Abstract
For a long time, PageRank has been widely used for authority computation and has been adopted as a solid baseline for evaluating social influence related applications. However, when measuring the authority of network nodes, the traditional PageRank method does not take the nodes’ prior knowledge into consideration. Also, the connection between PageRank and social influence modeling methods is not clearly established. To that end, this article provides a focused study on understanding PageRank as well as the relationship between PageRank and social influence analysis. Along this line, we first propose a linear social influence model and reveal that this model generalizes the PageRank-based authority computation by introducing some constraints. Then, we show that the authority computation by PageRank can be enhanced if exploiting more reasonable constraints (e.g., from prior knowledge). Next, to deal with the computational challenge of linear model with general constraints, we provide an upper bound for identifying nodes with top authorities. Moreover, we extend the proposed linear model for better measuring the authority of the given node sets, and we also demonstrate the way to quickly identify the top authoritative node sets. Finally, extensive experimental evaluations on four real-world networks validate the effectiveness of the proposed linear model with respect to different constraint settings. The results show that the methods with more reasonable constraints can lead to better ranking and recommendation performance. Meanwhile, the upper bounds formed by PageRank values could be used to quickly locate the nodes and node sets with the highest authorities.
- C. C. Aggarwal. 2011. Social Network Data Analytics. Springer. Google ScholarDigital Library
- C. C. Aggarwal, A. Khan, and X. Yan. 2011. On flow authority discovery in social networks. In Proceedings of SIAM Conference on Data Mining (SDM’11). 522--533.Google Scholar
- Réka Albert, Hawoong Jeong, and Albert-László Barabási. 1999. Internet: Diameter of the world-wide web. Nature 401, 6749 (1999), 130--131.Google Scholar
- Lars Backstrom and Jure Leskovec. 2011. Supervised random walks: Predicting and recommending links in social networks. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. ACM, 635--644. Google ScholarDigital Library
- Henry H. Bi, Jianrui Wang, and Dennis K. J. Lin. 2011. Comprehensive citation index for research networks. IEEE Transactions on Knowledge and Data Engineering 23, 8 (2011), 1274--1278. Google ScholarDigital Library
- M. Bianchini, M. Gori, and F. Scarselli. 2005. Inside pagerank. ACM Transactions on Internet Technology 5, 1 (2005), 92--128. Google ScholarDigital Library
- Wei Chen, Laks V. S. Lakshmanan, and Carlos Castillo. 2013. Information and influence propagation in social networks. Synthesis Lectures on Data Management 5, 4 (2013), 1--177. Google ScholarCross Ref
- W. Chen, C. Wang, and Y. Wang. 2010. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1029--1038. Google ScholarDigital Library
- W. Chen, Y. Wang, and S. Yang. 2009. Efficient influence maximization in social networks. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 199--208. Google ScholarDigital Library
- Suqi Cheng, Huawei Shen, Junming Huang, Wei Chen, and Xueqi Cheng. 2014. Imrank: Influence maximization via finding self-consistent ranking. In Proceedings of the 37th International ACM SIGIR Conference on Research 8 Development in Information Retrieval. ACM, 475--484. Google ScholarDigital Library
- Feng Ding, Peter X. Liu, and Jie Ding. 2008. Iterative solutions of the generalized Sylvester matrix equations by using the hierarchical identification principle. Applied Mathematics 8 Computation 197, 1 (2008), 41--50.Google Scholar
- Y. Ding. 2011. Topic-based pagerank on author cocitation networks. Journal of the American Society for Information Science and Technology 62, 3 (2011), 449--466. Google ScholarDigital Library
- Y. Ding, E. Yan, A. Frazho, and J. Caverlee. 2009. PageRank for ranking authors in co-citation networks. Journal of the American Society for Information Science and Technology 60, 11 (2009), 2229--2243. Google ScholarCross Ref
- Nan Du, Le Song, Manuel Gomez-Rodriguez, and Hongyuan Zha. 2013. Scalable influence estimation in continuous-time diffusion networks. In Proceedings of Advances in Neural Information Processing Systems Conference. 3147--3155. Google ScholarDigital Library
- A. Farahat, T. LoFaro, J. C. Miller, G. Rae, and L. A. Ward. 2006. Authority rankings from HITS, Pagerank, and SALSA: Existence, uniqueness, and effect of initialization. SIAM Journal on Scientific Computing 27, 4 (2006), 1181--1201. Google ScholarDigital Library
- Santo Fortunato. 2010. Community detection in graphs. Physics Reports 486, 3 (2010), 75--174.Google ScholarCross Ref
