Ding Zhu-Du

A list of influence maximization and influence learning papers, organized based on the type of data they rely on, their aim and their constraints:

  • Static network
  • Time constraint
  • Location constraint
  • Topic constraint
  • Competitive
  • Dynamic network
  • Ground-truth cascades
  • Adaptive
  • Influence learning
  • Learning and Maximization
  • Surveys

Static network

  • Mining the network value of customers

    • Domingos, Pedro and Richardson, Matt
    • KDD 2001 [Paper]
  • Maximizing the spread of influence through a social network

    • Kempe, David and Kleinberg, Jon and Tardos, Eva
    • KDD 2003 [Paper]
  • Influential nodes in a diffusion model for social networks

    • Kempe, David and Kleinberg, Jon and Tardos, Eva
    • ICALP2005 [Paper]
  • On the submodularity of influence in social networks

    • Mossel, Elchanan and Roch, Sebastien
    • Theory of computing 2007 [Paper]
  • Cost-effective outbreak detection in networks

    • Leskovec, Jure and Krause, Andreas and Guestrin, Carlos and Faloutsos, Christos and VanBriesen, Jeanne and Glance, Natalie
    • KDD 2008 [Paper]
  • Efficient influence maximization in social networks

    • Chen, Wei and Wang, Yajun and Yang, Siyu
    • KDD 2009 [Paper]
  • Scalable influence maximization for prevalent viral marketing in large-scale social networks

    • Chen, Wei and Wang, Chi and Wang, Yajun
    • KDD 2010 [Paper]
  • Simpath: An efficient Algorithm for Influence Maximization under the Linear Threshold model

    • Goyal, Amit and Lu, Wei and Lakshmanan, Laks VS
    • ICDM 2011 [Paper]
  • ** Simulated annealing based influence maximization in social networks **

    • Jiang, Qingye, Guojie Song, Cong Gao, Yu Wang, Wenjun Si, and Kunqing Xie
    • AAAI 2011.
  • Irie: Scalable and robust influence maximization in social networks

    • Jung, Kyomin, Wooram Heo, and Wei Chen
    • ICDM 2012
  • Scalable and parallelizable processing of influence maximization for large-scale social networks?

    • Kim, Jinha and Kim, Seung-Keol and Yu, Hwanjo
    • ICDE 2013
  • Staticgreedy: solving the scalability-accuracy dilemma in influence maximization

    • Cheng, Suqi and Shen, Huawei and Huang, Junming and Zhang, Guoqing and Cheng, Xueqi
    • CIKM 2013 [Paper]
  • On budgeted influence maximization in social networks

    • Nguyen, Huy, and Rong Zheng
    • IEEE Journal on Selected Areas in Communications 2013
  • Latency-Bounded Target Set Selection in Social Networks

    • Ferdinando, Cicalese, Gennaro Cordasco, Gargano Luisa, Milanic Martin, and Vaccaro Ugo
    • Theoretical Computer Science 2013
  • Fast and accurate influence maximization on large networks with pruned monte-carlo simulations

    • Ohsaka, Naoto and Akiba, Takuya and Yoshida, Yuichi and Kawarabayashi, Ken-ichi
    • AAAI 2014 [Paper]
  • Maximizing social influence in nearly optimal time

    • Borgs, Christian and Brautbar, Michael and Chayes, Jennifer and Lucier, Brendan
    • SODA 2014 [Paper]
  • IMRank: influence maximization via finding self-consistent ranking

    • Cheng, Suqi and Shen, Huawei and Huang, Junming and Chen, Wei and Cheng, Xueqi
    • SIGIR 2014 [Paper]
  • Sketch-based Influence Maximization and Computation: Scaling up with Guarantees

    • Cohen, Edith and Delling, Daniel and Pajor, Thomas and Werneck, Renato F
    • CIKM 2014 [Paper]
  • Influence maximization: Near-optimal time complexity meets practical efficiency

    • Tang, Youze and Xiao, Xiaokui and Shi, Yanchen
    • SIGMOD 2014 [Paper]
  • Fast and accurate influence maximization on large networks with pruned monte-carlo simulations

    • Ohsaka, Naoto and Akiba, Takuya and Yoshida, Yuichi and Kawarabayashi, Ken-ichi
    • AAAI 2014 [Paper]
  • Influence maximization in near-linear time: A martingale approach

    • Tang, Youze and Shi, Yanchen and Xiao, Xiaokui
    • SIGMOD 2015
  • A genetic newgreedy algorithm for influence maximization in social network

    • Tsai, Chun-Wei, Yo-Chung Yang, and Ming-Chao Chiang
    • ICS 2015
  • Asim: A scalable algorithm for influence maximization under the independent cascade model

    • Galhotra, Sainyam, Akhil Arora, Srinivas Virinchi, and Shourya Roy
    • The WebConf 2015 [Paper]
  • On the upper bounds of spread for greedy algorithms in social network influence maximization

    • Zhou, Chuan and Zhang, Peng and Zang, Wenyu and Guo, Li
    • TKDE 2015 [Paper]
  • Better approximation algorithms for influence maximization in online social networks

    • Zhu, Yuqing, Weili Wu, Yuanjun Bi, Lidong Wu, Yiwei Jiang, and Wen Xu
    • Journal of Combinatorial Optimization 2015
  • A fast and effective heuristic for discovering small target sets in social networks

    • Cordasco, Gennaro, Luisa Gargano, Marco Mecchia, Adele A. Rescigno, and Ugo Vaccaro
    • Combinatorial Optimization and Applications 2015
  • A fast algorithm for finding most influential people based on the linear threshold model

    • Rahimkhani, Khadije, Abolfazl Aleahmad, Maseud Rahgozar, and Ali Moeini
    • Expert Systems with Applications 2015
  • Influence maximization in social networks with genetic algorithms

    • Bucur, Doina, and Giovanni Iacca
    • European conference on the applications of evolutionary computation 2016 [Paper]
  • Stop-and-stare: Optimal sampling algorithms for viral marketing in billion-scale networks

