21 research outputs found
On the Stability of Graph Convolutional Neural Networks under Edge Rewiring
Graph neural networks are experiencing a surge of popularity within the
machine learning community due to their ability to adapt to non-Euclidean
domains and instil inductive biases. Despite this, their stability, i.e., their
robustness to small perturbations in the input, is not yet well understood.
Although there exists some results showing the stability of graph neural
networks, most take the form of an upper bound on the magnitude of change due
to a perturbation in the graph topology. However, the change in the graph
topology captured in existing bounds tend not to be expressed in terms of
structural properties, limiting our understanding of the model robustness
properties. In this work, we develop an interpretable upper bound elucidating
that graph neural networks are stable to rewiring between high degree nodes.
This bound and further research in bounds of similar type provide further
understanding of the stability properties of graph neural networks.Comment: To appear at the 46th International Conference on Acoustics, Speech
and Signal Processing (ICASSP 2021
On the stability of graph convolutional neural networks under edge rewiring
Graph neural networks are experiencing a surge of popularity within the machine learning community due to their ability to adapt to non-Euclidean domains and instil inductive biases. Despite this, their stability, i.e., their robustness to small perturbations in the input, is not yet well understood. Although there exists some results showing the stability of graph neural networks, most take the form of an upper bound on the magnitude of change due to a perturbation in the graph topology. However, the change in the graph topology captured in existing bounds tend not to be expressed in terms of structural properties, limiting our understanding of the model robustness properties. In this work, we develop an interpretable upper bound elucidating that graph neural networks are stable to rewiring between high degree nodes. This bound and further research in bounds of similar type provide further understanding of the stability properties of graph neural networks
Single-Node Attack for Fooling Graph Neural Networks
Graph neural networks (GNNs) have shown broad applicability in a variety of
domains. Some of these domains, such as social networks and product
recommendations, are fertile ground for malicious users and behavior. In this
paper, we show that GNNs are vulnerable to the extremely limited scenario of a
single-node adversarial example, where the node cannot be picked by the
attacker. That is, an attacker can force the GNN to classify any target node to
a chosen label by only slightly perturbing another single arbitrary node in the
graph, even when not being able to pick that specific attacker node. When the
adversary is allowed to pick a specific attacker node, the attack is even more
effective. We show that this attack is effective across various GNN types, such
as GraphSAGE, GCN, GAT, and GIN, across a variety of real-world datasets, and
as a targeted and a non-targeted attack. Our code is available at
https://github.com/benfinkelshtein/SINGLE
Robust Graph Representation Learning via Predictive Coding
Predictive coding is a message-passing framework initially developed to model
information processing in the brain, and now also topic of research in machine
learning due to some interesting properties. One of such properties is the
natural ability of generative models to learn robust representations thanks to
their peculiar credit assignment rule, that allows neural activities to
converge to a solution before updating the synaptic weights. Graph neural
networks are also message-passing models, which have recently shown outstanding
results in diverse types of tasks in machine learning, providing
interdisciplinary state-of-the-art performance on structured data. However,
they are vulnerable to imperceptible adversarial attacks, and unfit for
out-of-distribution generalization. In this work, we address this by building
models that have the same structure of popular graph neural network
architectures, but rely on the message-passing rule of predictive coding.
Through an extensive set of experiments, we show that the proposed models are
(i) comparable to standard ones in terms of performance in both inductive and
transductive tasks, (ii) better calibrated, and (iii) robust against multiple
kinds of adversarial attacks.Comment: 27 Pages, 31 Figure
Robust Counterfactual Explanations on Graph Neural Networks
Massive deployment of Graph Neural Networks (GNNs) in high-stake applications
generates a strong demand for explanations that are robust to noise and align
well with human intuition. Most existing methods generate explanations by
identifying a subgraph of an input graph that has a strong correlation with the
prediction. These explanations are not robust to noise because independently
optimizing the correlation for a single input can easily overfit noise.
Moreover, they do not align well with human intuition because removing an
identified subgraph from an input graph does not necessarily change the
prediction result. In this paper, we propose a novel method to generate robust
counterfactual explanations on GNNs by explicitly modelling the common decision
logic of GNNs on similar input graphs. Our explanations are naturally robust to
noise because they are produced from the common decision boundaries of a GNN
that govern the predictions of many similar input graphs. The explanations also
align well with human intuition because removing the set of edges identified by
an explanation from the input graph changes the prediction significantly.
Exhaustive experiments on many public datasets demonstrate the superior
performance of our method