- Bin Gao, Tie-Yan Liu, Wei Wei, Taifeng Wang, and Hang Li. 2011. Semi-supervised ranking on very large graphs with rich metadata. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 96--104. Google ScholarDigital Library
- Priyanka Garg, Irwin King, and Michael R Lyu. 2012. Information propagation in social rating networks. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM, 2279--2282. Google ScholarDigital Library
- J. Goldenberg, B. Libai, and E. Muller. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12, 3 (2001), 211--223.Google ScholarCross Ref
- Gene H. Golub and Charles F. Van Loan. 1996. Matrix Computations. Johns Hopkins University Press, 392--396.Google Scholar
- Manuel Gomez-rodriguez, Jure Leskovec, and others. 2013. Modeling information propagation with survival theory. In Proceedings of the 30th International Conference on Machine Learning (ICML’13). 666--674. Google ScholarDigital Library
- A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. 2010. Learning influence probabilities in social networks. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. ACM, 241--250. Google ScholarDigital Library
- A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. 2011. A data-based approach to social influence maximization. Proceedings of the VLDB Endowment 5, 1 (2011), 73--84. Google ScholarDigital Library
- M. Granovetter. 1978. Threshold models of collective behavior. American Journal of Sociology (1978), 1420--1443.Google Scholar
- T. H. Haveliwala. 2003. Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Transactions on Knowledge and Data Engineering 15, 4 (2003), 784--796. Google ScholarDigital Library
- Mohsen Jamali and Martin Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the 4th ACM Conference on Recommender Systems. ACM, 135--142. Google ScholarDigital Library
- Glen Jeh and Jennifer Widom. 2003. Scaling personalized web search. In Proceedings of the 12th International Conference on World Wide Web. ACM, 271--279. Google ScholarDigital Library
- Ruoming Jin, Victor E. Lee, and Longjie Li. 2014. Scalable and axiomatic ranking of network role similarity. ACM Transactions on Knowledge Discovery from Data 8, 1 (2014), 3. Google ScholarDigital Library
- Kyomin Jung, Wooram Heo, and Wei Chen. 2012. IRIE: Scalable and robust influence maximization in social networks. In Proceedings of the 12th IEEE International Conference on Data Mining (ICDM’12). IEEE, 918--923. Google ScholarDigital Library
- D. Kempe, J. Kleinberg, and É. Tardos. 2003. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 137--146. Google ScholarDigital Library
- M. Kimura and K. Saito. 2006. Tractable models for information diffusion in social networks. Knowledge Discovery in Databases 2006 (2006), 259--271.Google Scholar
- J. M. Kleinberg. 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM 46, 5 (1999), 604--632. Google ScholarDigital Library
- Onur Küçüktunç, Erik Saule, Kamer Kaya, and Ümit V. Çatalyürek. 2013. Diversified recommendation on graphs: Pitfalls, measures, and algorithms. In Proceedings of the 22nd International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 715--726. Google ScholarDigital Library
- Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is twitter, a social network or a news media?. In Proceedings of the 19th International Conference on World Wide Web. ACM, 591--600. Google ScholarDigital Library
- A. N. Langville and C. D. Meyer. 2004. Deeper inside PageRank. Internet Mathematics 1, 3 (2004), 335--380.Google ScholarCross Ref
- J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. 2007. Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 420--429. Google ScholarDigital Library
- Pei Li, Jeffrey Xu Yu, Hongyan Liu, Jun He, and Xiaoyong Du. 2011. Ranking individuals and groups by influence propagation. In Advances in Knowledge Discovery and Data Mining. Springer, 407--419. Google ScholarDigital Library
- Rong-Hua Li and Jeffrey Xu Yu. 2011. Scalable diversified ranking on large graphs. In Proceedings of the 11th IEEE International Conference on Data Mining (ICDM’11). IEEE, 1152--1157. Google ScholarDigital Library
- D. Liben-Nowell and J. Kleinberg. 2007. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology 58, 7 (2007), 1019--1031. Google ScholarDigital Library
- Q. Liu, B. Xiang, E. Chen, Y. Ge, H. Xiong, T. Bao, and Y. Zheng. 2012. Influential seed items recommendation. In Proceedings of the 6th ACM Conference on Recommender Systems. ACM, 245--248. Google ScholarDigital Library
- 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. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 171--180. Google ScholarDigital Library
- Qi Liu, Biao Xiang, Lei Zhang, Enhong Chen, Chang Tan, and Ji Chen. 2013. Linear computation for independent social influence. In Proceedings of the 13th IEEE International Conference on Data Mining (ICDM’13). IEEE, 468--477.Google ScholarCross Ref
- Brendan Lucier, Joel Oren, and Yaron Singer. 2015. Influence at scale: Distributed computation of complex contagion in networks. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 735--744. Google ScholarDigital Library
