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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 8: AAAI-22 Technical Tracks 8

Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles

February 1, 2023

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Authors

Zhijie Zhang

Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences


Wei Chen

Microsoft Research Asia


Xiaoming Sun

Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences


Jialin Zhang

Institute of Computing Technology, CAS University of Chinese Academy of Sciences


DOI:

10.1609/aaai.v36i8.20901


Abstract:

We study the online influence maximization (OIM) problem in social networks, where in multiple rounds the learner repeatedly chooses seed nodes to generate cascades, observes the cascade feedback, and gradually learns the best seeds that generate the largest cascade. We focus on two major challenges in this paper. First, we work with node-level feedback instead of edge-level feedback. The edge-level feedback reveals all edges that pass through information in a cascade, whereas the node-level feedback only reveals the activated nodes with timestamps. The node-level feedback is arguably more realistic since in practice it is relatively easy to observe who is influenced but very difficult to observe from which relationship (edge) the influence comes. Second, we use standard offline oracles instead of offline pair-oracles. To compute a good seed set for the next round, an offline pair-oracle finds the best seed set and the best parameters within the confidence region simultaneously, and such an oracle is difficult to compute due to the combinatorial core of the OIM problem. So we focus on how to use the standard offline influence maximization oracle which finds the best seed set given the edge parameters as input. In this paper, we resolve these challenges for the famous independent cascade (IC) diffusion model. The past research only achieves edge-level feedback, while we present the first optimal regret algorithm for the node-level feedback. For the first challenge above, we apply a novel adaptation of the maximum likelihood estimation (MLE) approach to learn the graph parameters and its confidence region (a confidence ellipsoid). For the second challenge, we adjust the update procedure to dissect the confidence ellipsoid into confidence intervals on each parameter, so that the standard offline influence maximization oracle is enough.

Topics: AAAI

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HOW TO CITE:

Zhijie Zhang||Wei Chen||Xiaoming Sun||Jialin Zhang Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles Proceedings of the AAAI Conference on Artificial Intelligence (2022) 9153-9161.

Zhijie Zhang||Wei Chen||Xiaoming Sun||Jialin Zhang Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles AAAI 2022, 9153-9161.

Zhijie Zhang||Wei Chen||Xiaoming Sun||Jialin Zhang (2022). Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles. Proceedings of the AAAI Conference on Artificial Intelligence, 9153-9161.

Zhijie Zhang||Wei Chen||Xiaoming Sun||Jialin Zhang. Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.9153-9161.

Zhijie Zhang||Wei Chen||Xiaoming Sun||Jialin Zhang. 2022. Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles. "Proceedings of the AAAI Conference on Artificial Intelligence". 9153-9161.

Zhijie Zhang||Wei Chen||Xiaoming Sun||Jialin Zhang. (2022) "Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles", Proceedings of the AAAI Conference on Artificial Intelligence, p.9153-9161

Zhijie Zhang||Wei Chen||Xiaoming Sun||Jialin Zhang, "Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles", AAAI, p.9153-9161, 2022.

Zhijie Zhang||Wei Chen||Xiaoming Sun||Jialin Zhang. "Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.9153-9161.

Zhijie Zhang||Wei Chen||Xiaoming Sun||Jialin Zhang. "Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 9153-9161.

Zhijie Zhang||Wei Chen||Xiaoming Sun||Jialin Zhang. Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles. AAAI[Internet]. 2022[cited 2023]; 9153-9161.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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