TY - JOUR
AU - Shi, Han
AU - Fan, Haozheng
AU - Kwok, James T.
PY - 2020/04/03
Y2 - 2024/08/08
TI - Effective Decoding in Graph Auto-Encoder Using Triadic Closure
JF - Proceedings of the AAAI Conference on Artificial Intelligence
JA - AAAI
VL - 34
IS - 01
SE - AAAI Technical Track: Applications
DO - 10.1609/aaai.v34i01.5437
UR - https://ojs.aaai.org/index.php/AAAI/article/view/5437
SP - 906-913
AB - <p>The (variational) graph auto-encoder and its variants have been popularly used for representation learning on graph-structured data. While the encoder is often a powerful graph convolutional network, the decoder reconstructs the graph structure by only considering two nodes at a time, thus ignoring possible interactions among edges. On the other hand, structured prediction, which considers the whole graph simultaneously, is computationally expensive. In this paper, we utilize the well-known <em>triadic closure</em> property which is exhibited in many real-world networks. We propose the triad decoder, which considers and predicts the three edges involved in a local triad together. The triad decoder can be readily used in any graph-based auto-encoder. In particular, we incorporate this to the (variational) graph auto-encoder. Experiments on link prediction, node clustering and graph generation show that the use of triads leads to more accurate prediction, clustering and better preservation of the graph characteristics.</p>
ER -