In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: In recent years there has been a rapid increase in classification methods on graph structured data. Both in graph kernels and graph neural networks, one of the implicit assumptions of successful state-of-the-art models was that incorporating graph isomorphism features into the architecture leads to better empirical performance. However, as we discover in this work, commonly used data sets for graph classification have repeating instances which cause the problem of isomorphism bias, i.e. artificially increasing the accuracy of the models by memorizing target information from the training set. This prevents fair competition of the algorithms and raises a question of the validity of the obtained results. We analyze 54 data sets, previously extensively used for graph-related tasks, on the existence of isomorphism bias, give a set of recommendations to machine learning practitioners to properly set up their models, and open source new data sets for the future experiments.
[A]: Many graph classification data sets have duplicates, thus raising questions about generalization abilities and fair comparison of the models. 


[Q]: We propose a framework to model the distribution of sequential data coming from
 a set of entities connected in a graph with a known topology. The method is
 based on a mixture of shared hidden Markov models (HMMs), which are trained
 in order to exploit the knowledge of the graph structure and in such a way that the
 obtained mixtures tend to be sparse. Experiments in different application domains
 demonstrate the effectiveness and versatility of the method.
[A]: A method to model the generative distribution of sequences coming from graph connected entities.


[Q]: The standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest. We introduce an unbiased estimator of the log marginal likelihood and its gradients for latent variable models based on randomized truncation of infinite series. If parameterized by an encoder-decoder architecture, the parameters of the encoder can be optimized to minimize its variance of this estimator. We show that models trained using our estimator give better test-set likelihoods than a standard importance-sampling based approach for the same average computational cost. This estimator also allows use of latent variable models for tasks where unbiased estimators, rather than marginal likelihood lower bounds, are preferred, such as minimizing reverse KL divergences and estimating score functions.
[A]:
We create an unbiased estimator for the log probability of latent variable models, extending such models to a larger scope of applications.