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Identifying Decision Makers from Professional Social Networks

Authors:

Evangelia Christakopoulou,

Abhishek GuptaAuthors Info & Claims

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 333 - 342

https://doi.org/10.1145/2939672.2939707

Published: 13 August 2016 Publication History

Abstract

Sales professionals help organizations win clients for products and services. Generating new clients starts with identifying the right decision makers at the target organization. For the past decade, online professional networks have collected tremendous amount of data on people's identity, their network and behavior data of buyers and sellers building relationships with each other for a variety of use-cases. Sales professionals are increasingly relying on these networks to research, identify and reach out to potential prospects, but it is often hard to find the right people effectively and efficiently. In this paper we present LDMS, the LinkedIn Decision Maker Score, to quantify the ability of making a sales decision for each of the 400M+ LinkedIn members. It is the key data-driven technology underlying Sales Navigator, a proprietary LinkedIn product that is designed for sales professionals. We will specifically discuss the modeling challenges of LDMS, and present two graph-based approaches to tackle this problem by leveraging the professional network data at LinkedIn. Both approaches are able to leverage both the graph information and the contextual information on the vertices, deal with small amount of labels on the graph, and handle heterogeneous graphs among different types of vertices. We will show some offline evaluations of LDMS on historical data, and also discuss its online usage in multiple applications in live production systems as well as future use cases within the LinkedIn ecosystem.

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References

[1]

S. Boyd, C. Cortes, M. Mohri, and A. Radovanovic. Accuracy at the top. In Advances in neural information processing systems, pages 953--961, 2012.

[2]

S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. In Proceedings of the Seventh World Wide Web Conference, 1998.

Digital Library

[3]

O. Chapelle and Y. Chang. Yahoo! learning to rank challenge overview. In Yahoo! Learning to Rank Challenge, pages 1--24, 2011.

[4]

T. Connor. 91 Mistakes Smart Salespeople Make. Sourcebooks, Inc., 2006.

[5]

B. A. Duncan and C. P. Elkan. Probabilistic modeling of a sales funnel to prioritize leads. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1751--1758. ACM, 2015.

Digital Library

[6]

J. J. Fox. Secrets of Great Rainmakers: The Keys to Success and Wealth. Hachette Books, 2006.

[7]

Z. Gyöngyi, H. Garcia-Molina, and J. Pedersen. Combating web spam with trustrank. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, pages 576--587. VLDB Endowment, 2004.

Digital Library

[8]

T. Haveliwala, S. Kamvar, and G. Jeh. An analytical comparison of approaches to personalizing pagerank. 2003.

[9]

T. H. Haveliwala. Topic-sensitive pagerank. In Proceedings of the 11th international conference on World Wide Web, pages 517--526. ACM, 2002.

Digital Library

[10]

A. B. Karl Schmidt, Brent Adamson. Making the consensus sale. https://hbr.org/2015/03/making-the-consensus-sale, 2015. {Online}.

[11]

J. M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5):604--632, 1999.

Digital Library

[12]

D. Kurlan. Top 4 reasons salespeople struggle to reach decision makers. https://www.salesforce.com/blog/2014/03/sales-decision-makers-gp.html, 2014. {Online}.

[13]

L. Liu, J. Tang, J. Han, M. Jiang, and S. Yang. Mining topic-level influence in heterogeneous networks. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 199--208. ACM, 2010.

Digital Library

[14]

Y. Liu, B. Gao, T.-Y. Liu, Y. Zhang, Z. Ma, S. He, and H. Li. Browserank: letting web users vote for page importance. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 451--458. ACM, 2008.

Digital Library

[15]

Q. Meng and P. J. Kennedy. Discovering influential authors in heterogeneous academic networks by a co-ranking method. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, pages 1029--1036. ACM, 2013.

Digital Library

[16]

A. Pal and S. Counts. Identifying topical authorities in microblogs. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 45--54. ACM, 2011.

Digital Library

[17]

M. Richardson and P. Domingos. The intelligent surfer: Probabilistic combination of link and content information in pagerank. In NIPS, pages 1441--1448, 2001.

Digital Library

[18]

D. Saez-Trumper, G. Comarela, V. Almeida, R. Baeza-Yates, and F. Benevenuto. Finding trendsetters in information networks. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1014--1022. ACM, 2012.

