KDDCup 2020 - Track 2 - Adversarial Attacks and Defense on Academic Graph
https://www.biendata.xyz/competition/kddcup_2020_formal/
The challenge: Creating an attacker (i.e., a modified input consisting of graph structure (adjacency matrix) and node features (embedding vectors)) and a defender (i.e., a robust Graph Neural Network model). The organizers will match all attackers and defenders from all teams and rank the final leaderboard.
Final result: 7th
REFERENCES
Some Relevant Papers/Articles
| Paper (year) | Category | Link |
|---|---|---|
| Semi-supervised Classification with Graph Convolutional Networks (GCN) (2017) | graph_neural_network | https://arxiv.org/pdf/1609.02907.pdf |
| Adversarial Examples on Graph Data: Deep Insights into Attack and Defense (2019) | attack,defense | https://arxiv.org/pdf/1903.01610.pdf |
| Type of Attacks in ML | attack | https://towardsdatascience.com/how-to-attack-machine-learning-evasion-poisoning-inference-trojans-backdoors-a7cb5832595c |
| Adversarial Attacks on Neural Networks for Graph Data (Nettack) (2018) | attack | https://arxiv.org/pdf/1805.07984.pdf |
| Attacking Graph-based Classification via Manipulating theGraph Structure (2019) | attack | https://arxiv.org/pdf/1903.00553.pdf |
| Backdoor Attacks to Graph Neural Networks (2020) | attack,defense | https://arxiv.org/pdf/2006.11165.pdf |
| Inductive Representation Learning on Large Graphs (GraphSAGE) (2017) | graph_neural_network | https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf |
| Adversarial Attack on Graph Structured Data (2018) | attack | https://arxiv.org/pdf/1806.02371.pdf |
| Practical Attacks Against Graph-based Clustering (2017) | attack | https://arxiv.org/pdf/1708.09056.pdf |
| Adversarial Attack and Defense on Graph Data: A Survey (2020) | attack,defense | https://arxiv.org/pdf/1812.10528.pdf |
| HOW POWERFUL ARE GRAPH NEURAL NETWORKS? (2019) | graph_neural_network | https://arxiv.org/pdf/1810.00826.pdf |
| A Comprehensive Survey on Graph Neural Networks (2019) | graph_neural_network | https://arxiv.org/pdf/1901.00596.pdf |
Other Resources
- https://github.com/yenchenlin/awesome-adversarial-machine-learning
- https://stellargraph.readthedocs.io/en/stable/demos/index.html#find-algorithms-for-a-task
- https://adversarial-ml-tutorial.org/
- CS224W: Machine Learning with Graphs (Stanford - Fall 2019) [Course] [Videos]
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