Abstract
Attributed network embedding enables joint representation learning of node links and attributes. Existing attributed network embedding models are designed in continuous Euclidean spaces which often introduce data redundancy and impose challenges to storage and computation costs. To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation. Specifically, we define a new Weisfeiler-Lehman proximity matrix to capture data dependence between node links and attributes by aggregating the information of node attributes and links from neighboring nodes to a given target node in a layer-wise manner. Based on the Weisfeiler-Lehman proximity matrix, we formulate a new Weisfiler-Lehman matrix factorization learning function under the binary node representation constraint. The learning problem is a mixed integer optimization and an efficient cyclic coordinate descent (CCD) algorithm is used as the solution. Node classification and link prediction experiments on real-world datasets show that the proposed BANE model outperforms the state-of-the-art network embedding methods.
Original language | English |
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Title of host publication | 2018 IEEE International Conference on Data Mining (ICDM 2018) |
Editors | Dacheng Tao, Bhavani Thuraisingham |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1476-1481 |
Number of pages | 6 |
ISBN (Electronic) | 9781538691595, 9781538691588 |
ISBN (Print) | 9781538691601 |
DOIs | |
Publication status | Published - 2018 |
Externally published | Yes |
Event | IEEE International Conference on Data Mining 2018 - Singapore, Singapore Duration: 17 Nov 2018 → 20 Nov 2018 http://icdm2018.org/ |
Conference
Conference | IEEE International Conference on Data Mining 2018 |
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Abbreviated title | ICDM 2018 |
Country | Singapore |
City | Singapore |
Period | 17/11/18 → 20/11/18 |
Internet address |
Keywords
- Attributed network embedding
- Learning to hash
- Weisfeiler-Lehman graph kernels