    • Nguyen, Hung T and Thai, My T and Dinh, Thang N
    • SIGMOD 2016 [Paper]
  • Influence Maximization in Social Networks Based on Discrete Particle Swarm Optimization

    • Gonga, Maoguo, Jianan Yana, Bo Shena, Lijia Maa, and Qing Caia. "." (2016).
    • Information Sciences 2016 [Paper]
  • Holistic influence maximization: Combining scalability and efficiency with opinion-aware models

    • Galhotra, Sainyam, Akhil Arora, and Shourya Roy
    • SIGMOD 2016
  • Holistic influence maximization: Combining scalability and efficiency with opinion-aware models

    • Galhotra, Sainyam and Arora, Akhil and Roy, Shourya
    • SIGMOD 2016 [Paper]
  • INCIM: A community-based algorithm for influence maximization problem under the linear threshold model

    • Bozorgi, Arastoo, Hassan Haghighi, Mohammad Sadegh Zahedi, and Mojtaba Rezvani.
    • Information Processing & Management 2016
  • Diffusion centrality: a paradigm to maximize spread in social networks

    • Kang, Chanhyun, Sarit Kraus, Cristian Molinaro, Francesca Spezzano, and V. S. Subrahmanian
    • Artificial Intelligence 2016
  • Revisiting the stop-and-stare algorithms for influence maximization

    • Huang, Keke and Wang, Sibo and Bevilacqua, Glenn and Xiao, Xiaokui and Lakshmanan, Laks VS
    • VLDB 2017 [Paper]
  • Social influence spectrum at scale: near-optimal solutions for multiple budgets at once

    • Nguyen HT, Ghosh P, Mayo ML, Dinh TN
    • ACM Transactions on Information Systems 2017
  • Leveraging cross-network information for graph sparsification in influence maximization

    • Shen, Xiao, Fu-lai Chung, and Sitong Mao
    • SIGIR 2017
  • A billion-scale approximation algorithm for maximizing benefit in viral marketing

    • Nguyen, Hung T., My T. Thai, and Thang N. Dinh.
    • IEEE/ACM Transactions on Networking 2017
  • On the approximability of influence in social networks

    • Chen Ning
    • SIAM J Discrete Math 2017
  • Optimizing influence diffusion in a social network with fuzzy costs for targeting nodes

    • Ni, Yaodong and Shi, Qiaoni and Wei, Zhiyuan
    • Journal of Ambient Intelligence and Humanized Computing 2017
  • MATI: An efficient algorithm for influence maximization in social networks

    • Rossi, Maria-Evgenia G and Shi, Bowen and Tziortziotis, Nikolaos and Malliaros, Fragkiskos D and Giatsidis, Christos and Vazirgiannis, Michalis
    • PloS one 2018
  • An Inapproximability Result for the Target Set Selection Problem on Bipartite Graphs.

    • Banerjee, Suman, and Rogers Mathew
    • arXiv 2018 [Paper]
  • An efficient and effective hop-based approach for influence maximization in social networks

    • Tang, Jing, Xueyan Tang, and Junsong Yuan
    • ASONAM 2018
  • Community-based seeds selection algorithm for location aware influence maximization

    • Li, Xiao, Xiang Cheng, Sen Su, and Chenna Sun
    • Neurocomputing 2018
  • Maximizing positive influence spread in online social networks via fluid dynamics

    • Wang, Feng, Wenjun Jiang, Xiaolin Li, and Guojun Wang
    • Future Generation Computer Systems 2018
  • Scalable Lattice Influence Maximization

    • Wu, Ruihan, Zheng Yu, and Wei Chen
    • arXiv 2018 [Paper]
  • Influence Maximization in Signed Social Networks with Opinion Formation

    • Liang, Wenxin and Shen, Chengguang and Li, Xiao and Nishide, Ryo and Piumarta, Ian and Takada, Hideyuki
    • IEEE Access 2019 [Paper]
  • LAPSO-IM: A learning-based influence maximization approach for social networks

    • Singh, Shashank Sheshar and Kumar, Ajay and Singh, Kuldeep and Biswas, Bhaskar
    • Applied Soft Computing 2019
  • Self-Activation Influence Maximization

    • Sun, Lichao and Chen, Albert and Yu, Philip S and Chen, Wei
    • arXiv 2019 [Paper]
  • A Centrality Measure for Influence Maximization Across Multiple Social Networks

    • Singh, Shashank Sheshar, Ajay Kumar, Shivansh Mishra, Kuldeep Singh, and Bhaskar Biswas
    • Advanced Informatics for Computing Research 2019 [Paper]
  • Accelerating influence maximization using heterogeneous algorithms

    • Haque, Mridul, and Dip Sankar Banerjee
    • The Journal of Supercomputing 2019 [Paper]
  • Efficient Influence Maximization Under Network Uncertainty

    • Eshghi, Soheil, Setareh Maghsudi, Valerio Restocchi, Sebastian Stein, and Leandros Tassiulas
    • INFOCOM 2019 [Paper]
  • Influence Maximization on Large-Scale Networks with a Group-Based Method via Network Embedding

    • Ji, Yaoxuan, Li Pan, and Peng Wu
    • DSC 2019
  • A Multi-criteria Approximation Algorithm for Influence Maximization with Probabilistic Guarantees

    • Khan, Maleq, Gopal Pandurangan, Nguyen Dinh Pham, Anil Vullikanti, and Qin Zhang
    • SIAM ALENEX 2019
  • "Robust Influence Maximization for Hyperparametric Models

    • Kalimeris, Dimitris, Gal Kaplun, and Yaron Singer
    • ICML 2019 [Paper]
  • An Exact Algorithm for Robust Influence Maximization

    • Nannicini, Giacomo, Giorgio Sartor, Emiliano Traversi, and Roberto Wolfler-Calvo
    • Integer Programming and Combinatorial Optimization 2019
  • Tiptop:(almost) exact solutions for influence maximization in billion-scale networks

