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Leveraging Cross-Network Information for Graph Sparsification in Influence Maximization

Published: 07 August 2017 Publication History

Abstract

When tackling large-scale influence maximization (IM) problem, one effective strategy is to employ graph sparsification as a pre-processing step, by removing a fraction of edges to make original networks become more concise and tractable for the task. In this work, a Cross-Network Graph Sparsification (CNGS) model is proposed to leverage the influence backbone knowledge pre-detected in a source network to predict and remove the edges least likely to contribute to the influence propagation in the target networks. Experimental results demonstrate that conducting graph sparsification by the proposed CNGS model can obtain a good trade-off between efficiency and effectiveness of IM, i.e., existing IM greedy algorithms can run more efficiently, while the loss of influence spread can be made as small as possible in the sparse target networks.

References

[1]
P. Domingos, and M. Richardson. Mining the network value of customers. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001.
[2]
D. Kempe, J. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.
[3]
J. Leskovec, A. Krause, and C. Guestrin. Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007.
[4]
W. Chen, Y. Wang, and S. Yang. Efficient influence maximization in social networks. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009.
[5]
A. Goyal, W. Lu, and L. V. S. Lakshmanan. Celf++: Optimizing the greedy algorithm for influence maximization in social networks. In Proceedings of the 20th International Conference Companion on World Wide Web, 2011.
[6]
B. Wilder, and G. Sukthankar. Sparsification of Social Networks Using Random Walks. In Proceedings of International Conference on Social Computation (SocialCom), 2015.
[7]
M. Mathioudakis, F. Bonchi, and C. Castillo. Sparsification of influence networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
[8]
F. Zhou, S.Mahler, and H. Toivonen. Simplification of networks by edge pruning. In Bisociative Knowledge Discovery, 2012.
[9]
H. Lamba, and R. Narayanam. A novel and model independent approach for efficient influence maximization in social networks. In Proceedings of International Conference on Web Information Systems Engineering, 2013.
[10]
Q. Hu, G. Wang, and P. S. Yu. Transferring influence: Supervised learning for efficient influence maximization across networks. In International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), 2014.
[11]
M. Chen, K. Q. Weinberger, and J. Blitzer. Co-training for domain adaptation. In Advances in Neural Information Processing Systems, 2011.

Cited By

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  • (2024)Multi-Source and Multi-modal Deep Network Embedding for Cross-Network Node ClassificationACM Transactions on Knowledge Discovery from Data10.1145/365330418:6(1-26)Online publication date: 26-Apr-2024
  • (2024)Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge ClassificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.330963235:12(17842-17855)Online publication date: Dec-2024
  • (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
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cover image ACM Conferences
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
August 2017
1476 pages
ISBN:9781450350228
DOI:10.1145/3077136
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: 07 August 2017

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Author Tags

  1. cross-network
  2. domain adaptation
  3. feature incompatibility
  4. graph sparsification
  5. influence maximization
  6. self-training

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  • Short-paper

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  • HKPhDF
  • RGC General Research Fund PolyU

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SIGIR '17
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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2024)Multi-Source and Multi-modal Deep Network Embedding for Cross-Network Node ClassificationACM Transactions on Knowledge Discovery from Data10.1145/365330418:6(1-26)Online publication date: 26-Apr-2024
  • (2024)Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge ClassificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.330963235:12(17842-17855)Online publication date: Dec-2024
  • (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
  • (2023)MSDS: A Novel Framework for Multi-Source Data Selection Based Cross-Network Node ClassificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327795735:12(12799-12813)Online publication date: 1-Dec-2023
  • (2023)Domain-adaptive message passing graph neural networkNeural Networks10.1016/j.neunet.2023.04.038164(439-454)Online publication date: Jul-2023
  • (2022)SparRL: Graph Sparsification via Deep Reinforcement LearningProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3520254(2521-2523)Online publication date: 10-Jun-2022
  • (2022)Graph Transfer Learning via Adversarial Domain Adaptation with Graph ConvolutionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3144250(1-1)Online publication date: 2022
  • (2022)A Generic Graph Sparsification Framework using Deep Reinforcement Learning2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00158(1221-1226)Online publication date: Nov-2022
  • (2021)Network Together: Node Classification via Cross-Network Deep Network EmbeddingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.299548332:5(1935-1948)Online publication date: May-2021
  • (2021)MHCNC: A Novel Framework for Multi-Source Heterogeneous Cross-Network Node Classification2021 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM46510.2021.9685924(1-6)Online publication date: 7-Dec-2021
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