Adversarial Substructured Representation Learning for Mobile User Profiling


Mobile user profiles are a summary of characteristics of user-specific mobile activities. Mobile user profiling is to extract a user's interest and behavioral patterns from mobile behavioral data. While some efforts have been made for mobile user profiling, existing methods can be improved via representation learning with awareness of substructures in users' behavioral graphs. Specifically, in this paper, we study the problem of mobile users profiling with POI check-in data. To this end, we first construct a graph, where a vertex is a POI category and an edge is the transition frequency of a user between two POI categories, to represent each user. We then formulate mobile user profiling as a task of representation learning from user behavioral graphs. We later develop a deep adversarial substructured learning framework for the task. This framework has two mutually-enhanced components. The first component is to preserve the structure of the entire graph, which is formulated as an encoding-decoding paradigm. In particular, the structure of the entire graph is preserved by minimizing reconstruction loss between an original graph and a reconstructed graph. The second component is to preserve the structure of subgraphs, which is formulated as a substructure detector based adversarial training paradigm. In particular, this paradigm includes a substructure detector and an adversarial trainer. Instead of using non-differentiable substructure detection algorithms, we pre-train a differentiable convolutional neural network as the detector to approximate these detection algorithms. The adversarial trainer is to match the detected substructure of the reconstructed graph to the detected substructure of the original graph. Also, we provide an effective solution for the optimization problems. Moreover, we exploit the learned representations of users for the next activity type prediction. Finally, we present extensive experimental results to demonstrate the improved performances of the proposed method.

Meeting Name

25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 (2019: Aug. 4-8, Anchorage, AK)


Computer Science


This research was partially supported by the National Science Foundation (NSF) via the grant number: 1755946. This research was partially supported by the National Science Foundation (NSF) via the grant number: IIS-1814510.

Keywords and Phrases

Mobile User Profiling; Representation Learning; Substructure

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Document Type

Article - Conference proceedings

Document Version


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© 2019 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Aug 2019