    • Li, Xiang, J. David Smith, Thang N. Dinh, and My T. Thai.
    • Transactions on Networking 2019 [Paper]
  • Group-fairness in influence maximization

    • Tsang, Alan, Bryan Wilder, Eric Rice, Milind Tambe, and Yair Zick
    • arXiv 2019[Paper]
  • Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks

    • Khajehnejad, Moein, Ahmad Asgharian Rezaei, Mahmoudreza Babaei, Jessica Hoffmann, Mahdi Jalili, and Adrian Weller
    • IJCAI 2020[Paper]
  • Sample Complexity Bounds for Influence Maximization

    • Sadeh, Gal, Edith Cohen, and Haim Kaplan
    • ITCS 2020 [Paper]
  • Sample Complexity Bounds for Influence Maximization

    • Sadeh, Gal, Edith Cohen, and Haim Kaplan
    • ITCS 2020 [Paper]
  • Earned Benefit Maximization in Social Networks Under Budget Constraint

    • Banerjee, Suman, Mamata Jenamani, and Dilip Kumar Pratihar
    • arXiv 2020 [Paper]
  • Continuous Influence Maximization

    • Yang, Y., Mao, X., Pei, J., & He, X.
    • TKDD 2020 [Paper]
  • Targeted Influence Maximization Based on Cloud Computing Over Big Data in Social Networks

    • Chen, Shiyu, Xiaochun Yin, Qi Cao, Qianmu Li, and Huaqiu Long
    • IEEE Access 2020 [Paper]
  • Influence maximization through user interaction modeling

    • Oriedi, David, Cyril de Runz, Zahia Guessoum, Amine Aït Younes, and Henry Nyongesa
    • ACM SAC 2020
  • A Unifying Framework for Fairness-Aware Influence Maximization

    • Farnad, Golnoosh, Behrouz Babaki, and Michel Gendreau
    • The WebConf 2020 [Paper]
  • The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches

    • Ohsaka, Naoto
    • SIGMOD 2020 [Paper]
  • Cores matter? An analysis of graph decomposition effects on influence maximization problems

    • Caliò, Antonio, Andrea Tagarelli, and Francesco Bonch
    • Web Science 2020
  • CutTheTail: An Accurate and Space-Efficient Heuristic Algorithm for Influence Maximization

    • Popova, Diana, Ken-ichi Kawarabayashi, and Alex Thomo
    • The Computer Journal 2020
  • A Unifying Framework for Fairness-Aware Influence Maximization

    • Farnad, Golnoosh, Behrouz Babaki, and Michel Gendreau
    • Web Science 2020[Paper]
  • Improving Fairness of Influence Maximization in Social Networks Using Grey Wolf Optimizer

    • Razzaghi, Behnam, and Mehdy Roayaei Ardakany
    • JEWR 2020 [Paper]
  • Influence Maximization with Priority in Online Social Networks

    • Pham, Canh V., Dung KT Ha, Quang C. Vu, Anh N. Su, and Huan X. Hoang
    • Algorithms 2020 [Paper]
  • Geodemographic Influence Maximization

    • Zhang, Kaichen, Jingbo Zhou, Donglai Tao, Panagiotis Karras, Qing Li, and Hui Xiong
    • KDD 2020 [Paper]
  • Taxonomy of Influence Maximization Techniques in Unknown Social Networks

    • Ahamed, B. Bazeer, and Sudhakaran Periakaruppan
    • In Research Advancements in Smart Technology, Optimization, and Renewable Energy (pp. 351-363) 2020

Time constraint

  • Time-critical influence maximization in social networks with time-delayed diffusion process

    • Chen, Wei and Lu, Wei and Zhang, Ning
    • AAAI 2012 [Paper]
  • Time constrained influence maximization in social networks

    • Liu, Bo and Cong, Gao and Xu, Dong and Zeng, Yifeng
    • ICDM 2012 [Paper]
  • Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships

    • Li, Yanhua and Chen, Wei and Wang, Yajun and Zhang, Zhi-Li
    • WSDM 2013 [Paper]
  • Influence spreading path and its application to the time constrained social influence maximization problem and beyond

    • Liu, Bo and Cong, Gao and Zeng, Yifeng and Xu, Dong and Chee, Yeow Meng
    • TKDE 2013 [Paper]
  • Maximizing rumor containment in social networks with constrained time

    • Fan, Lidan and Wu, Weili and Zhai, Xuming and Xing, Kai and Lee, Wonjun and Du, Ding-Zhu
    • ASONAM 2014 [Paper]
  • Influence spreading path and its application to the time constrained social influence maximization problem and beyond

    • Liu, Bo and Cong, Gao and Zeng, Yifeng and Xu, Dong and Chee, Yeow Meng
    • TKDE 2014 [Paper]
  • Cost-effective viral marketing for time-critical campaigns in large-scale social networks

    • Dinh, Thang N and Zhang, Huiyuan and Nguyen, Dzung T and Thai, My T
    • Transactions on Networking 2014 [Paper]
  • Influence maximization with novelty decay in social networks

    • Feng, Shanshan and Chen, Xuefeng and Cong, Gao and Zeng, Yifeng and Chee, Yeow Meng and Xiang, Yanping
    • AAAI 2014 [Paper]
  • Time-sensitive influence maximization in social networks

    • Mohammadi, Azadeh and Saraee, Mohamad and Mirzaei, Abdolreza
    • Journal of Information Science 2015 [Paper]
  • DynaDiffuse: A Dynamic Diffusion Model for Continuous Time Constrained Influence Maximization

    • Xie, Miao and Yang, Qiusong and Wang, Qing and Cong, Gao and De Melo, Gerard
    • AAAI 2015 [Paper]
  • Credit distribution for influence maximization in online social networks with time constraint

    • Pan, Yan and Deng, Xiaoheng and Shen, Hailan
    • Smartcity 2015
  • Towards time-discounted influence maximization

  • Maximizing time-decaying influence in social networks

    • Ohsaka, Naoto and Yamaguchi, Yutaro and Kakimura, Naonori and Kawarabayashi, Ken-Ichi
    • ECML/PKDD 2016 [Paper]
  • Time-sensitive influence maximization in social networks