- Paolo Massa and Paolo Avesani. 2006. Trust-aware bootstrapping of recommender systems. In Proceedings of ECAI Workshop on Recommender Systems.29--33.Google Scholar
- L. Page, S. Brin, R. Motwani, and T. Winograd. 1999. The pagerank citation ranking: Bringing order to the web. (1999).Google Scholar
- B. Aditya Prakash and Christos Faloutsos. 2012. Understanding and managing cascades on large graphs. Proceedings of the VLDB Endowment 5, 12 (2012), 2024--2025. Google ScholarDigital Library
- Diego Saez-Trumper, Giovanni Comarela, Virgílio Almeida, Ricardo Baeza-Yates, and Fabrício Benevenuto. 2012. Finding trendsetters in information networks. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1014--1022. Google ScholarDigital Library
- Chuan Shi, Xiangnan Kong, Yue Huang, Philip S. Yu, and Bin Wu. 2014. Hetesim: A general framework for relevance measure in heterogeneous networks. IEEE Transactions on Knowledge and Data Engineering 26, 10 (2014), 2479--2492.Google ScholarCross Ref
- Karthik Subbian, Chidananda Sridhar, Charu C. Aggarwal, and Jaideep Srivastava. 2014. Scalable information flow mining in networks. In Machine Learning and Knowledge Discovery in Databases. Springer, 130--146.Google Scholar
- J. Tang, J. Sun, C. Wang, and Z. Yang. 2009. Social influence analysis in large-scale networks. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 807--816. Google ScholarDigital Library
- Lei Tang, Huan Liu, and Jianping Zhang. 2012. Identifying evolving groups in dynamic multimode networks. IEEE Transactions on Knowledge and Data Engineering 24, 1 (2012), 72--85. Google ScholarDigital Library
- Hanghang Tong, Jingrui He, Zhen Wen, Ravi Konuru, and Ching-Yung Lin. 2011. Diversified ranking on large graphs: An optimization viewpoint. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’11), Vol. 11. 1028--1036. Google ScholarDigital Library
- Guan Wang, Yuchen Zhao, Xiaoxiao Shi, and Philip S. Yu. 2012. Magnet community identification on social networks. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 588--596. Google ScholarDigital Library
- Jianshu Weng, Ee-Peng Lim, Jing Jiang, and Qi He. 2010. Twitterrank: Finding topic-sensitive influential twitterers. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. ACM, 261--270. Google ScholarDigital Library
- B. Xiang, Q. Liu, E. Chen, H. Xiong, Y. Zheng, and Y. Yang. 2013. Pagerank with priors: An influence propagation perspective. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI’13). 2740--2746. Google ScholarDigital Library
- Tong Xu, Hengshu Zhu, Xiangyu Zhao, Qi Liu, Hao Zhong, Enhong Chen, and Hui Xiong. 2016. Taxi driving behavior analysis in latent vehicle-to-vehicle networks: A social influence perspective. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1285--1294. Google ScholarDigital Library
- Jaewon Yang, Bee-Chung Chen, and Deepak Agarwal. 2013. Estimating sharer reputation via social data calibration. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 59--67. Google ScholarDigital Library
- Y. Yang, E. Chen, Q. Liu, B. Xiang, T. Xu, and S. Shad. 2012. On approximation of real-world influence spread. In proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases. 548--564.Google Scholar
- Zhiwen Yu, Zhu Wang, Huilei He, Jilei Tian, Xinjiang Lu, and Bin Guo. 2015. Discovering information propagation patterns in microblogging services. ACM Transactions on Knowledge Discovery from Data 10, 1 (2015), 7. Google ScholarDigital Library
- Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu. 2014. Social Media Mining: An Introduction. Cambridge University Press. Google ScholarDigital Library
- Jing Zhang, Jie Tang, Juanzi Li, Yang Liu, and Chunxiao Xing. 2014. Who influenced you? Predicting retweet via social influence locality. ACM Transactions on Knowledge Discovery from Data 9, 3 (2014), 25. Google ScholarDigital Library
- Jiawei Zhang and Philip S. Yu. 2015. Community detection for emerging networks. In Proceedings of SIAM Conference on Data Mining (SDM’15).Google Scholar
- Kai Zheng, Han Su, Bolong Zheng, Shuo Shang, Jiajie Xu, Jiajun Liu, and Xiaofang Zhou. 2015. Interactive top-k spatial keyword queries. In Proceedings of the 2015 IEEE 31st International Conference on Data Engineering. IEEE, 423--434.Google ScholarCross Ref
- Yang Zhou and Ling Liu. 2015. Social influence based clustering and optimization over heterogeneous information networks. ACM Transactions on Knowledge Discovery from Data) 10, 1 (2015), 2. Google ScholarDigital Library
- H. Zhu, H. Cao, H. Xiong, E. Chen, and J. Tian. 2011. Towards expert finding by leveraging relevant categories in authority ranking. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management. ACM, 2221--2224. Google ScholarDigital Library
Index Terms
- An Influence Propagation View of PageRank
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