Probabilistic graphical models, such as Markov random fields (MRF),
exploit dependencies among random variables to model a rich family of joint probability distributions.
Inference algorithms, such as belief propagation (BP),
can effectively compute the marginal posteriors for decision making.
Nonetheless,
inferences involve sophisticated probability calculations and are difficult for humans to interpret.
Among all existing explanation methods for MRFs,
no method is designed for fair attributions of an inference outcome to elements on the MRF where the inference takes place.
Shapley values provide rigorous attributions but so far have not been studied on MRFs.
We thus define Shapley values for MRFs to capture both probabilistic and topological contributions of the variables on MRFs.
We theoretically characterize the new definition regarding independence, equal contribution, additivity, and submodularity.
As brute-force computation of the Shapley values is challenging,
we propose GraphShapley, an approximation algorithm that exploits
the decomposability of Shapley values,
the structure of MRFs,
and the iterative nature of BP inference
to speed up the computation.
In practice,
we propose meta-explanations to explain the Shapley values and make them more accessible and trustworthy to human users.
On four synthetic and nine real-world MRFs,
we demonstrate that GraphShapley generates sensible and practical explanations.
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Generalization of graph network inferences in higher-order graphical models
Probabilistic graphical models provide a powerful tool to describe complex statistical structure, with many real-world applications in science and engineering from controlling robotic arms to understanding neuronal computations. A major challenge for these graphical models is that inferences such as marginalization are intractable for general graphs. These inferences are often approximated by a distributed message-passing algorithm such as Belief Propagation, which does not always perform well on graphs with cycles, nor can it always be easily specified for complex continuous probability distributions. Such difficulties arise frequently in expressive graphical models that include intractable higher-order interactions. In this paper we define the Recurrent Factor Graph Neural Network (RF-GNN) to achieve fast approximate inference on graphical models that involve many-variable interactions. Experimental results on several families of graphical models demonstrate the out-of-distribution generalization capability of our method to different sized graphs, and indicate the domain in which our method outperforms Belief Propagation (BP). Moreover, we test the RF-GNN on a real-world Low-Density Parity-Check dataset as a benchmark along with other baseline models including BP variants and other GNN methods. Overall we find that RF-GNNs outperform other methods under high noise levels.
more » « less- NSF-PAR ID:
- 10473838
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of Applied and Computational Topology
- ISSN:
- 2367-1726
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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