Graph Feature Gating Networks
Document Type
Conference Proceeding
Publication Date
10-30-2021
Abstract
Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs. Most GNNs follow a message-passing scheme where the node representations are updated by aggregating and transforming the information from the neighborhood. Meanwhile, they adopt the same strategy in aggregating the information from different feature dimensions. However, suggested by social dimension theory and spectral embedding, there are potential benefits to treat the dimensions differently during the aggregation process. In this work, we investigate to enable heterogeneous contributions of feature dimensions in GNNs. In particular, we propose a general graph feature gating network (GFGN) based on the graph signal denoising problem and then correspondingly introduce three graph filters under GFGN to allow different levels of contributions from feature dimensions. Extensive experiments on various real-world datasets demonstrate the effectiveness and robustness of the proposed frameworks.
Identifier
85119186170 (Scopus)
ISBN
[9781450384469]
Publication Title
International Conference on Information and Knowledge Management Proceedings
External Full Text Location
https://doi.org/10.1145/3459637.3482434
ISSN
21550751
First Page
813
Last Page
822
Recommended Citation
Jin, Wei; Liu, Xiaorui; Ma, Yao; Derr, Tyler; Aggarwal, Charu; and Tang, Jiliang, "Graph Feature Gating Networks" (2021). Faculty Publications. 3728.
https://digitalcommons.njit.edu/fac_pubs/3728
