{Cha10} Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, Krishna P. Gummadi. Measuring User Influence in Twitter: The Million Follower Fallacy. Proc. of AAAI ICWSM, 2010.
{Kwak10} Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. What is Twitter, a Social Network or a News Media? Proc. of WWW, 2010.
{Ye10} Shaozhi Ye and Felix Wu. Measuring Message Propagation and Social Influence on Twitter.com. Proc. of SocInfo, 2010.
{Wang10} Yu Wang, Gao Cong, Guojie Song, Kunqing Xie. Community-based Greedy Algorithm for Mining Top-K Influential Nodes in Mobile Social Networks. Proc. of ACM KDD, 2010. proposed algorithm encompasses two components: 1) an algorithm for detecting communities in a social network by taking into account information diffusion; and 2) a dynamic programming algo- rithm for selecting communities to find influential nodes | have a three-month CDR (call detailed record) data of a city from China Mobile, the largest mobile communication service provider in China; extract a Mobile Social Network from the CDR data
{Bakshy11} Eytan Bakshy, Winter A. Mason, Jake M. Hofman, Duncan J. Watts. Everyone’s an Influencer: Quantifying Influence on Twitter. Proc. of ACM WSDM, 2011.
{Bakshy12} Eytan Bakshy, Itamar Rosenn, Cameron Marlow, and Lada Adamic. The role of social networks in information diffusion. Proc. of WWW, 2012. how tie strength relates to influence and information diffusion
{Lü11} Linyuan Lü, Yi-Cheng Zhang, Chi Ho Yeung, Tao Zhou. Leaders in Social Networks, the Delicious Case. PLOS ONE, 2011, 6(6): e21202. LeaderRank outperforms PageRank in terms of ranking effectiveness
{Tang12} Jie Tang, Tiancheng Lou, and Jon Kleinberg. Inferring social ties across heterogenous networks. Proc. of ACM WSDM, 2012.
{Sáez-Trumper12} D. Sáez-Trumper, G. Comarela, V. A. F. Almeida, R. A. Baeza-Yates, and F. Benevenuto. Finding trendsetters in information networks. In KDD, 2012.
{Silva13} Arlei Silva, Sara Guimarães, Wagner Meira Jr., Mohammed Javeed Zaki. ProfileRank: finding relevant content and influential users based on information diffusion. Proc. of SNAKDD, 2013. while TwitterRank and Influence-Passivity rely on the social network structure in order to identify influential users, ProfileRank measures influence and relevance based only on diffusion data; it is computed by random walks on a bipartite graph whose edges represent generation and consumption of content by users over time
{Liu14} Qi Liu, Biao Xiang, Enhong Chen, Hui Xiong, Fangshuang Tang, and Jeffrey Xu Yu. 2014. Influence Maximization over Large-Scale Social Networks: A Bounded Linear Approach. Proc. of ACM CIKM, 2014. Group-PageRank
{Lü16} Linyuan Lü, Tao Zhou, Qian-Ming Zhang, H. Eugene Stanley. The H-index of a network node and its relation to degree and coreness. Nature Communications, 2016, 7: 10168. maximum value h such that there exists at least h neighbours of degree no less than h; better quantify node influence than either degree or coreness
{Song17} Guojie Song, Yuanhao Li, Xiaodong Chen, Xinran He, Jie Tang. Influential Node Tracking on Dynamic Social Network: An Interchange Greedy Approach. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(2): 359-372.
{Li17} Jianxin Li, Xinjue Wang, Ke Deng, Xiaochun Yang, Timos Sellis, Jeffrey Xu Yu. Most Influential Community Search over Large Social Networks. Proc. of ICDE, 2017.
{Liu17} Qi Liu, Biao Xiang, Nicholas Jing Yuan, Enhong Chen, Hui Xiong, Yi Zheng, and Yu Yang. An Influence Propagation View of PageRank. ACM Trans. Knowl. Discov. Data, 2017, 11(3):Article 30.
{Hofman17} Jake M. Hofman, Amit Sharma, Duncan J. Watts. Prediction and explanation in social systems. Science, 2017, 355: 486–488.