    • Min Hu, Qin Liu, Hejiao Huang, Xiaohua Jia
    • ICCT 2018 [Paper]
  • Influence Maximization: A Time-Space Efficient Algorithm

    • Xia, Ganming
    • Materials Science and Engineering 2019 [Paper]
  • On the Fairness of Time-Critical Influence Maximization in Social Networks

    • Ali, Junaid and Babaei, Mahmoudreza and Chakraborty, Abhijnan and Mirzasoleiman, Baharan and Gummadi, Krishna P and Singla, Adish
    • arXiv 2019 [Paper]
  • Influence maximization on undirected graphs: Towards closing the (1-1/e) gap

    • Schoenebeck, Grant, and Biaoshuai Tao
    • CEC 2019 [Paper]

Location constraint

  • Efficient location-aware influence maximization

    • Li, Guoliang and Chen, Shuo and Feng, Jianhua and Tan, Kian-lee and Li, Wen-syan
    • SIGMOD 2014 [Paper]
  • Location-based influence maximization in social networks

    • Zhou, Tao and Cao, Jiuxin and Liu, Bo and Xu, Shuai and Zhu, Ziqing and Luo, Junzhou
    • CIKM 2015 [Paper]
  • Influence maximization in trajectory databases

    • Guo, Long and Zhang, Dongxiang and Cong, Gao and Wu, Wei and Tan, Kian-Lee
    • TKDE 2016 [Paper]
  • Distance-aware influence maximization in geo-social network

    • Wang, Xiaoyang and Zhang, Ying and Zhang, Wenjie and Lin, Xuemin
    • ICDE 2016
  • Efficient distance-aware influence maximization in geo-social networks

    • Wang, Xiaoyang and Zhang, Ying and Zhang, Wenjie and Lin, Xuemin
    • TKDE 2016
  • Geo-social influence spanning maximization

    • Li, Jianxin and Sellis, Timos and Culpepper, J Shane and He, Zhenying and Liu, Chengfei and Wang, Junhu
    • TKDE 2017
  • Location-aware targeted influence maximization in social networks

    • Su, Sen and Li, Xiao and Cheng, Xiang and Sun, Chenna
    • Association for Information Science and Technology 2018
  • Topology-driven diversity for targeted influence maximization with application to user engagement in social networks

    • Caliò, Antonio, Roberto Interdonato, Chiara Pulice, and Andrea Tagarelli
    • TKDE 2018 [Paper]
  • Location-aware influence maximization over dynamic social streams

    • Wang, Yanhao and Li, Yuchen and Fan, Ju and Tan, Kian-Lee
    • TOIS 2018 [Paper]
  • Multi-location Influence Maximization in Location-Based Social Networks

    • Zhang, Zhen and Zhao, Xiangguo and Wang, Guoren and Bi, Xin
    • APWeb 2018

Topic constraint

  • Topic-aware social influence propagation models

    • Barbieri, Nicola and Bonchi, Francesco and Manco, Giuseppe
    • Knowledge and information systems 2013
  • Social influence analysis in large-scale networks

    • Tang, Jie and Sun, Jimeng and Wang, Chi and Yang, Zi
    • KDD 2019 [Paper]
  • Online Topic-aware Influence Maximization Queries

    • Aslay, Cigdem and Barbieri, Nicola and Bonchi, Francesco and Baeza-Yates, Ricardo A
    • EDBT 2014 [Paper]
  • Scalable topic-specific influence analysis on microblogs

    • Bi, Bin and Tian, Yuanyuan and Sismanis, Yannis and Balmin, Andrey and Cho, Junghoo
    • WSDM 2014 [Paper]
  • Efficient topic-aware influence maximization using preprocessing

    • Chen, Wei and Lin, Tian and Yang, Cheng
    • Corr 2014 [Paper]
  • Online topic-aware influence maximization

    • Chen, Shuo and Fan, Ju and Li, Guoliang and Feng, Jianhua and Tan, Kian-lee and Tang, Jinhui
    • VLDB 2015 [Paper]
  • Microblogging content propagation modeling using topic-specific behavioral factors

    • Hoang, Tuan-Anh and Lim, Ee-Peng
    • TKDE 2016 [Paper]
  • Real-time topic-aware influence maximization using preprocessing

    • Chen, Wei and Lin, Tian and Yang, Cheng
    • Computational Social Networks 2016
  • Octopus: An online topic-aware influence analysis system for social networks

    • Fan, Ju and Qiu, Jiarong and Li, Yuchen and Meng, Qingfei and Zhang, Dongxiang and Li, Guoliang and Tan, Kian-Lee and Du, Xiaoyong
    • ICDE 2018 [Paper]

Competitive

  • Competitive influence maximization in social networks

    • Bharathi, Shishir and Kempe, David and Salek, Mahyar
    • Workshop on web and internet economics 2007 [Paper]
  • Threshold models for competitive influence in social networks

    • Borodin, Allan and Filmus, Yuval and Oren, Joel
    • Workshop on Internet and Network Economics 2010 [Paper]
  • Diffusion in social networks with competing products

    • Apt, Krzysztof R and Markakis, Evangelos
    • Symposium on Algorithmic Game Theory 2011 [Paper]
  • Influence blocking maximization in social networks under the competitive linear threshold model

    • He, Xinran and Song, Guojie and Chen, Wei and Jiang, Qingye
    • SDM 2012 [Paper]
  • A game-theoretic analysis of a competitive diffusion process over social networks

    • Tzoumas, Vasileios and Amanatidis, Christos and Markakis, Evangelos
    • Workshop on Internet and Network Economics 2012 [Paper]
  • Clash of the contagions: Cooperation and competition in information diffusion

    • Myers, Seth A and Leskovec, Jure
    • ICDM 2012 [Paper]
  • Game theoretic analysis of a strategic model of competitive contagion and product adoption in social networks

