Using Graph Edit Distance for Noisy Subgraph Matching of Semantic Property Graphs

Ebsch, Christopher L.; Cottam, Joseph A.; Heller, Natalie C.; Deshmukh, Rahul; Chin, George

doi:10.1109/bigdata50022.2020.9378349

Cited by 2 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Graphs are used to model data in a wide variety of domains. Examples include chemical compounds [44], protein-protein interaction networks (PPI) [1], knowledge graphs [17], and social networks [26]. A distance function on any dataset, including graphs, is a fundamental operator.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…SED, on the other hand, is useful when the database has large graphs and the query is a comparatively smaller graph. As examples, subgraph queries are used on knowledge graphs for analogy reasoning [17]. In PPI and chemical compounds, SED is of central importance to identify functional motifs and binding pockets [44,22,16].…”

Section: Introduction and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Neural Framework for Learning Subgraph and Graph Similarity Measures

Sharma¹,

Grover²,

Medya³

et al. 2021

Preprint

View full text Add to dashboard Cite

Subgraph similarity search is a fundamental operator in graph analysis. In this framework, given a query graph and a graph database, the goal is to identify subgraphs of the database graphs that are structurally similar to the query. Subgraph edit distance (SED) is one of the most expressive measures for subgraph similarity. In this work, we study the problem of learning SED from a training set of graph pairs and their SED values. Towards that end, we design a novel siamese graph neural network called NEUROSED, which learns an embedding space with a rich structure reminiscent of SED. With the help of a specially crafted inductive bias, NEUROSED not only enables high accuracy but also ensures that the predicted SED, like true SED, satisfies triangle inequality. The design is generic enough to also model graph edit distance (GED), while ensuring that the predicted GED space is metric, like the true GED space. Extensive experiments on real graph datasets, for both SED and GED, establish that NEUROSED achieves ≈ 2 times lower RMSE than the state of the art and is ≈ 18 times faster than the fastest baseline. Further, owing to its pair-independent embeddings and theoretical properties, NEUROSED allows ≈ 3 orders of magnitude faster retrieval of graphs and subgraphs.Preprint. Under review.

show abstract

Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

A Neural Framework for Learning Subgraph and Graph Similarity Measures

Sharma¹,

Grover²,

Medya³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Multiplex graph matching matched filters

et al. 2022

View full text Add to dashboard Cite

We consider the problem of detecting a noisy induced multiplex template network in a larger multiplex background network. Our approach, which extends the graph matching matched filter framework of Sussman et al. (IEEE Trans Pattern Anal Mach Intell 42(11):2887–2900, 2019) to the multiplex setting, utilizes a multiplex analogue of the classical graph matching problem to use the template as a matched filter for efficiently searching the background for candidate template matches. The effectiveness of our approach is demonstrated both theoretically and empirically, with particular attention paid to the potential benefits of considering multiple channels in the multiplex framework.

show abstract

Using Graph Edit Distance for Noisy Subgraph Matching of Semantic Property Graphs

Cited by 2 publications

References 9 publications

A Neural Framework for Learning Subgraph and Graph Similarity Measures

A Neural Framework for Learning Subgraph and Graph Similarity Measures

Multiplex graph matching matched filters

Contact Info

Product

Resources

About