{Canossa19} Alessandro Canossa, Ahmad Azadvar, Casper Harteveld, Anders Drachen, Sebastian Deterding. Influencers in Multiplayer Online Shooters: Evidence of Social Contagion in Playtime and Social Play. Proc. of ACM CHI, 2019. inluencers do indeed impact other players and more so than power users or the average player, thus providing evidence for a social contagion efect and the im- portant role inluencers have in these online social networks
{Song21} Changhao Song, Bo Wang, Qinxue Jiang, Yehua Zhang, Ruifang He, Yuexian Hou. Social Recommendation with Implicit Social Influence. Proc. of ACM SIGIR, 2021. we concern two kinds of implicit influence: Local Implicit Influence of persons on unobserved interpersonal relations, and Global Implicit Influence of items broadcasted to users
Cascade
{Cheng14} Justin Cheng, Lada A. Adamic, P. Alex Dow, Jon Kleinberg, Jure Leskovec. Can cascades be predicted? Proc. of WWW, 2014. cascade growth prediction problem: given a cascade that currently has size k, predict whether it grow beyond the median size f(k); while user cascades are typically smaller than page cascades in our dataset, they tend to have greater structural virality, supporting the intuition that the structure of user-initiated cascades is richer and deeper than that of page-initiated cascadesPDF
{Zhang15} Jing Zhang, Jie Tang, Juanzi Li, Yang Liu, and Chunxiao Xing. 2015. Who Influenced You? Predicting Retweet via Social Influence Locality. ACM Trans. Knowl. Discov. Data, 2015, 9(3):Article 25. If one user observes a microblog having been retweeted by many friends from one same group, she will be very likely to also retweet it under the peer pressure
{Yang16} Yang Yang, Jia Jia, Boya Wu, Jie Tang. Social Role-Aware Emotion Contagion in Image Social Networks. Proc. of AAAI, 2016. users with social roles of opinion leaders and structural hole spanners tend to be more influential than ordinary users in positive emotion contagion while be less influential in negative emotion contagion
{Althoff17} Tim Althoff, Pranav Jindal, and Jure Leskovec. Online Actions with Offline Impact: How Online Social Networks Influence Online and Offline User Behavior. Proc. of ACM WSDM, 2017. how social networks influence user behavior in a physical activity tracking application
{Li17} Cheng Li, Jiaqi Ma, Xiaoxiao Guo, Qiaozhu Mei. DeepCas: an End-to-end Predictor of Information Cascades. Proc. of WWW, 2017. present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines including feature based methods, node embedding methods, and graph kernel methods
{Qiu18} Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. DeepInf: Social Influence Prediction with Deep Learning. Proc. of ACM KDD, 2018. DeepInf takes a user’s local network as the input to a graph neural network for learning her latent social representation
{Cao20} Qi Cao, Huawei Shen, Jinhua Gao, Bingzheng Wei, Xueqi Cheng. Popularity Prediction on Social Platforms with Coupled Graph Neural Networks. Proc. of ACM WSDM, 2020. propose a novel method, namely CoupledGNN, which uses two coupled graph neural networks to capture the interplay between node activation states and the spread of influence
{Jain23} Lokesh Jain, Rahul Katarya, and Shelly Sachdeva. Opinion Leaders for Information Diffusion Using Graph Neural Network in Online Social Networks. ACM Trans. Web, 2023, 17(2):Article 13. proposed an exclusive GNN for Opinion Leader Identification (GOLI) model utilizing the power of GNNs to categorize the opinion leaders and their impact on online social networks
Prediction of Influencers
{Salve21} Andrea De Salve, Paolo Mori, Barbara Guidi, Laura Ricci, and Roberto Di Pietro. Predicting Influential Users in Online Social Network Groups. ACM Trans. Knowl. Discov. Data, 2021, 15(3):Article 35. being able to know in advance which will be the influencers in a near future in a given OSN with respect to a given topic would be a competitive advantage for a company, because it would allow such a company to enter in a long-term advertising contract with such future influencers at a considerably lower price with respect to already well-known ones
Influence-Based Prediction
{Liu20} Haobing Liu, Yanmin Zhu, Tianzi Zang, Jiadi Yu, Haibin Cai. Jointly Modeling Individual Student Behaviors and Social Influence for Prediction Tasks. Proc. of ACM CIKM, 2020. propose a general deep neural network which can jointly model student heterogeneous daily behaviors generated from digital footprints and social influence to deal with prediction tasks
{Wang20} Hongyang Wang, Qingfei Meng, Ju Fan, Yuchen Li, Laizhong Cui, Xiaoman Zhao, Chong Peng, Gong Chen, Xiaoyong Du. Social Influence Does Matter: User Action Prediction for In-Feed Advertising. Proc. of AAAI, 2020. introduces an end-to-end approach that leverages social influence for action prediction, and focuses on addressing the high sparsity challenge for in-feed ads