    • Fazeli, Arastoo and Jadbabaie, Ali
    • Decision and Control 2012 [Paper]
  • The bang for the buck: fair competitive viral marketing from the host perspective

    • Lu, Wei and Bonchi, Francesco and Goyal, Amit and Lakshmanan, Laks VS
    • KDD 2013 [Paper]
  • Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships

    • Li, Yanhua and Chen, Wei and Wang, Yajun and Zhang, Zhi-Li
    • WSDM 2013 [Paper]
  • Competitive contagion in networks

    • Goyal, Sanjeev and Heidari, Hoda and Kearns, Michael
    • Games and Economic Behavior 2014 [Paper]
  • Influence maximization in switching-selection threshold models

    • Fotakis, Dimitris and Lykouris, Thodoris and Markakis, Evangelos and Obraztsova, Svetlana
    • Symposium on Algorithmic Game Theory 2014 [Paper]
  • Strategic resource allocation for competitive influence in social networks

    • Masucci, Antonia Maria and Silva, Alonso
    • Allerton 2014 [Paper]
  • Social networks with competing products

    • Apt, Krzysztof R and Markakis, Evangelos
    • Fundamenta Informaticae 2014 [Paper]
  • New models for competitive contagion

    • Draief, Moez and Heidari, Hoda and Kearns, Michael
    • AAAI 2014 [Paper]
  • From competition to complementarity: comparative influence diffusion and maximization

    • Lu, Wei and Chen, Wei and Lakshmanan, Laks VS
    • VLDB 2015 [Paper]
  • Containment of competitive influence spread in social networks

    • Liu, Weiyi and Yue, Kun and Wu, Hong and Li, Jin and Liu, Donghua and Tang, Duanping
    • Knowledge-Based Systems 2016
  • Competitive propagation: Models, asymptotic behavior and quality-seeding games

    • Mei, Wenjun and Bullo, Francesco
    • IEEE Transactions on Network Science and Engineering 2017 [Paper]
  • Competitive diffusion in social networks: Quality or seeding?

    • Fazeli, Arastoo and Ajorlou, Amir and Jadbabaie, Ali
    • Transactions on Control of Network Systems 2016 [Paper]
  • Competitive rumor spread in social networks

    • Lim, Yongwhan and Ozdaglar, Asuman and Teytelboym, Alexander
    • SIGMETRICS 2017 [Paper]
  • Community-based influence maximization in social networks under a competitive linear threshold model

    • Bozorgi, Arastoo, Saeed Samet, Johan Kwisthout, and Todd Wareham
    • Knowledge-Based Systems 2017
  • Dominated competitive influence maximization with time-critical and time-delayed diffusion in social networks

    • Li, Huijuan and Pan, Li and Wu, Peng
    • Journal of computational science 2018
  • A game-theoretic approach for modeling competitive diffusion over social networks

    • Jafari, Shahla and Navidi, Hamidreza
    • Games 2018 [Paper]
  • IDR: Positive Influence Maximization and Negative Influence Minimization Under Competitive Linear Threshold Model

    • Chi-Lung Lee, Cheng-En Sung, Hao-Shang Ma, and Jen-Wei Huang
    • IEEE MDM 2019 [Paper]
  • Influence Minimization Algorithm Based on Coordination Game

    • Yi Yang, Ming He, Bo Zhou, and Chi Zhang
    • IEEE Access 7 2019 [Paper]
  • Adversarial Influence Maximization

    • Khim, Justin, Varun Jog, and Po-Ling Loh
    • ISIT 2019 [Paper]
  • Recurrent Neural Variational Model for Follower-based Influence Maximization

    • Huang, Huimin, Zaiqiao Meng, and Shangsong Liang
    • Information Sciences 2020
  • Real-Time Influence Maximization in a RTB Setting

    • Dupuis, David, Cédric du Mouza, Nicolas Travers, and Gaël Chareyron
    • Data Science and Engineering 2020
  • Balancing spreads of influence in a social network

    • Becker, Ruben, Federico Coro, Gianlorenzo D'Angelo, and Hugo Gilbert
    • AAAI 2020

Dynamic network

  • On influential node discovery in dynamic social networks

    • Aggarwal, Charu C and Lin, Shuyang and Yu, Philip S
    • SDM 2012 [Paper]
  • Influence maximization in dynamic social networks

    • Zhuang, Honglei and Sun, Yihan and Tang, Jie and Zhang, Jialin and Sun, Xiaoming
    • ICDM 2013
  • On influential nodes tracking in dynamic social networks

    • Chen, Xiaodong and Song, Guojie and He, Xinran and Xie, Kunqing
    • SDM 2015
  • Diffusion maximization in evolving social networks

    • Gayraud, Nathalie TH and Pitoura, Evaggelia and Tsaparas, Panayiotis
    • Online Social Networks [Paper]
  • Dynamic influence analysis in evolving networks

    • Ohsaka, Naoto and Akiba, Takuya and Yoshida, Yuichi and Kawarabayashi, Ken-ichi
    • VLDB 2016 [Paper]
  • Incremental influence maximization for dynamic social networks

    • Wang, Yake and Zhu, Jinghua and Ming, Qian
    • International Conference of Pioneering Computer Scientists, Engineers and Educators 2017
  • Tracking influential individuals in dynamic networks

    • Yang, Yu and Wang, Zhefeng and Pei, Jian and Chen, Enhong
    • TKDE 2017 [Paper]
  • Real-time influence maximization on dynamic social streams

    • Wang, Yanhao and Fan, Qi and Li, Yuchen and Tan, Kian-Lee
    • VLDB 2017 [Paper]
  • Fast influence maximization in dynamic graphs: A local updating approach

    • Yalavarthi, Vijaya Krishna and Khan, Arijit
    • arXiv 2018 [Paper]
  • RTIM: A Real-Time Influence Maximization Strategy

    • Dupuis, David, Cédric du Mouza, Nicolas Travers, and Gaël Chareyron
    • International Conference on Web Information Systems Engineering 2019 [Paper]

