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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 11: AAAI-21 Technical Tracks 11

Adversarial Permutation Guided Node Representations for Link Prediction

February 1, 2023

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Authors

Indradyumna Roy

IIT Bombay


Abir De

IIT Bombay


Soumen Chakrabarti

IIT Bombay


DOI:

10.1609/aaai.v35i11.17138


Abstract:

After observing a snapshot of a social network, a link prediction (LP) algorithm identifies node pairs between which new edges will likely materialize in future. Most LP algorithms estimate a score for currently non-neighboring node pairs, and rank them by this score. Recent LP systems compute this score by comparing dense, low dimensional vector representations of nodes. Graph neural networks (GNNs), in particular graph convolutional networks (GCNs), are popular examples. For two nodes to be meaningfully compared, their embeddings should be indifferent to reordering of their neighbors. GNNs typically use simple, symmetric set aggregators to ensure this property, but this design decision has been shown to produce representations with limited expressive power. Sequence encoders are more expressive, but are permutation sensitive by design. Recent efforts to overcome this dilemma turn out to be unsatisfactory for LP tasks. In response, we propose PermGNN, which aggregates neighbor features using a recurrent, order-sensitive aggregator and directly minimizes an LP loss while it is `attacked' by adversarial generator of neighbor permutations. PermGNN has superior expressive power compared to earlier GNNs. Next, we devise an optimization framework to map PermGNN's node embeddings to a suitable locality-sensitive hash, which speeds up reporting the top-K most likely edges for the LP task. Our experiments on diverse datasets show that PermGNN outperforms several state-of-the-art link predictors by a significant margin, and can predict the most likely edges fast.

Topics: AAAI

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HOW TO CITE:

Indradyumna Roy||Abir De||Soumen Chakrabarti Adversarial Permutation Guided Node Representations for Link Prediction Proceedings of the AAAI Conference on Artificial Intelligence (2021) 9445-9453.

Indradyumna Roy||Abir De||Soumen Chakrabarti Adversarial Permutation Guided Node Representations for Link Prediction AAAI 2021, 9445-9453.

Indradyumna Roy||Abir De||Soumen Chakrabarti (2021). Adversarial Permutation Guided Node Representations for Link Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 9445-9453.

Indradyumna Roy||Abir De||Soumen Chakrabarti. Adversarial Permutation Guided Node Representations for Link Prediction. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.9445-9453.

Indradyumna Roy||Abir De||Soumen Chakrabarti. 2021. Adversarial Permutation Guided Node Representations for Link Prediction. "Proceedings of the AAAI Conference on Artificial Intelligence". 9445-9453.

Indradyumna Roy||Abir De||Soumen Chakrabarti. (2021) "Adversarial Permutation Guided Node Representations for Link Prediction", Proceedings of the AAAI Conference on Artificial Intelligence, p.9445-9453

Indradyumna Roy||Abir De||Soumen Chakrabarti, "Adversarial Permutation Guided Node Representations for Link Prediction", AAAI, p.9445-9453, 2021.

Indradyumna Roy||Abir De||Soumen Chakrabarti. "Adversarial Permutation Guided Node Representations for Link Prediction". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.9445-9453.

Indradyumna Roy||Abir De||Soumen Chakrabarti. "Adversarial Permutation Guided Node Representations for Link Prediction". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 9445-9453.

Indradyumna Roy||Abir De||Soumen Chakrabarti. Adversarial Permutation Guided Node Representations for Link Prediction. AAAI[Internet]. 2021[cited 2023]; 9445-9453.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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