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Title: Learning Deep Generative Models of Graphs

Paper content:
Graphs are fundamental data structures required to model many important real-world data, from knowledge graphs, physical and social interactions to molecules and proteins.
In this paper, we study the problem of learning generative models of graphs from a dataset of graphs of interest.
After learning, these models can be used to generate samples with similar properties as the ones in the dataset.
Such models can be useful in a lot of applications, e.g. drug discovery and knowledge graph construction.
The task of learning generative models of graphs, however, has its unique challenges.
In particular, how to handle symmetries in graphs and ordering of its elements during the generation process are important issues.
We propose a generic graph neural net based model that is capable of generating any arbitrary graph.
We study its performance on a few graph generation tasks compared to baselines that exploit domain knowledge.
We discuss potential issues and open problems for such generative models going forward.
Graphs are natural representations of information in many problem domains.
For example, relations between entities in knowledge graphs and social networks are well captured by graphs, and they are also good for modeling the physical world, e.g. molecular structure and the interactions between objects in physical systems.
Thus, the ability to capture the distribution of a particular family of graphs has many applications.
For instance, sampling from the graph model can lead to the discovery of new configurations that share same global properties as is, for example, required in drug discovery BID10 .
Obtaining graph-structured semantic representations for natural language sentences BID15 requires the ability to model (conditional) distributions on graphs.
Distributions on graphs can also provide priors for Bayesian structure learning of graphical models BID23 .Probabilistic
models of graphs have been studied for a long time, from at least two perspectives. On one hand,
there are random graph models that robustly assign probabilities to large classes of graphs BID8 BID1 . These make strong
independence assumptions and are designed to capture only certain graph properties, like degree distribution and diameter. While these are effective
models of the distributions of graphs found in some domains, such as social networks, they are poor models of more richly structured graphs where small structural differences can be functionally significant, such as those encountered in chemistry or when representing the meaning of natural language sentences. As an alternative, a more
expressive class of models makes use of graph grammars, which generalize devices from formal language theory so as to produce non-sequential structures BID27 . Graph grammars are systems
of rewrite rules that incrementally derive an output graph via a sequence of transformations of intermediate graphs.While symbolic graph grammars can be made stochastic or otherwise weighted using standard techniques BID5 , from a learnability standpoint, two problems remain. First, inducing grammars from
a set of unannotated graphs is nontrivial since formalism-appropriate derivation steps must be inferred and transformed into rules BID17 Aguiñaga et al., 2016, for example) . Second, as with linear output
grammars, graph grammars make a hard distinction between what is in the language and what is excluded, making such models problematic for applications where it is inappropriate to assign 0 probability to certain graphs.In this work we develop an expressive model which makes no assumptions on the graphs and can therefore assign probabilities to any arbitrary graph.1 Our model generates graphs in
a manner similar to graph grammars, where during the course of a derivation new structure (specifically, a new node or a new edge) is added to the existing graph, and where the probability of that addition event depends on the history of the graph derivation. To represent the graph during
each step of the derivation, we use a representation based on graph-structured neural networks (graph nets). Recently there has been a surge
of interest in graph nets for learning graph representations and solving graph prediction problems BID11 BID6 BID2 BID14 BID9 . These models are structured according
to the graph being utilized, and are parameterized independent of graph sizes therefore invariant to isomorphism, providing a good match for our purposes. We evaluate our model by fitting graphs
in three problem domains: (1) generating random graphs with certain common topological properties (e.g., cyclicity); (2) generating molecule graphs; and (3) conditional generation of parse trees. Our proposed model performs better than
random graph models and LSTM baselines on (1) and FORMULA0 and is close to a LSTM sequence to sequence with attention model on (3). We also analyze the challenges our model
is facing, e.g. the difficulty of learning and optimization, and discuss possible ways to make it better.
The graph model in the proposed form is a powerful model capable of generating arbitrary graphs.
However, as we have seen in the experiments and the analysis, there are still a number of challenges facing these models.
Here we discuss a few of these challenges and possible solutions going forward.Ordering Ordering of nodes and edges is critical for both learning and evaluation.
In the experiments we always used predefined distribution over orderings.
However, it may be possible to learn an ordering of nodes and edges by treating the ordering π as a latent variable, this is an interesting direction to explore in the future.Long Sequences The generation process used by the graph model is typically a long sequence of decisions.
If other forms of sequentializing the graph is available, e.g. SMILES strings or flattened parse trees, then such sequences are typically 2-3x shorter.
This is a significant disadvantage for the graph model, it not only makes it harder to get the likelihood right, but also makes training more difficult.
To alleviate this problem we can tweak the graph model to be more tied to the problem domain, and reduce multiple decision steps and loops to single steps.Scalability Scalability is a challenge to the graph generative model we proposed in this paper.
Large graphs typically lead to very long graph generating sequences.
On the other side, the graph nets use a fixed T propagation steps to propagate information on the graph.
However, large graphs require large T s to have sufficient information flow, this would also limit the scalability of these models.
To solve this problem, we may use models that sequentially sweep over edges, like BID25 , or come up with ways to do coarse-to-fine generation.
In this paper, we proposed a powerful deep generative model capable of generating arbitrary graphs through a sequential process.
We studied its properties on a few graph generation problems.
This model has shown great promise and has unique advantages over standard LSTM models.
We hope that our results can spur further research in this direction to obtain better generative models of graphs.
DISPLAYFORM0 DISPLAYFORM1 Incorporate node v t 6: DISPLAYFORM2 Probability of adding an edge to v t 8: DISPLAYFORM3 Sample whether to add an edge to v t

assistant: text: We study the graph generation problem and propose a powerful deep generative model capable of generating arbitrary graphs.