Ground-truth cascades

  • A Data-Based Approach to Social Influence Maximization

    • Amit Goyal, Francesco Bonchi, Laks V. S. Lakshmanan
    • VLDB 2011 [Paper]
  • Two evidential data based models for influence maximization in Twitter

    • Jendoubi, Siwar and Martin, Arnaud and Li{'e}tard, Ludovic and Hadji, Hend Ben and Yaghlane, Boutheina Ben
    • Knowledge-Based Systems 2017 [Paper]
  • DiffuGreedy: An Influence Maximization Algorithm Based on Diffusion Cascades

    • Panagopoulos, George and Malliaros, Fragkiskos D and Vazirgiannis, Michalis
    • Complex Networks and their Applications 2018 [Paper]
  • Identifying topical influencers on twitter based on user behavior and network topology

    • Alp, Zeynep Zengin and ŞG Öğüdücü
    • Knowledge-Based Systems 2018
  • Influence maximization by leveraging the crowdsensing data in information diffusion network

    • Feng Wang, Wenjun Jiang, Guojun Wang, Song Guo
    • Network and Computer Applications 2019
  • Multi-task Learning for Influence Estimation and Maximization

    • Panagopoulos, George and Vazirgiannis, Michalis and Malliaros, Fragkiskos D
    • arXiv 2019 [Paper]
  • Influence Maximization Using Influence and Susceptibility Embeddings

    • Panagopoulos, George, Fragkiskos D. Malliaros, and Michalis Vazirgianis
    • ICWSM 2020 [Paper]

Adaptive

  • Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization

    • Golovin, Daniel and Krause, Andreas
    • COLT 2010 [Paper]
  • Adaptive influence maximization in social networks: Why commit when you can adapt?

    • Vaswani, Sharan and Lakshmanan, Laks VS
    • arXiv 2016 [Paper]
  • No time to observe: Adaptive influence maximization with partial feedback

    • Yuan, Jing and Tang, Shaojie
    • arXiv 2016 [Paper]
  • Adaptive influence maximization in dynamic social networks

    • Tong, Guangmo and Wu, Weili and Tang, Shaojie and Du, Ding-Zhu
    • Transactions on Networking 2017 [Paper]
  • Efficient algorithms for adaptive influence maximization

    • Han, Kai and Huang, Keke and Xiao, Xiaokui and Tang, Jing and Sun, Aixin and Tang, Xueyan
    • VLDB 2018 [Paper]
  • Adaptive submodular influence maximization with myopic feedback

    • Salha, Guillaume and Tziortziotis, Nikolaos and Vazirgiannis, Michalis
    • ASONAM 2018 [Paper]
  • Adaptive Influence Maximization with Myopic Feedback

    • Peng, Binghui and Chen, Wei
    • arXiv 2019 [Paper]
  • On adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback

    • Chen, Wei, and Binghui Peng
    • arXiv 2019 [Paper]
  • Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization

    • Chen, Wei, Binghui Peng, Grant Schoenebeck, and Biaoshuai Tao
    • arxiv 2019 [Paper]
  • Efficient Approximation Algorithms for Adaptive Influence Maximization

    • Huang, Keke, Jing Tang, Kai Han, Xiaokui Xiao, Wei Chen, Aixin Sun, Xueyan Tang, and Andrew Lim
    • arXiv 2020 [Paper]
  • Time-constrained Adaptive Influence Maximization

    • Guangmo, Tong and Ruiqi, Wang and Chen, Ling and Zheng, Dong amd Xiang, Li
    • arXiv 2020 [Paper]
  • Better Bounds on the Adaptivity Gap of Influence Maximization under Full-adoption Feedback

    • D'Angelo, Gianlorenzo, Debashmita Poddar, and Cosimo Vinci
    • arXiv 2020 [Paper]

Influence learning

  • Prediction of information diffusion probabilities for independent cascade model

    • Saito, Kazumi and Nakano, Ryohei and Kimura, Masahiro
    • Knowledge-based and intelligent information and engineering systems 2008 [Paper]
  • Learning continuous-time information diffusion model for social behavioral data analysis

    • Saito, Kazumi and Kimura, Masahiro and Ohara, Kouzou and Motoda, Hiroshi
    • ACML 2009 [Paper]
  • Learning influence probabilities in social networks

    • Goyal, Amit and Bonchi, Francesco and Lakshmanan, Laks VS
    • WSDM 2010 [Paper]
  • Learning the graph of epidemic cascades

    • Netrapalli, Praneeth and Sanghavi, Sujay
    • SIGMETRICS 2012 [Paper]
  • Sparsification of influence networks

    • Mathioudakis, Michael and Bonchi, Francesco and Castillo, Carlos and Gionis, Aristides and Ukkonen, Antti
    • KDD 2011 [Paper]
  • Strip: stream learning of influence probabilities

    • Kutzkov, Konstantin and Bifet, Albert and Bonchi, Francesco and Gionis, Aristides
    • KDD 2013 [Paper]
  • Scalable influence estimation in continuous-time diffusion networks

    • Du, Nan and Song, Le and Rodriguez, Manuel Gomez and Zha, Hongyuan
    • NeurIPS 2013 [Paper]
  • Efficient topic-aware influence maximization using preprocessing

    • Chen, Wei and Lin, Tian and Yang, Cheng
    • CoRR 2014 [Paper]
  • Who influenced you? predicting retweet via social influence locality

    • Zhang, Jing and Tang, Jie and Li, Juanzi and Liu, Yang and Xing, Chunxiao
    • TKDD 2015 [Paper]
  • Seismic: A self-exciting point process model for predicting tweet popularity

    • Zhao, Qingyuan and Erdogdu, Murat A and He, Hera Y and Rajaraman, Anand and Leskovec, Jure
    • KDD 2015 [Paper]
  • Microblogging content propagation modeling using topic-specific behavioral factors

    • Hoang, Tuan-Anh and Lim, Ee-Peng
    • TKDE 2016 [Paper]
  • Recurrent marked temporal point processes: Embedding event history to vector

    • Du, Nan and Dai, Hanjun and Trivedi, Rakshit and Upadhyay, Utkarsh and Gomez-Rodriguez, Manuel and Song, Le
    • KDD 2016 [Paper]
  • Recurrent marked temporal point processes: Embedding event history to vector

    • Du, Nan and Dai, Hanjun and Trivedi, Rakshit and Upadhyay, Utkarsh and Gomez-Rodriguez, Manuel and Song, Le
    • KDD 2016 [Paper]
  • Representation learning for information diffusion through social networks: an embedded cascade model

    • Bourigault, Simon and Lamprier, Sylvain and Gallinari, Patrick
    • WSDM 2016 [Paper]
  • Continuous influence maximization: What discounts should we offer to social network users?