Digital Library

[19]

J. Weng, E.-P. Lim, J. Jiang, and Q. He. Twitterrank: finding topic-sensitive influential twitterers. In Proceedings of the third ACM international conference on Web search and data mining, pages 261--270. ACM, 2010.

Digital Library

[20]

D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf. Learning with local and global consistency. Advances in neural information processing systems, 16(16):321--328, 2004.

Digital Library

[21]

D. Zhou, T. Hofmann, and B. Schölkopf. Semi-supervised learning on directed graphs. In Advances in neural information processing systems, pages 1633--1640, 2004.

Digital Library

[22]

D. Zhou, J. Huang, and B. Schölkopf. Learning from labeled and unlabeled data on a directed graph. In Proceedings of the 22nd international conference on Machine learning, pages 1036--1043. ACM, 2005.

Digital Library

[23]

D. Zhou, S. Orshanskiy, H. Zha, C. L. Giles, et al. Co-ranking authors and documents in a heterogeneous network. In Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on, pages 739--744. IEEE, 2007.

Digital Library

[24]

X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the Twentieth International Conference on Machine Learning (IC\ ML-2003), 2003.

[25]

H. Zou and T. Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2):301--320, 2005.

Cited By

Zhu HXiong HGe YLim ESingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)The 4th International Workshop on Talent and Management Computing (TMC'2023)Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599200(5909-5910)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599200
LI ZZHU C(2022)The marketing strategy of online video based on danmaku-video: A bimodal analysisAdvances in Psychological Science10.3724/SP.J.1042.2021.0156129:9(1561-1575)Online publication date: 13-Jul-2022
https://doi.org/10.3724/SP.J.1042.2021.01561
Omidvar-Tehrani BAmer-Yahia S(2020)User Group Analytics Survey and Research OpportunitiesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.291365132:10(2040-2059)Online publication date: 1-Oct-2020
https://doi.org/10.1109/TKDE.2019.2913651
Show More Cited By

Index Terms

Identifying Decision Makers from Professional Social Networks
1. Computing methodologies
  1. Artificial intelligence

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Published In

cover image ACM Conferences

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2016

2176 pages

ISBN:9781450342322

DOI:10.1145/2939672

General Chairs:
Balaji Krishnapuram
IBM
,
Mohak Shah
Bosch
,
Program Chairs:
Alex Smola
Amazon
,
Charu Aggarwal
IBM
,
Dou Shen
Baidu
,
Rajeev Rastogi
Amazon

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 August 2016

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KDD '16

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KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2016

California, San Francisco, USA

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KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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August 3 - 7, 2025

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Cited By

Zhu HXiong HGe YLim ESingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)The 4th International Workshop on Talent and Management Computing (TMC'2023)Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599200(5909-5910)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599200
LI ZZHU C(2022)The marketing strategy of online video based on danmaku-video: A bimodal analysisAdvances in Psychological Science10.3724/SP.J.1042.2021.0156129:9(1561-1575)Online publication date: 13-Jul-2022
https://doi.org/10.3724/SP.J.1042.2021.01561
Omidvar-Tehrani BAmer-Yahia S(2020)User Group Analytics Survey and Research OpportunitiesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.291365132:10(2040-2059)Online publication date: 1-Oct-2020
https://doi.org/10.1109/TKDE.2019.2913651
Ni WGuo HLiu TZeng Q(2020)Automatic role identification for research teams with ranking multi-view machinesKnowledge and Information Systems10.1007/s10115-020-01504-w62:12(4681-4716)Online publication date: 27-Aug-2020
https://doi.org/10.1007/s10115-020-01504-w
Jia HLi C(2019)The Research of the Persona of Online Car-hailing Drivers based on Density Peak Clustering2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE)10.1109/EITCE47263.2019.9094867(1443-1448)Online publication date: Oct-2019
https://doi.org/10.1109/EITCE47263.2019.9094867
Rossetti GMilli LGiannotti FPedreschi D(2017)Forecasting success via early adoptions analysis: A data-driven studyPLOS ONE10.1371/journal.pone.018909612:12(e0189096)Online publication date: 7-Dec-2017
https://doi.org/10.1371/journal.pone.0189096

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