A new algorithm for error-tolerant subgraph isomorphism detection

Messmer, Bruno T.; Bunke, Horst

doi:10.1109/34.682179

Cited by 288 publications

(195 citation statements)

References 24 publications

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“…A number of techniques have been proposed to quantify graph similarity, including graph isomorphism, edit distance [24], common subgraphs and supergraphs, and statistical measurements of graph structure. We chose to use a statistical approach for our study because most of the alternative methods were computationally intractable for our large graph datasets.…”

Section: Graph Similaritymentioning

confidence: 99%

Measurement-calibrated graph models for social network experiments

Sala

Cao

Wilson

et al. 2010

Proceedings of the 19th International Conference on World Wide Web

146

127

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Access to realistic, complex graph datasets is critical to research on social networking systems and applications. Simulations on graph data provide critical evaluation of new systems and applications ranging from community detection to spam filtering and social web search. Due to the high time and resource costs of gathering real graph datasets through direct measurements, researchers are anonymizing and sharing a small number of valuable datasets with the community. However, performing experiments using shared real datasets faces three key disadvantages: concerns that graphs can be de-anonymized to reveal private information, increasing costs of distributing large datasets, and that a small number of available social graphs limits the statistical confidence in the results.The use of measurement-calibrated graph models is an attractive alternative to sharing datasets. Researchers can "fit" a graph model to a real social graph, extract a set of model parameters, and use them to generate multiple synthetic graphs statistically similar to the original graph. While numerous graph models have been proposed, it is unclear if they can produce synthetic graphs that accurately match the properties of the original graphs. In this paper, we explore the feasibility of measurement-calibrated synthetic graphs using six popular graph models and a variety of real social graphs gathered from the Facebook social network ranging from 30,000 to 3 million edges. We find that two models consistently produce synthetic graphs with common graph metric values similar to those of the original graphs. However, only one produces high fidelity results in our application-level benchmarks. While this shows that graph models can produce realistic synthetic graphs, it also highlights the fact that current graph metrics remain incomplete, and some applications expose graph properties that do not map to existing metrics.

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Section: Graph Similaritymentioning

confidence: 99%

Measurement-calibrated graph models for social network experiments

Sala

Cao

Wilson

et al. 2010

Proceedings of the 19th International Conference on World Wide Web

146

127

View full text Add to dashboard Cite

show abstract

“…The problem of many-to-many graph matching has been studied most often in the context of edit-distance (see, e.g., [14,12,15,18]). In such a setting, one seeks a minimal set of re-labelings, additions, deletions, merges, and splits of nodes and edges that transform one graph into another.…”

Section: Related Workmentioning

confidence: 99%

Many-to-Many Feature Matching Using Spherical Coding of Directed Graphs

Demirci

Shokoufandeh

Dickinson

et al. 2004

Lecture Notes in Computer Science

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Abstract. In recent work, we presented a framework for many-to-many matching of multi-scale feature hierarchies, in which features and their relations were captured in a vertex-labeled, edge-weighted directed graph. The algorithm was based on a metric-tree representation of labeled graphs and their metric embedding into normed vector spaces, using the embedding algorithm of Matousek [13]. However, the method was limited by the fact that two graphs to be matched were typically embedded into vector spaces with different dimensionality. Before the embeddings could be matched, a dimensionality reduction technique (PCA) was required, which was both costly and prone to error. In this paper, we introduce a more efficient embedding procedure based on a spherical coding of directed graphs. The advantage of this novel embedding technique is that it prescribes a single vector space into which both graphs are embedded. This reduces the problem of directed graph matching to the problem of geometric point matching, for which efficient many-to-many matching algorithms exist, such as the Earth Mover's Distance. We apply the approach to the problem of multi-scale, view-based object recognition, in which an image is decomposed into a set of blobs and ridges with automatic scale selection.

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“…For a detailed comparison and description of the correspondences between such techniques we refer to [32]. In a similar sense, the graph edit operations that are widely used in several applications, such as Pattern Recognition [33][34][35], are not appropriate for the problem at hand. These operations typically include node/edge insertion, node/edge deletion, and node/edge relabeling.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Driven Conceptual Design of ETL Processes Using Graph Transformations

Skoutas

Simitsis

Sellis³

2009

Lecture Notes in Computer Science

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Abstract. One of the main tasks during the early steps of a data warehouse project is the identification of the appropriate transformations and the specification of inter-schema mappings from the source to the target data stores. This is a challenging task, requiring firstly the semantic and secondly the structural reconciliation of the information provided by the available sources. This task is a part of the Extract-Transform-Load (ETL) process, which is responsible for the population of the data warehouse. In this paper, we propose a customizable and extensible ontologydriven approach for the conceptual design of ETL processes. A graphbased representation is used as a conceptual model for the source and target data stores. We then present a method for devising flows of ETL operations by means of graph transformations. In particular, the operations comprising the ETL process are derived through graph transformation rules, the choice and applicability of which are determined by the semantics of the data with respect to an attached domain ontology. Finally, we present our experimental findings that demonstrate the applicability of our approach.

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A new algorithm for error-tolerant subgraph isomorphism detection

Cited by 288 publications

References 24 publications

Measurement-calibrated graph models for social network experiments

Measurement-calibrated graph models for social network experiments

Many-to-Many Feature Matching Using Spherical Coding of Directed Graphs

Ontology-Driven Conceptual Design of ETL Processes Using Graph Transformations

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