Long-short distance aggregation networks for positive unlabeled graph learning

Man Wu, Ivor Tsang, Shirui Pan, Xingquan Zhu, Lan Du, Bo Du

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

Graph neural nets are emerging tools to represent network nodes for classification. However, existing approaches typically suffer from two limitations: (1) they only aggregate information from short distance (e.g., 1-hop neighbors) each round and fail to capture long distance relationship in graphs; (2) they require users to label data from several classes to facilitate the learning of discriminative models; whereas in reality, users may only provide labels of a small number of nodes in a single class. To overcome these limitations, this paper presents a novel long-short distance aggregation networks (LSDAN) for positive unlabeled (PU) graph learning. Our theme is to generate multiple graphs at different distances based on the adjacency matrix, and further develop a long-short distance attention model for these graphs. The short-distance attention mechanism is used to capture the importance of neighbor nodes to a target node. The long-distance attention mechanism is used to capture the propagation of information within a localized area of each node and help model weights of different graphs for node representation learning. A non-negative risk estimator is further employed, to aggregate long- short-distance networks, for PU learning using back-propagated loss modeling. Experiments on real-world datasets validate the effectiveness of our approach.

Original languageEnglish
Title of host publicationProceedings of the 28th ACM International Conference on Information & Knowledge Management
EditorsPeng Cui, Elke Rundensteiner, David Carmel, Qi He, Jeffrey Xu Yu
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages2157-2160
Number of pages4
ISBN (Electronic)9781450369763
DOIs
Publication statusPublished - 2019
EventACM International Conference on Information and Knowledge Management 2019 - Beijing, China
Duration: 3 Nov 20197 Nov 2019
Conference number: 28th
http://www.cikm2019.net/
https://dl.acm.org/doi/proceedings/10.1145/3357384

Conference

ConferenceACM International Conference on Information and Knowledge Management 2019
Abbreviated titleCIKM 2019
CountryChina
CityBeijing
Period3/11/197/11/19
Internet address

Keywords

  • Graph neural networks
  • Positive unlabeled learning

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