Controlled Transformation of Text-Attributed Graphs (2024)

Abstract: Graph generation is the process of generating novel graphs with similar attributes to real world graphs. The explicit and precise control of granular structural attributes, such as node centrality and graph density, is crucial for effective graph generation. This paper introduces a controllable multi-objective translation model for text-attributed graphs, titled Controlled Graph Translator (CGT). It is designed to effectively and efficiently translate a given source graph to a target graph, while satisfying multiple desired graph attributes at granular level. Designed with an encoder-decoder architecture, CGT develops fusion and graph attribute predictor neural networks for controlled graph translation. We validate the effectiveness of CGT through extensive experiments on different genres of datasets. In addition, we illustrate the application of CGT in data augmentation and taxonomy creation, particularly in low resource settings.

## introduction graph generation is the process of generating new graphs that satisfy the properties of existing realworld graphs @xcite @xcite . graph generation has extensive real world applications. these include generating new molecules that satisfy certain chemical properties for applications like drug discovery @xcite @xcite , creating periodic graphs with repetitive patterns for new synthetic materials @xcite , generating synthetic social network for studying network dynamics and understanding complex social interactions @xcite , generating program graphs for representing source codes using abstract syntax trees @xcite , and completing knowledge graphs @xcite @xcite . effective neural graph generation methods have been proposed. in particular, @xcite and @xcite proposed to turn input graphs into sequences and generate graphs sequentially using two recurrent neural networks, which iteratively determine if and how new nodes and edges should be added to the partially generated graph. several effective methods have been developed specifically for text graph generation @xcite @xcite @xcite @xcite @xcite . for example, @xcite proposed a variational autoencoder that generates graphs through disentanglement of node and edge features. the model implements three encoders to model the distribution of node, edge and graph features; and two decoders to jointly generate these features based on the resulting latent representations. recently, @xcite proposed a low-resource approach to generate text from input graph by introducing several graph masking strategies to integrate both local and global information into the pre-trained language models. however, despite their effectiveness, existing works often lack explicit control over desired structural properties, or focus on a limited set of graph properties in the output space. in this paper, we propose the controlled graph translator (cgt), which transforms a given source graph into a new target graph that satisfies multiple, precisely-defined structural attributes. cgt implements an effective graph encoder-decoder model, augmented with a fusion and pre-trained structural attribute predictor. this predictor efficiently computes the attributes of graphs generated in real-time to explicitly guide the generation process. specifically, the model enables translating a source graph into a target graph that not only satisfies the desired attributes but does so with granular control over the changes in its structural attributes. the contribution of this paper are (a): a novel approach capable of controlling the transformation of text-attributed graphs and satisfying predefined structural attributes, and (b): a graph attribute predictor that explicitly guides the generation process by computing graph attributes in real-time. we evaluate the efficacy of cgt across several domains including ontologies, citation networks, and molecular biology. it consistently outperforms existing baselines on these datasets. in addition, we show that cgt is capable of refining existing ontologies, such as wordnet @xcite , and generating new additional data points in low resource domains such as molecular biology, thereby improving the performance of downstream models developed for specific tasks. in addition, cgt is scalable to larger graphs. ## control attributes we provide a list of structural attributes that are crucial for controlled graph translation. these attributes, which have not been previously utilized in graph generation, add granularity, complexity and depth to the generation process, and impose explicit and precise control over the generation process: these attributes reflect the complexity involved in transforming graphs through structural modifications. they enable precise control over the structures of generated graphs, making them suitable for specific applications. for example, attributes like number of nodes and edges, and local bridges are crucial in ontologies like wordnet, where the richness of linguistic connections, e.g. hypernyms and hyponyms, reflects the complexity and depth of semantic relationships. edge connectivity and subgraph density are key in citation networks to model the influential works and inter-connectivity of research areas. in molecular datasets, transitivity can indicate the overall connectivity and potential stability of a compound, while clustering coefficient can help understand the compactness of molecular structures. in social networks, graph density is a crucial metric for community detection and analyzing the likelihood of forming tight-knit communities within graphs. ## controlled graph translator problem statement given a source graph g s , a source attribute vector c s , and a target attribute vector c t , we aim to translate g s to a target graph g t that satisfies specified desired attributes c t .