    • Yang, Yu, Xiangbo Mao, Jian Pei, and Xiaofei He
    • SIGMOD 2016 [Paper]
  • Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks.

    • Du, Nan and Liang, Yingyu and Balcan, Maria-Florina and Gomez-Rodriguez, Manuel and Zha, Hongyuan and Song, Le
    • JMLR 2017 [Paper]
  • Predicting information diffusion probabilities in social networks: A Bayesian networks based approach

    • Varshney, Devesh and Kumar, Sandeep and Gupta, Vineet
    • Knowledge-Based Systems 2017 [Paper]
  • Cascade dynamics modeling with attention-based recurrent neural network

    • Wang, Yongqing and Shen, Huawei and Liu, Shenghua and Gao, Jinhua and Cheng, Xueqi
    • IJCAI 2017 [Paper]
  • Cascade Dynamics Modeling with Attention-based Recurrent Neural Network

    • Wang, Yongqing and Shen, Huawei and Liu, Shenghua and Gao, Jinhua and Cheng, Xueqi
    • IJCAI 2017 [Paper]
  • Topological recurrent neural network for diffusion prediction

    • Wang, Jia and Zheng, Vincent W and Liu, Zemin and Chang, Kevin Chen-Chuan
    • ICDM 2017 [Paper]
  • DeepCas: An end-to-end predictor of information cascades

    • Li, Cheng and Ma, Jiaqi and Guo, Xiaoxiao and Mei, Qiaozhu
    • The WebConf 2017 [Paper]
  • Data-Driven Influence Learning in Social Networks

    • Wang, Feng and Jiang, Wenjun and Wang, Guojun and Xie, Dongqing
    • Symposium on Parallel and Distributed Processing with Applications (ISPA/IUCC) 2017 [Paper]
  • COSINE: community-preserving social network embedding from information diffusion cascades

    • Zhang, Yuan and Lyu, Tianshu and Zhang, Yan
    • AAAI 2018 [Paper]
  • Inf2vec: Latent Representation Model for Social Influence Embedding

    • Feng, Shanshan and Cong, Gao and Khan, Arijit and Li, Xiucheng and Liu, Yong and Chee, Yeow Meng
    • ICDE 2018 [Paper]
  • A Sequential Neural Information Diffusion Model with Structure Attention

    • Wang, Zhitao and Chen, Chengyao and Li, Wenjie
    • CIKM 2018 [Paper]
  • Learning Diffusion using Hyperparameters

    • Kalimeris, Dimitris and Singer, Yaron and Subbian, Karthik and Weinsberg, Udi
    • ICML 2018 [Paper]
  • DeepInf: Modeling influence locality in large social networks

    • Qiu, JZ and Tang, Jian and Ma, Hao and Dong, YX and Wang, KS and Tang, J
    • KDD 2018 [Paper]
  • DeepDiffuse: Predicting the 'Who' and 'When' in Cascades

    • Islam, Mohammad Raihanul and Muthiah, Sathappan and Adhikari, Bijaya and Prakash, B Aditya and Ramakrishnan, Naren
    • ICDM 2018 [Paper]
  • Neural diffusion model for microscopic cascade prediction

    • Yang, Cheng, Maosong Sun, Haoran Liu, Shiyi Han, Zhiyuan Liu, and Huanbo Luan
    • arXiv 2018 [Paper]
  • Information Cascades Modeling via Deep Multi-Task Learning

    • Chen, Xueqin, Kunpeng Zhang, Fan Zhou, Goce Trajcevski, Ting Zhong, and Fengli Zhang
    • SIGIR 2019
  • DeepInfer: Diffusion Network Inference through Representation Learning

    • Kefato, Zekarias T., Nasrullah Sheikh, and Alberto Montresor
    • MLG 2019 [Paper]
  • Information Cascades Modeling via Deep Multi-Task Learning

    • Chen, Xueqin, Kunpeng Zhang, Fan Zhou, Goce Trajcevski, Ting Zhong, and Fengli Zhang
    • SIGIR 2019
  • Learning Influence Probabilities and Modelling Influence Diffusion in Twitter

    • Zhang, Zizhu and Zhao, Weiliang and Yang, Jian and Paris, Cecile and Nepal, Surya
    • TheWebConf 2019 [Paper]
  • Information Diffusion Prediction with Network Regularized Role-based User Representation Learning

    • Wang, Zhitao and Chen, Chengyao and Li, Wenjie
    • TKDD 2019 [Paper]
  • Hierarchical diffusion attention network

    • Wang, Zhitao, and Wenjie Li
    • IJCAI 2019 [Paper]
  • Joint Learning of Embedding-based Parent Components and Information Diffusion for Social Networks

    • Bao, Q., Cheung, W. K., Shi, B., Qiu, H., & Ma, L.
    • IEEE Access 2020
  • Personalized DeepInf: enhanced social influence prediction with deep learning and transfer learning

    • Leung, C. K., Cuzzocrea, A., Mai, J. J., Deng, D., & Jiang, F.
    • IEEE International Conference on Big Data 2019.
  • DiffusionGAN: Network Embedding for Information Diffusion Prediction with Generative Adversarial Nets

    • Zhuo, W., Zhao, Y., Zhan, Q., & Liu, Y.
    • Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) 2019
  • Inf-VAE: A Variational Autoencoder Framework to IntegrateHomophily and Influence in Diffusion Prediction

    • Aravind Sankar, Xinyang Zhang, Adit Krishnan, Jiawei Han
    • arXiv 2020 [Paper]
  • Network Diffusions via Neural Mean-Field Dynamics

    • He, Shushan, Hongyuan Zha, and Xiaojing Ye
    • arXiv 2020 [Paper]
  • HID: Hierarchical Multiscale Representation Learning for Information Diffusion

    • Honglu Zhou , Shuyuan Xu , Zuohui Fu ,Gerard de Melo , Yongfeng Zhang and Mubbasir Kapadia
    • IJCAI 2020 [Paper]

Learning and Maximization

  • Online influence maximization

    • Lei, Siyu and Maniu, Silviu and Mo, Luyi and Cheng, Reynold and Senellart, Pierre
    • KDD 2015 [Paper]
  • A learning-based framework to handle multi-round multi-party influence maximization on social networks

    • Lin, Su-Chen and Lin, Shou-De and Chen, Ming-Syan
    • KDD 2015 [Paper]
  • Combinatorial multi-armed bandit and its extension to probabilistically triggered arms

    • Chen, Wei, Yajun Wang, Yang Yuan, and Qinshi Wang
    • JMLR 2016 [Paper]
  • Influence maximization with bandits

    • Vaswani, Sharan and Lakshmanan, Laks and Schmidt, Mark and others
    • arXiv 2016 [paper]
  • Stochastic online greedy learning with semi-bandit feedbacks

    • Lin, Tian and Li, Jian and Chen, Wei
    • NIPS 2015 [Paper]
  • Online influence maximization under independent cascade model with semi-bandit feedback

    • Wen, Zheng and Kveton, Branislav and Valko, Michal and Vaswani, Sharan
    • NIPS 2017 [Paper]
  • Model-Independent Online Learning for Influence Maximization

    • Vaswani, Sharan and Kveton, Branislav and Wen, Zheng and Ghavamzadeh, Mohammad and Lakshmanan, Laks and Schmidt, Mark
    • arXiv 2017 [Paper]
  • Uncharted but not Uninfluenced: Influence Maximization with an Uncertain Network

    • Wilder, Bryan, Amulya Yadav, Nicole Immorlica, Eric Rice, and Milind Tambe
    • AAMAS 2017 [Paper]
  • Viral cascade probability estimation and maximization in diffusion networks

    • Sepehr, Arman and Beigy, Hamid
    • TKDE 2018 [Paper]
  • Maximizing influence in an unknown social network

    • Wilder, Bryan, Nicole Immorlica, Eric Rice, and Milind Tambe.
    • AAAI 2018 [Paper]
  • Multi-round influence maximization

    • Sun, Lichao and Huang, Weiran and Yu, Philip S and Chen, Wei
    • KDD 2018 [Paper]
  • Factorization Bandits for Online Influence Maximization

    • Wu, Qingyun and Li, Zhige and Wang, Huazheng and Chen, Wei and Wang, Hongning
    • KDD 2019 [Paper]
  • Online Influence Maximization with Local Observations

    • Khim, Justin, Varun Jog, and Po-Ling Loh
    • Algorithmic Learning Theory 2019 [Paper]
  • Online Competitive Influence Maximization

    • Zuo, Jinhang, Xutong Liu, Carlee Joe-Wong, John Lui, and Wei Chen
    • arXiv 2020 [Paper]
  • Budgeted Online Influence Maximization

    • Pierre Perrault, Jennifer Healey, Zheng Wen and Michal Valko
    • ICML 2020 [Paper]

Surveys

  • A survey of models and algorithms for social influence analysis

    • Sun, Jimeng and Tang, Jie
    • Social network data analytics 2011 [Paper]
  • Information and influence propagation in social networks

    • Chen, Wei and Lakshmanan, Laks VS and Castillo, Carlos
    • Synthesis Lectures on Data Management 2013 [Paper]
  • Recent advances in information diffusion and influence maximization in complex social networks

    • Zhang, Huiyuan and Mishra, Subhankar and Thai, My T and Wu, J and Wang, Y
    • Opportunistic Mobile Social Networks 2014 [Paper]
  • Diffusion models and approaches for influence maximization in social networks

    • Tejaswi, V and Bindu, PV and Thilagam, P Santhi
    • ICACCI 2016 [Paper]
  • Influence maximization in the field: The arduous journey from emerging to deployed application

    • Yadav, Amulya and Wilder, Bryan and Rice, Eric and Petering, Robin and Craddock, Jaih and Yoshioka-Maxwell, Amanda and Hemler, Mary and Onasch-Vera, Laura and Tambe, Milind and Woo, Darlene
    • Autonomous agents and multiagent systems 2017 [Paper]
  • Debunking the myths of influence maximization: An in-depth benchmarking study

    • Arora, Akhil and Galhotra, Sainyam and Ranu, Sayan
    • SIGMOD 2017 [Paper]
  • Influence maximization in large social networks: Heuristics, models and parameters

    • Sumith, N and Annappa, B and Bhattacharya, Swapan
    • Future Generation Computer Systems 2018 [Paper]
  • A Survey on Influence Maximization in a Social Network

    • Banerjee, Suman and Jenamani, Mamata and Pratihar, Dilip Kumar
    • arXiv preprint arXiv:1808.05502 2018 [Paper]
  • Influence maximization on social graphs: A survey

    • Li, Yuchen and Fan, Ju and Wang, Yanhao and Tan, Kian-Lee
    • TKDE 2018 [Paper]
  • A Survey on Influence and Information Diffusion in Twitter Using Big Data Analytics

    • El Bacha, Radia and Zin, Thi Thi
    • Big Data Analysis and Deep Learning Applications 2018 [Paper]
  • A survey on influence maximization in a social network

    • Banerjee, Suman and Jenamani, Mamata and Pratihar, Dilip Kumar
    • Knowledge and Information Systems 2020 [Paper]

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